\n\n \n\n \n\n \n \n\n \n\n \n \n\n \n\n \n

\n generated by\n \n $\"bibbase.org\"\n$ \n \n

\n \n\n

\n\n

\n \n Group by\n \n \n
- \n Year\n
- \n Author\n
- \n Type\n
- \n Keyword\n
- \n Downloads\n
\n
\n \n \n \n \n
- \n \n Expand/Collapse All\n \n
- \n \n Download BibTeX\n \n
- \n \n RSS Feed\n \n
\n

\n\n\n

\n\n Excellent! Next you can\n create a new website with this list, or\n embed it in an existing web page by copying & pasting\n any of the following snippets.\n\n

\n JavaScript\n (easiest)\n

\n            <script src=\"https://bibbase.org/show?bib=https%3A%2F%2Fbibbase.org%2Fnetwork%2Ffiles%2FATqGzmCTsrzAKzFAt&noBootstrap=1&jsonp=1&jsonp=1\"></script>\n

\n\n PHP\n

\n            <?php\n            $contents = file_get_contents(\"https://bibbase.org/show?bib=https%3A%2F%2Fbibbase.org%2Fnetwork%2Ffiles%2FATqGzmCTsrzAKzFAt&noBootstrap=1&jsonp=1\");\n            print_r($contents);\n            ?>\n

\n\n iFrame\n (not recommended)\n

\n            <iframe src=\"https://bibbase.org/show?bib=https%3A%2F%2Fbibbase.org%2Fnetwork%2Ffiles%2FATqGzmCTsrzAKzFAt&noBootstrap=1&jsonp=1\"></iframe>\n

\n\n

\n For more details see the documention.\n

\n\n

\n\n This is a preview! To use this list on your own web site\n or create a new web site from it,\n create a free account. The file will be added\n and you will be able to edit it in the File Manager.\n We will show you instructions once you've created your account.\n

\n\n

To the site owner:

\n\n

Action required! Mendeley is changing its\n API. In order to keep using Mendeley with BibBase past April\n 14th, you need to:\n

renew the authorization for BibBase on Mendeley, and
update the BibBase URL\n in your page the same way you did when you initially set up\n this page.\n

\n\n

\n \n \n Fix it now\n

\n\n

\n\n\n

\n \n \n

\n \n 2023\n \n \n (8)\n \n \n

\n \n \n

\n \n\n \n \n \n \n \n \n Assessing efficiency of fine-mapping obesity-associated variants through leveraging ancestry architecture and functional annotation using PAGE and UKBB cohorts.\n \n \n \n \n\n\n \n Anwar, M. Y.; Graff, M.; Highland, H. M; Smit, R.; Wang, Z.; Buchanan, V. L; Young, K. L; Kenny, E. E; Fernandez-Rhodes, L.; Liu, S.; Assimes, T.; Garcia, D. O; Daeeun, K.; Gignoux, C. R; Justice, A. E; Haiman, C. A; Buyske, S.; Peters, U.; Loos, R. J F; Kooperberg, C.; and North, K. E\n\n\n \n\n\n\n Human Genetics. September 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Assessing$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@ARTICLE{Anwar2023-zs,\n  title    = "Assessing efficiency of fine-mapping obesity-associated variants\n              through leveraging ancestry architecture and functional\n              annotation using {PAGE} and {UKBB} cohorts",\n  author   = "Anwar, Mohammad Yaser and Graff, Mariaelisa and Highland, Heather\n              M and Smit, Roelof and Wang, Zhe and Buchanan, Victoria L and\n              Young, Kristin L and Kenny, Eimear E and Fernandez-Rhodes,\n              Lindsay and Liu, Simin and Assimes, Themistocles and Garcia,\n              David O and Daeeun, Kim and Gignoux, Christopher R and Justice,\n              Anne E and Haiman, Christopher A and Buyske, Steve and Peters,\n              Ulrike and Loos, Ruth J F and Kooperberg, Charles and North, Kari\n              E",\n  abstract = "Inadequate representation of non-European ancestry populations in\n              genome-wide association studies (GWAS) has limited opportunities\n              to isolate functional variants. Fine-mapping in multi-ancestry\n              populations should improve the efficiency of prioritizing\n              variants for functional interrogation. To evaluate this\n              hypothesis, we leveraged ancestry architecture to perform\n              comparative GWAS and fine-mapping of obesity-related phenotypes\n              in European ancestry populations from the UK Biobank (UKBB) and\n              multi-ancestry samples from the Population Architecture for\n              Genetic Epidemiology (PAGE) consortium with comparable sample\n              sizes. In the investigated regions with genome-wide significant\n              associations for obesity-related traits, fine-mapping in our\n              ancestrally diverse sample led to 95\\% and 99\\% credible sets\n              (CS) with fewer variants than in the European ancestry sample.\n              Lead fine-mapped variants in PAGE regions had higher average\n              coding scores, and higher average posterior probabilities for\n              causality compared to UKBB. Importantly, 99\\% CS in PAGE loci\n              contained strong expression quantitative trait loci (eQTLs) in\n              adipose tissues or harbored more variants in tighter linkage\n              disequilibrium (LD) with eQTLs. Leveraging ancestrally diverse\n              populations with heterogeneous ancestry architectures, coupled\n              with functional annotation, increased fine-mapping efficiency and\n              performance, and reduced the set of candidate variants for\n              consideration for future functional studies. Significant overlap\n              in genetic causal variants across populations suggests\n              generalizability of genetic mechanisms underpinning\n              obesity-related traits across populations.",\n  journal  = "Human Genetics",\n  month    =  sep,\n  year     =  2023,\n  language = "en",\n\tpmid = {37658231},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/37658231/},\n  bdsk-url-1 = {https://doi.org/10.1007/s00439-023-02593-7},\n    doi = {10.1007/s00439-023-02593-7}\n}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Ancestral diversity in lipoprotein(a) studies helps address evidence gaps.\n \n \n \n \n\n\n \n Lee, M. P; Dimos, S. F; Raffield, L. M; Wang, Z.; Ballou, A. F; Downie, C. G; Arehart, C. H; Correa, A.; de Vries, P. S; Du, Z.; Gignoux, C. R; Gordon-Larsen, P.; Guo, X.; Haessler, J.; Howard, A. G.; Hu, Y.; Kassahun, H.; Kent, S. T; Lopez, J A. G; Monda, K. L; North, K. E; Peters, U.; Preuss, M. H; Rich, S. S; Rhodes, S. L; Yao, J.; Yarosh, R.; Tsai, M. Y; Rotter, J. I; Kooperberg, C. L; Loos, R. J F; Ballantyne, C.; Avery, C. L; and Graff, M.\n\n\n \n\n\n\n Open Heart, 10(2). August 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Ancestral$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@ARTICLE{Lee2023-cf,\n  title    = "Ancestral diversity in lipoprotein(a) studies helps address\n              evidence gaps",\n  author   = "Lee, Moa P and Dimos, Sofia F and Raffield, Laura M and Wang, Zhe\n              and Ballou, Anna F and Downie, Carolina G and Arehart,\n              Christopher H and Correa, Adolfo and de Vries, Paul S and Du,\n              Zhaohui and Gignoux, Christopher R and Gordon-Larsen, Penny and\n              Guo, Xiuqing and Haessler, Jeffrey and Howard, Annie Green and\n              Hu, Yao and Kassahun, Helina and Kent, Shia T and Lopez, J\n              Antonio G and Monda, Keri L and North, Kari E and Peters, Ulrike\n              and Preuss, Michael H and Rich, Stephen S and Rhodes, Shannon L\n              and Yao, Jie and Yarosh, Rina and Tsai, Michael Y and Rotter,\n              Jerome I and Kooperberg, Charles L and Loos, Ruth J F and\n              Ballantyne, Christie and Avery, Christy L and Graff, Mariaelisa",\n  abstract = "INTRODUCTION: The independent and causal cardiovascular disease\n              risk factor lipoprotein(a) (Lp(a)) is elevated in >1.5 billion\n              individuals worldwide, but studies have prioritised European\n              populations. METHODS: Here, we examined how ancestrally diverse\n              studies could clarify Lp(a)'s genetic architecture, inform\n              efforts examining application of Lp(a) polygenic risk scores\n              (PRS), enable causal inference and identify unexpected Lp(a)\n              phenotypic effects using data from African (n=25 208), East Asian\n              (n=2895), European (n=362 558), South Asian (n=8192) and\n              Hispanic/Latino (n=8946) populations. RESULTS: Fourteen\n              genome-wide significant loci with numerous population specific\n              signals of large effect were identified that enabled construction\n              of Lp(a) PRS of moderate (R2=15\\% in East Asians) to high\n              (R2=50\\% in Europeans) accuracy. For all populations, PRS showed\n              promise as a 'rule out' for elevated Lp(a) because certainty of\n              assignment to the low-risk threshold was high (88.0\\%-99.9\\%)\n              across PRS thresholds (80th-99th percentile). Causal effects of\n              increased Lp(a) with increased glycated haemoglobin were\n              estimated for Europeans (p value =1.4$\\times$10-6), although\n              inverse effects in Africans and East Asians suggested the\n              potential for heterogeneous causal effects. Finally,\n              Hispanic/Latinos were the only population in which known\n              associations with coronary atherosclerosis and ischaemic heart\n              disease were identified in external testing of Lp(a) PRS\n              phenotypic effects. CONCLUSIONS: Our results emphasise the merits\n              of prioritising ancestral diversity when addressing Lp(a)\n              evidence gaps.",\n  journal  = "Open Heart",\n  volume   =  10,\n  number   =  2,\n  month    =  aug,\n  year     =  2023,\n  keywords = "biomarkers; epidemiology; genetic association studies;\n              genome-wide association study",\n  language = "en",\n\tpmid = {37648373},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/37648373/},\n  bdsk-url-1 = {https://doi.org/10.1136/openhrt-2023-002382},\n  doi = {10.1136/openhrt-2023-002382}\n}\n\n% The entry below contains non-ASCII chars that could not be converted\n% to a LaTeX equivalent.\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Admixture mapping of peripheral artery disease in a Dominican population reveals a putative risk locus on 2q35.\n \n \n \n \n\n\n \n Cullina, S.; Wojcik, G. L; Shemirani, R.; Klarin, D.; Gorman, B. R; Sorokin, E. P; Gignoux, C. R; Belbin, G. M; Pyarajan, S.; Asgari, S.; Tsao, P. S; Damrauer, S. M; Abul-Husn, N. S; and Kenny, E. E\n\n\n \n\n\n\n Front. Genet., 14: 1181167. August 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Admixture$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@ARTICLE{Cullina2023-uj,\n  title    = "Admixture mapping of peripheral artery disease in a Dominican\n              population reveals a putative risk locus on 2q35",\n  author   = "Cullina, Sinead and Wojcik, Genevieve L and Shemirani, Ruhollah\n              and Klarin, Derek and Gorman, Bryan R and Sorokin, Elena P and\n              Gignoux, Christopher R and Belbin, Gillian M and Pyarajan, Saiju\n              and Asgari, Samira and Tsao, Philip S and Damrauer, Scott M and\n              Abul-Husn, Noura S and Kenny, Eimear E",\n  abstract = "Peripheral artery disease (PAD) is a form of atherosclerotic\n              cardiovascular disease, affecting ∼8 million Americans, and is\n              known to have racial and ethnic disparities. PAD has been\n              reported to have a significantly higher prevalence in African\n              Americans (AAs) compared to non-Hispanic European Americans\n              (EAs). Hispanic/Latinos (HLs) have been reported to have lower or\n              similar rates of PAD compared to EAs, despite having a\n              paradoxically high burden of PAD risk factors; however, recent\n              work suggests prevalence may differ between sub-groups. Here, we\n              examined a large cohort of diverse adults in the BioMe biobank in\n              New York City. We observed the prevalence of PAD at 1.7\\% in EAs\n              vs. 8.5\\% and 9.4\\% in AAs and HLs, respectively, and among HL\n              sub-groups, the prevalence was found at 11.4\\% and 11.5\\% in\n              Puerto Rican and Dominican populations, respectively. Follow-up\n              analysis that adjusted for common risk factors demonstrated that\n              Dominicans had the highest increased risk for PAD relative to EAs\n              [OR = 3.15 (95\\% CI 2.33-4.25), p < 6.44 $\\times$ 10-14]. To\n              investigate whether genetic factors may explain this increased\n              risk, we performed admixture mapping by testing the association\n              between local ancestry and PAD in Dominican BioMe participants (N\n              = 1,813) separately from European, African, and Native American\n              (NAT) continental ancestry tracts. The top association with PAD\n              was an NAT ancestry tract at chromosome 2q35 [OR = 1.96 (SE =\n              0.16), p < 2.75 $\\times$ 10-05) with 22.6\\% vs. 12.9\\% PAD\n              prevalence in heterozygous NAT tract carriers versus\n              non-carriers, respectively. Fine-mapping at this locus implicated\n              tag SNP rs78529201 located within a long intergenic non-coding\n              RNA (lincRNA) LINC00607, a gene expression regulator of key genes\n              related to thrombosis and extracellular remodeling of endothelial\n              cells, suggesting a putative link of the 2q35 locus to PAD\n              etiology. Efforts to reproduce the signal in other Hispanic\n              cohorts were unsuccessful. In summary, we showed how leveraging\n              health system data helped understand nuances of PAD risk across\n              HL sub-groups and admixture mapping approaches elucidated a\n              putative risk locus in a Dominican population.",\n  journal  = "Front. Genet.",\n  volume   =  14,\n  pages    = "1181167",\n  month    =  aug,\n  year     =  2023,\n  keywords = "Dominicans; admixture mapping; biobanks; genetic epidemiology;\n              peripheral artery disease",\n  language = "en",\n\tpmid = {37600667},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/37600667/},\n  bdsk-url-1 = {https://doi.org/10.3389/fgene.2023.1181167},\n    doi = {10.3389/fgene.2023.1181167}  \n}\n\n

\n\n\n

\n Peripheral artery disease (PAD) is a form of atherosclerotic cardiovascular disease, affecting ∼8 million Americans, and is known to have racial and ethnic disparities. PAD has been reported to have a significantly higher prevalence in African Americans (AAs) compared to non-Hispanic European Americans (EAs). Hispanic/Latinos (HLs) have been reported to have lower or similar rates of PAD compared to EAs, despite having a paradoxically high burden of PAD risk factors; however, recent work suggests prevalence may differ between sub-groups. Here, we examined a large cohort of diverse adults in the BioMe biobank in New York City. We observed the prevalence of PAD at 1.7% in EAs vs. 8.5% and 9.4% in AAs and HLs, respectively, and among HL sub-groups, the prevalence was found at 11.4% and 11.5% in Puerto Rican and Dominican populations, respectively. Follow-up analysis that adjusted for common risk factors demonstrated that Dominicans had the highest increased risk for PAD relative to EAs [OR = 3.15 (95% CI 2.33-4.25), p < 6.44 $×$ 10-14]. To investigate whether genetic factors may explain this increased risk, we performed admixture mapping by testing the association between local ancestry and PAD in Dominican BioMe participants (N = 1,813) separately from European, African, and Native American (NAT) continental ancestry tracts. The top association with PAD was an NAT ancestry tract at chromosome 2q35 [OR = 1.96 (SE = 0.16), p < 2.75 $×$ 10-05) with 22.6% vs. 12.9% PAD prevalence in heterozygous NAT tract carriers versus non-carriers, respectively. Fine-mapping at this locus implicated tag SNP rs78529201 located within a long intergenic non-coding RNA (lincRNA) LINC00607, a gene expression regulator of key genes related to thrombosis and extracellular remodeling of endothelial cells, suggesting a putative link of the 2q35 locus to PAD etiology. Efforts to reproduce the signal in other Hispanic cohorts were unsuccessful. In summary, we showed how leveraging health system data helped understand nuances of PAD risk across HL sub-groups and admixture mapping approaches elucidated a putative risk locus in a Dominican population.\n

\n\n\n

\n \n\n \n \n \n \n \n \n Disease risk and healthcare utilization among ancestrally diverse groups in the Los Angeles region.\n \n \n \n \n\n\n \n Caggiano, C.; Boudaie, A.; Shemirani, R.; Mefford, J.; Petter, E.; Chiu, A.; Ercelen, D.; He, R.; Tward, D.; Paul, K. C; Chang, T. S; Pasaniuc, B.; Kenny, E. E; Shortt, J. A; Gignoux, C. R; Balliu, B.; Arboleda, V. A; Belbin, G.; and Zaitlen, N.\n\n\n \n\n\n\n Nature Medicine, 29(7): 1845–1856. July 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Disease$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@ARTICLE{Caggiano2023-ue,\n  title    = "Disease risk and healthcare utilization among ancestrally diverse\n              groups in the Los Angeles region",\n  author   = "Caggiano, Christa and Boudaie, Arya and Shemirani, Ruhollah and\n              Mefford, Joel and Petter, Ella and Chiu, Alec and Ercelen, Defne\n              and He, Rosemary and Tward, Daniel and Paul, Kimberly C and\n              Chang, Timothy S and Pasaniuc, Bogdan and Kenny, Eimear E and\n              Shortt, Jonathan A and Gignoux, Christopher R and Balliu,\n              Brunilda and Arboleda, Valerie A and Belbin, Gillian and Zaitlen,\n              Noah",\n  abstract = "An individual's disease risk is affected by the populations that\n              they belong to, due to shared genetics and environmental factors.\n              The study of fine-scale populations in clinical care is important\n              for identifying and reducing health disparities and for\n              developing personalized interventions. To assess patterns of\n              clinical diagnoses and healthcare utilization by fine-scale\n              populations, we leveraged genetic data and electronic medical\n              records from 35,968 patients as part of the UCLA ATLAS Community\n              Health Initiative. We defined clusters of individuals using\n              identity by descent, a form of genetic relatedness that utilizes\n              shared genomic segments arising due to a common ancestor. In\n              total, we identified 376 clusters, including clusters with\n              patients of Afro-Caribbean, Puerto Rican, Lebanese Christian,\n              Iranian Jewish and Gujarati ancestry. Our analysis uncovered\n              1,218 significant associations between disease diagnoses and\n              clusters and 124 significant associations with specialty visits.\n              We also examined the distribution of pathogenic alleles and found\n              189 significant alleles at elevated frequency in particular\n              clusters, including many that are not regularly included in\n              population screening efforts. Overall, this work progresses the\n              understanding of health in understudied communities and can\n              provide the foundation for further study into health inequities.",\n  journal  = "Nature Medicine",\n  volume   =  29,\n  number   =  7,\n  pages    = "1845--1856",\n  month    =  jul,\n  year     =  2023,\n  language = "en",\n\tpmid = {37464048},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/37464048/},\n  bdsk-url-1 = {https://doi.org/10.1038/s41591-023-02425-1},\n  doi = {10.1038/s41591-023-02425-1}  \n}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals.\n \n \n \n \n\n\n \n Hou, K.; Ding, Y.; Xu, Z.; Wu, Y.; Bhattacharya, A.; Mester, R.; Belbin, G. M; Buyske, S.; Conti, D. V; Darst, B. F; Fornage, M.; Gignoux, C.; Guo, X.; Haiman, C.; Kenny, E. E; Kim, M.; Kooperberg, C.; Lange, L.; Manichaikul, A.; North, K. E; Peters, U.; Rasmussen-Torvik, L. J; Rich, S. S; Rotter, J. I; Wheeler, H. E; Wojcik, G. L; Zhou, Y.; Sankararaman, S.; and Pasaniuc, B.\n\n\n \n\n\n\n Nature Genetics, 55(4): 549–558. April 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Causal$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@ARTICLE{Hou2023-fm,\n  title    = "Causal effects on complex traits are similar for common variants\n              across segments of different continental ancestries within\n              admixed individuals",\n  author   = "Hou, Kangcheng and Ding, Yi and Xu, Ziqi and Wu, Yue and\n              Bhattacharya, Arjun and Mester, Rachel and Belbin, Gillian M and\n              Buyske, Steve and Conti, David V and Darst, Burcu F and Fornage,\n              Myriam and Gignoux, Chris and Guo, Xiuqing and Haiman,\n              Christopher and Kenny, Eimear E and Kim, Michelle and Kooperberg,\n              Charles and Lange, Leslie and Manichaikul, Ani and North, Kari E\n              and Peters, Ulrike and Rasmussen-Torvik, Laura J and Rich,\n              Stephen S and Rotter, Jerome I and Wheeler, Heather E and Wojcik,\n              Genevieve L and Zhou, Ying and Sankararaman, Sriram and Pasaniuc,\n              Bogdan",\n  abstract = "Individuals of admixed ancestries (for example, African\n              Americans) inherit a mosaic of ancestry segments (local ancestry)\n              originating from multiple continental ancestral populations. This\n              offers the unique opportunity of investigating the similarity of\n              genetic effects on traits across ancestries within the same\n              population. Here we introduce an approach to estimate correlation\n              of causal genetic effects (radmix) across local ancestries and\n              analyze 38 complex traits in African-European admixed individuals\n              (N = 53,001) to observe very high correlations (meta-analysis\n              radmix = 0.95, 95\\% credible interval 0.93-0.97), much higher\n              than correlation of causal effects across continental ancestries.\n              We replicate our results using regression-based methods from\n              marginal genome-wide association study summary statistics. We\n              also report realistic scenarios where regression-based methods\n              yield inflated heterogeneity-by-ancestry due to ancestry-specific\n              tagging of causal effects, and/or polygenicity. Our results\n              motivate genetic analyses that assume minimal heterogeneity in\n              causal effects by ancestry, with implications for the inclusion\n              of ancestry-diverse individuals in studies.",\n  journal  = "Nature Genetics",\n  volume   =  55,\n  number   =  4,\n  pages    = "549--558",\n  month    =  apr,\n  year     =  2023,\n  language = "en",\n\tpmid = {36941441},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/36941441/},\n  bdsk-url-1 = {https://doi.org/10.1038/s41588-023-01338-6},\n  doi = {10.1038/s41588-023-01338-6}  \n}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Selecting clustering algorithms for identity-by-descent mapping.\n \n \n \n \n\n\n \n Shemirani, R.; Belbin, G. M; Burghardt, K.; Lerman, K.; Avery, C. L; Kenny, E. E; Gignoux, C. R; and Ambite, J. L.\n\n\n \n\n\n\n Pacific Symposium on Biocomputing, 28: 121–132. 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Selecting$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@ARTICLE{Shemirani2023-vb,\n  title    = "Selecting clustering algorithms for identity-by-descent mapping",\n  author   = "Shemirani, Ruhollah and Belbin, Gillian M and Burghardt, Keith\n              and Lerman, Kristina and Avery, Christy L and Kenny, Eimear E and\n              Gignoux, Christopher R and Ambite, Jos{\\'e} Luis",\n  abstract = "Groups of distantly related individuals who share a short segment\n              of their genome identical-by-descent (IBD) can provide insights\n              about rare traits and diseases in massive biobanks using IBD\n              mapping. Clustering algorithms play an important role in finding\n              these groups accurately and at scale. We set out to analyze the\n              fitness of commonly used, fast and scalable clustering algorithms\n              for IBD mapping applications. We designed a realistic benchmark\n              for local IBD graphs and utilized it to compare the statistical\n              power of clustering algorithms via simulating 2.3 million\n              clusters across 850 experiments. We found Infomap and Markov\n              Clustering (MCL) community detection methods to have high\n              statistical power in most of the scenarios. They yield a 30\\%\n              increase in power compared to the current state-of-art approach,\n              with a 3 orders of magnitude lower runtime. We also found that\n              standard clustering metrics, such as modularity, cannot predict\n              statistical power of algorithms in IBD mapping applications. We\n              extend our findings to real datasets by analyzing the Population\n              Architecture using Genomics and Epidemiology (PAGE) Study dataset\n              with 51,000 samples and 2 million shared segments on Chromosome\n              1, resulting in the extraction of 39 million local IBD clusters.\n              We demonstrate the power of our approach by recovering signals of\n              rare genetic variation in the Whole-Exome Sequence data of\n              200,000 individuals in the UK Biobank. We provide an efficient\n              implementation to enable clustering at scale for IBD mapping for\n              various populations and scenarios.Supplementary Information: The\n              code, along with supplementary methods and figures are available\n              at https://github.com/roohy/localIBDClustering.",\n  journal  = "Pacific Symposium on Biocomputing",\n  volume   =  28,\n  pages    = "121--132",\n  year     =  2023,\n  language = "en",\n\tpmid = {36540970},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/36540970/},\n  bdsk-url-1 = {https://doi.org/10.1142/9789811270611_0012},\n  doi = {10.1142/9789811270611_0012}  \n}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n IMMerge: merging imputation data at scale.\n \n \n \n \n\n\n \n Zhu, W.; Chen, H.; Petty, A. S; Petty, L. E; Polikowsky, H. G; Gamazon, E. R; Below, J. E; and Highland, H. M\n\n\n \n\n\n\n Bioinformatics, 39(1). January 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"IMMerge:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@ARTICLE{Zhu2023-ti,\n  title     = "{IMMerge}: merging imputation data at scale",\n  author    = "Zhu, Wanying and Chen, Hung-Hsin and Petty, Alexander S and\n               Petty, Lauren E and Polikowsky, Hannah G and Gamazon, Eric R and\n               Below, Jennifer E and Highland, Heather M",\n  abstract  = "SUMMARY: Genomic data are often processed in batches and\n               analyzed together to save time. However, it is challenging to\n               combine multiple large VCFs and properly handle imputation\n               quality and missing variants due to the limitations of available\n               tools. To address these concerns, we developed IMMerge, a\n               Python-based tool that takes advantage of multiprocessing to\n               reduce running time. For the first time in a publicly available\n               tool, imputation quality scores are correctly combined with\n               Fisher's z transformation. AVAILABILITY AND IMPLEMENTATION:\n               IMMerge is an open-source project under MIT license. Source code\n               and user manual are available at\n               https://github.com/belowlab/IMMerge.",\n  journal   = "Bioinformatics",\n  publisher = "Oxford University Press (OUP)",\n  volume    =  39,\n  number    =  1,\n  month     =  jan,\n  year      =  2023,\n  copyright = "https://creativecommons.org/licenses/by/4.0/",\n  language  = "en",\n\tpmid = {36413071},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/36413071/},\n  bdsk-url-1 = {https://doi.org/10.1093/bioinformatics/btac750},\n  doi = {10.1093/bioinformatics/btac750}\n}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Assessment of multi-population polygenic risk scores for lipid traits in African Americans.\n \n \n \n \n\n\n \n Drouet, D. E; Liu, S.; and Crawford, D. C\n\n\n \n\n\n\n PeerJ, 11: e14910. May 2023.\n \n\n\n\n
\n\n\n\n \n \n $\"Assessment$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@ARTICLE{Drouet2023-sx,\n  title    = "Assessment of multi-population polygenic risk scores for lipid\n              traits in African Americans",\n  author   = "Drouet, Domenica E and Liu, Shiying and Crawford, Dana C",\n  abstract = "Polygenic risk scores (PRS) based on genome-wide discoveries are\n              promising predictors or classifiers of disease development,\n              severity, and/or progression for common clinical outcomes. A\n              major limitation of most risk scores is the paucity of\n              genome-wide discoveries in diverse populations, prompting an\n              emphasis to generate these needed data for trans-population and\n              population-specific PRS construction. Given diverse genome-wide\n              discoveries are just now being completed, there has been little\n              opportunity for PRS to be evaluated in diverse populations\n              independent from the discovery efforts. To fill this gap, we\n              leverage here summary data from a recent genome-wide discovery\n              study of lipid traits (HDL-C, LDL-C, triglycerides, and total\n              cholesterol) conducted in diverse populations represented by\n              African Americans, Hispanics, Asians, Native Hawaiians, Native\n              Americans, and others by the Population Architecture using\n              Genomics and Epidemiology (PAGE) Study. We constructed lipid\n              trait PRS using PAGE Study published genetic variants and weights\n              in an independent African American adult patient population\n              linked to de-identified electronic health records and genotypes\n              from the Illumina Metabochip (n = 3,254). Using multi-population\n              lipid trait PRS, we assessed levels of association for their\n              respective lipid traits, clinical outcomes (cardiovascular\n              disease and type 2 diabetes), and common clinical labs. While\n              none of the multi-population PRS were strongly associated with\n              the tested trait or outcome, PRSLDL-Cwas nominally associated\n              with cardiovascular disease. These data demonstrate the\n              complexity in applying PRS to real-world clinical data even when\n              data from multiple populations are available.",\n  journal  = "PeerJ",\n  volume   =  11,\n  pages    = "e14910",\n  month    =  may,\n  year     =  2023,\n  keywords = "African Americans; Biorepository; Electronic health records;\n              Genetic risk scores; Lipids; Metabochip; Polygenic risk scores",\n  language = "en",\n\tpmid = {37214096},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/37214096/},\n  bdsk-url-1 = {https://doi.org/10.7717/peerj.14910},\n  doi = {10.7717/peerj.14910}\n}\n\n\n

\n\n\n\n\n\n

\n\n

\n \n 2022\n \n \n (9)\n \n \n

\n \n \n

\n \n\n \n \n \n \n \n \n Gaseous air pollutants and DNA methylation in a methylome-wide association study of an ethnically and environmentally diverse population of U.S. adults.\n \n \n \n \n\n\n \n Holliday, K. M; Gondalia, R.; Baldassari, A.; Justice, A. E; Stewart, J. D; Liao, D.; Yanosky, J. D; Jordahl, K. M; Bhatti, P.; Assimes, T. L; Pankow, J. S; Guan, W.; Fornage, M.; Bressler, J.; North, K. E; Conneely, K. N; Li, Y.; Hou, L.; Vokonas, P. S; Ward-Caviness, C. K; Wilson, R.; Wolf, K.; Waldenberger, M.; Cyrys, J.; Peters, A.; Boezen, H M.; Vonk, J. M; Sayols-Baixeras, S.; Lee, M.; Baccarelli, A. A; and Whitsel, E. A\n\n\n \n\n\n\n Environmental Research, 212(Pt C): 113360. September 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Gaseous$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@ARTICLE{Holliday2022-po,\n  title     = "Gaseous air pollutants and {DNA} methylation in a methylome-wide\n               association study of an ethnically and environmentally diverse\n               population of {U.S}. adults",\n  author    = "Holliday, Katelyn M and Gondalia, Rahul and Baldassari, Antoine\n               and Justice, Anne E and Stewart, James D and Liao, Duanping and\n               Yanosky, Jeff D and Jordahl, Kristina M and Bhatti, Parveen and\n               Assimes, Themistocles L and Pankow, James S and Guan, Weihua and\n               Fornage, Myriam and Bressler, Jan and North, Kari E and\n               Conneely, Karen N and Li, Yun and Hou, Lifang and Vokonas,\n               Pantel S and Ward-Caviness, Cavin K and Wilson, Rory and Wolf,\n               Kathrin and Waldenberger, Melanie and Cyrys, Josef and Peters,\n               Annette and Boezen, H Marike and Vonk, Judith M and\n               Sayols-Baixeras, Sergi and Lee, Mikyeong and Baccarelli, Andrea\n               A and Whitsel, Eric A",\n  abstract  = "Epigenetic mechanisms may underlie air pollution-health outcome\n               associations. We estimated gaseous air pollutant-DNA methylation\n               (DNAm) associations using twelve subpopulations within Women's\n               Health Initiative (WHI) and Atherosclerosis Risk in Communities\n               (ARIC) cohorts (n = 8397; mean age 61.3 years; 83\\% female; 46\\%\n               African-American, 46\\% European-American, 8\\% Hispanic/Latino).\n               We used geocoded participant address-specific mean ambient\n               carbon monoxide (CO), nitrogen oxides (NO2; NOx), ozone (O3),\n               and sulfur dioxide (SO2) concentrations estimated over the 2-,\n               7-, 28-, and 365-day periods before collection of blood samples\n               used to generate Illumina 450 k array leukocyte DNAm\n               measurements. We estimated methylome-wide, subpopulation- and\n               race/ethnicity-stratified pollutant-DNAm associations in\n               multi-level, linear mixed-effects models adjusted for\n               sociodemographic, behavioral, meteorological, and technical\n               covariates. We combined stratum-specific estimates in inverse\n               variance-weighted meta-analyses and characterized significant\n               associations (false discovery rate; FDR 0.05). We attempted\n               replication in the Cooperative Health Research in Region of\n               Augsburg (KORA) study and Normative Aging Study (NAS). We\n               observed a -0.3 (95\\% CI: -0.4, -0.2) unit decrease in percent\n               DNAm per interquartile range (IQR, 7.3 ppb) increase in 28-day\n               mean NO2 concentration at cg01885635 (chromosome 3; regulatory\n               region 290 bp upstream from ZNF621; FDR = 0.03). At intragenic\n               sites cg21849932 (chromosome 20; LIME1; intron 3) and cg05353869\n               (chromosome 11; KLHL35; exon 2), we observed a -0.3 (95\\% CI:\n               -0.4, -0.2) unit decrease (FDR = 0.04) and a 1.2 (95\\% CI: 0.7,\n               1.7) unit increase (FDR = 0.04), respectively, in percent DNAm\n               per IQR (17.6 ppb) increase in 7-day mean ozone concentration.\n               Results were not fully replicated in KORA and NAS. We identified\n               three CpG sites potentially susceptible to gaseous air\n               pollution-induced DNAm changes near genes relevant for\n               cardiovascular and lung disease. Further harmonized\n               investigations with a range of gaseous pollutants and averaging\n               durations are needed to determine the effect of gaseous air\n               pollutants on DNA methylation and ultimately gene expression.",\n  journal   = "Environmental Research",\n  publisher = "Elsevier BV",\n  volume    =  212,\n  number    = "Pt C",\n  pages     = "113360",\n  month     =  sep,\n  year      =  2022,\n  keywords  = "Air pollution; DNA methylation; Epigenetics; Epigenome-wide\n               association study; Gaseous pollutants",\n  language  = "en",\n\tpmid = {35500859},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/35500859/},\n  bdsk-url-1 = {https://doi.org/10.1016/j.envres.2022.113360},\n  doi = {10.1016/j.envres.2022.113360}\n}\n\n

\n\n\n

\n Epigenetic mechanisms may underlie air pollution-health outcome associations. We estimated gaseous air pollutant-DNA methylation (DNAm) associations using twelve subpopulations within Women's Health Initiative (WHI) and Atherosclerosis Risk in Communities (ARIC) cohorts (n = 8397; mean age 61.3 years; 83% female; 46% African-American, 46% European-American, 8% Hispanic/Latino). We used geocoded participant address-specific mean ambient carbon monoxide (CO), nitrogen oxides (NO2; NOx), ozone (O3), and sulfur dioxide (SO2) concentrations estimated over the 2-, 7-, 28-, and 365-day periods before collection of blood samples used to generate Illumina 450 k array leukocyte DNAm measurements. We estimated methylome-wide, subpopulation- and race/ethnicity-stratified pollutant-DNAm associations in multi-level, linear mixed-effects models adjusted for sociodemographic, behavioral, meteorological, and technical covariates. We combined stratum-specific estimates in inverse variance-weighted meta-analyses and characterized significant associations (false discovery rate; FDR 0.05). We attempted replication in the Cooperative Health Research in Region of Augsburg (KORA) study and Normative Aging Study (NAS). We observed a -0.3 (95% CI: -0.4, -0.2) unit decrease in percent DNAm per interquartile range (IQR, 7.3 ppb) increase in 28-day mean NO2 concentration at cg01885635 (chromosome 3; regulatory region 290 bp upstream from ZNF621; FDR = 0.03). At intragenic sites cg21849932 (chromosome 20; LIME1; intron 3) and cg05353869 (chromosome 11; KLHL35; exon 2), we observed a -0.3 (95% CI: -0.4, -0.2) unit decrease (FDR = 0.04) and a 1.2 (95% CI: 0.7, 1.7) unit increase (FDR = 0.04), respectively, in percent DNAm per IQR (17.6 ppb) increase in 7-day mean ozone concentration. Results were not fully replicated in KORA and NAS. We identified three CpG sites potentially susceptible to gaseous air pollution-induced DNAm changes near genes relevant for cardiovascular and lung disease. Further harmonized investigations with a range of gaseous pollutants and averaging durations are needed to determine the effect of gaseous air pollutants on DNA methylation and ultimately gene expression.\n

\n\n\n

\n \n\n \n \n \n \n \n \n Genetic determinants of metabolic biomarkers and their associations with cardiometabolic traits in Hispanic/Latino adolescents.\n \n \n \n \n\n\n \n Kim, D.; Justice, A. E; Chittoor, G.; Blanco, E.; Burrows, R.; Graff, M.; Howard, A. G.; Wang, Y.; Rohde, R.; Buchanan, V. L; Voruganti, V S.; Almeida, M.; Peralta, J.; Lehman, D. M; Curran, J. E; Comuzzie, A. G; Duggirala, R.; Blangero, J.; Albala, C.; Santos, J. L; Angel, B.; Lozoff, B.; Gahagan, S.; and North, K. E\n\n\n \n\n\n\n Pediatric Research, 92(2): 563–571. August 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Genetic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@ARTICLE{Kim2022-in,\n  title     = "Genetic determinants of metabolic biomarkers and their\n               associations with cardiometabolic traits in {Hispanic/Latino}\n               adolescents",\n  author    = "Kim, Daeeun and Justice, Anne E and Chittoor, Geetha and Blanco,\n               Estela and Burrows, Raquel and Graff, Mariaelisa and Howard,\n               Annie Green and Wang, Yujie and Rohde, Rebecca and Buchanan,\n               Victoria L and Voruganti, V Saroja and Almeida, Marcio and\n               Peralta, Juan and Lehman, Donna M and Curran, Joanne E and\n               Comuzzie, Anthony G and Duggirala, Ravindranath and Blangero,\n               John and Albala, Cecilia and Santos, Jos{\\'e} L and Angel,\n               B{\\'a}rbara and Lozoff, Betsy and Gahagan, Sheila and North,\n               Kari E",\n  abstract  = "BACKGROUND: Metabolic regulation plays a significant role in\n               energy homeostasis, and adolescence is a crucial life stage for\n               the development of cardiometabolic disease (CMD). This study\n               aims to investigate the genetic determinants of metabolic\n               biomarkers-adiponectin, leptin, ghrelin, and orexin-and their\n               associations with CMD risk factors. METHODS: We characterized\n               the genetic determinants of the biomarkers among Hispanic/Latino\n               adolescents of the Santiago Longitudinal Study (SLS) and\n               identified the cumulative effects of genetic variants on\n               adiponectin and leptin using biomarker polygenic risk scores\n               (PRS). We further investigated the direct and indirect effect of\n               the biomarker PRS on downstream body fat percent (BF\\%) and\n               glycemic traits using structural equation modeling. RESULTS: We\n               identified putatively novel genetic variants associated with the\n               metabolic biomarkers. A substantial amount of biomarker variance\n               was explained by SLS-specific PRS, and the prediction was\n               improved by including the putatively novel loci. Fasting blood\n               insulin and insulin resistance were associated with PRS for\n               adiponectin, leptin, and ghrelin, and BF\\% was associated with\n               PRS for adiponectin and leptin. We found evidence of substantial\n               mediation of these associations by the biomarker levels.\n               CONCLUSIONS: The genetic underpinnings of metabolic biomarkers\n               can affect the early development of CMD, partly mediated by the\n               biomarkers. IMPACT: This study characterized the genetic\n               underpinnings of four metabolic hormones and investigated their\n               potential influence on adiposity and insulin biology among\n               Hispanic/Latino adolescents. Fasting blood insulin and insulin\n               resistance were associated with polygenic risk score (PRS) for\n               adiponectin, leptin, and ghrelin, with evidence of some degree\n               of mediation by the biomarker levels. Body fat percent (BF\\%)\n               was also associated with PRS for adiponectin and leptin. This\n               provides important insight on biological mechanisms underlying\n               early metabolic dysfunction and reveals candidates for\n               prevention efforts. Our findings also highlight the importance\n               of ancestrally diverse populations to facilitate valid studies\n               of the genetic architecture of metabolic biomarker levels.",\n  journal   = "Pediatric Research",\n  publisher = "Springer Science and Business Media LLC",\n  volume    =  92,\n  number    =  2,\n  pages     = "563--571",\n  month     =  aug,\n  year      =  2022,\n  language  = "en",\n\tpmid = {34645953},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/34645953/},\n  bdsk-url-1 = {https://doi.org/10.1038/s41390-021-01729-7},\n  doi = {10.1038/s41390-021-01729-7}\n}\n\n\n\n

\n\n\n

\n BACKGROUND: Metabolic regulation plays a significant role in energy homeostasis, and adolescence is a crucial life stage for the development of cardiometabolic disease (CMD). This study aims to investigate the genetic determinants of metabolic biomarkers-adiponectin, leptin, ghrelin, and orexin-and their associations with CMD risk factors. METHODS: We characterized the genetic determinants of the biomarkers among Hispanic/Latino adolescents of the Santiago Longitudinal Study (SLS) and identified the cumulative effects of genetic variants on adiponectin and leptin using biomarker polygenic risk scores (PRS). We further investigated the direct and indirect effect of the biomarker PRS on downstream body fat percent (BF%) and glycemic traits using structural equation modeling. RESULTS: We identified putatively novel genetic variants associated with the metabolic biomarkers. A substantial amount of biomarker variance was explained by SLS-specific PRS, and the prediction was improved by including the putatively novel loci. Fasting blood insulin and insulin resistance were associated with PRS for adiponectin, leptin, and ghrelin, and BF% was associated with PRS for adiponectin and leptin. We found evidence of substantial mediation of these associations by the biomarker levels. CONCLUSIONS: The genetic underpinnings of metabolic biomarkers can affect the early development of CMD, partly mediated by the biomarkers. IMPACT: This study characterized the genetic underpinnings of four metabolic hormones and investigated their potential influence on adiposity and insulin biology among Hispanic/Latino adolescents. Fasting blood insulin and insulin resistance were associated with polygenic risk score (PRS) for adiponectin, leptin, and ghrelin, with evidence of some degree of mediation by the biomarker levels. Body fat percent (BF%) was also associated with PRS for adiponectin and leptin. This provides important insight on biological mechanisms underlying early metabolic dysfunction and reveals candidates for prevention efforts. Our findings also highlight the importance of ancestrally diverse populations to facilitate valid studies of the genetic architecture of metabolic biomarker levels.\n

\n\n\n

\n \n\n \n \n \n \n \n \n Analyses of biomarker traits in diverse UK biobank participants identify associations missed by European-centric analysis strategies.\n \n \n \n \n\n\n \n Sun, Q.; Graff, M.; Rowland, B.; Wen, J.; Huang, L.; Miller-Fleming, T. W; Haessler, J.; Preuss, M. H; Chai, J.; Lee, M. P; Avery, C. L; Cheng, C.; Franceschini, N.; Sim, X.; Cox, N. J; Kooperberg, C.; North, K. E; Li, Y.; and Raffield, L. M\n\n\n \n\n\n\n J Hum Genet, 67(2): 87-93. Feb 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Analyses$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{Sun:2022aa,\n\tabstract = {Despite the dramatic underrepresentation of non-European populations in human genetics studies, researchers continue to exclude participants of non-European ancestry, as well as variants rare in European populations, even when these data are available. This practice perpetuates existing research disparities and can lead to important and large effect size associations being missed. Here, we conducted genome-wide association studies (GWAS) of 31 serum and urine biomarker quantitative traits in African (n = 9354), East Asian (n = 2559), and South Asian (n = 9823) ancestry UK Biobank (UKBB) participants. We adjusted for all known GWAS catalog variants for each trait, as well as novel signals identified in a recent European ancestry-focused analysis of UKBB participants. We identify 7 novel signals in African ancestry and 2 novel signals in South Asian ancestry participants (p < 1.61E-10). Many of these signals are highly plausible, including a cis pQTL for the gene encoding gamma-glutamyl transferase and PIEZO1 and G6PD variants with impacts on HbA1c through likely erythrocytic mechanisms. This work illustrates the importance of using the genetic data we already have in diverse populations, with novel discoveries possible in even modest sample sizes.},\n\tauthor = {Sun, Quan and Graff, Misa and Rowland, Bryce and Wen, Jia and Huang, Le and Miller-Fleming, Tyne W and Haessler, Jeffrey and Preuss, Michael H and Chai, Jin-Fang and Lee, Moa P and Avery, Christy L and Cheng, Ching-Yu and Franceschini, Nora and Sim, Xueling and Cox, Nancy J and Kooperberg, Charles and North, Kari E and Li, Yun and Raffield, Laura M},\n\tdate-added = {2022-09-26 11:26:01 -0400},\n\tdate-modified = {2022-09-26 11:26:01 -0400},\n\tdoi = {10.1038/s10038-021-00968-0},\n\tjournal = {J Hum Genet},\n\tjournal-full = {Journal of human genetics},\n\tmesh = {Alleles; Asians; Biological Specimen Banks; Biomarkers; Blacks; Female; Gene Frequency; Genetic Predisposition to Disease; Genome-Wide Association Study; Genotype; Humans; Male; Phenotype; Polymorphism, Single Nucleotide; Quantitative Trait Loci; United Kingdom; Whites},\n\tmonth = {Feb},\n\tnumber = {2},\n\tpages = {87-93},\n\tpmc = {PMC8792153},\n\tpmid = {34376796},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/34376796/},\n\tpst = {ppublish},\n\ttitle = {Analyses of biomarker traits in diverse UK biobank participants identify associations missed by European-centric analysis strategies},\n\tvolume = {67},\n\tyear = {2022},\n\tbdsk-url-1 = {https://doi.org/10.1038/s10038-021-00968-0}\n\t\t}\n\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n A large-scale transcriptome-wide association study (TWAS) of 10 blood cell phenotypes reveals complexities of TWAS fine-mapping.\n \n \n \n \n\n\n \n Tapia, A. L; Rowland, B. T; Rosen, J. D; Preuss, M.; Young, K.; Graff, M.; Choquet, H.; Couper, D. J; Buyske, S.; Bien, S. A; Jorgenson, E.; Kooperberg, C.; Loos, R. J F; Morrison, A. C; North, K. E; Yu, B.; Reiner, A. P; Li, Y.; and Raffield, L. M\n\n\n \n\n\n\n Genet Epidemiol, 46(1): 3-16. Feb 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@article{Tapia:2022aa,\n\tabstract = {Hematological measures are important intermediate clinical phenotypes for many acute and chronic diseases and are highly heritable. Although genome-wide association studies (GWAS) have identified thousands of loci containing trait-associated variants, the causal genes underlying these associations are often uncertain. To better understand the underlying genetic regulatory mechanisms, we performed a transcriptome-wide association study (TWAS) to systematically investigate the association between genetically predicted gene expression and hematological measures in 54,542 Europeans from the Genetic Epidemiology Research on Aging cohort. We found 239 significant gene-trait associations with hematological measures; we replicated 71 associations at p < 0.05 in a TWAS meta-analysis consisting of up to 35,900 Europeans from the Women's Health Initiative, Atherosclerosis Risk in Communities Study, and BioMe Biobank. Additionally, we attempted to refine this list of candidate genes by performing conditional analyses, adjusting for individual variants previously associated with hematological measures, and performed further fine-mapping of TWAS loci. To facilitate interpretation of our findings, we designed an R Shiny application to interactively visualize our TWAS results by integrating them with additional genetic data sources (GWAS, TWAS from multiple reference panels, conditional analyses, known GWAS variants, etc.). Our results and application highlight frequently overlooked TWAS challenges and illustrate the complexity of TWAS fine-mapping.},\n\tauthor = {Tapia, Amanda L and Rowland, Bryce T and Rosen, Jonathan D and Preuss, Michael and Young, Kris and Graff, Misa and Choquet, H{\\'e}l{\\`e}ne and Couper, David J and Buyske, Steve and Bien, Stephanie A and Jorgenson, Eric and Kooperberg, Charles and Loos, Ruth J F and Morrison, Alanna C and North, Kari E and Yu, Bing and Reiner, Alexander P and Li, Yun and Raffield, Laura M},\n\tdate-added = {2022-09-26 11:25:47 -0400},\n\tdate-modified = {2022-09-26 11:25:47 -0400},\n\tdoi = {10.1002/gepi.22436},\n\tjournal = {Genet Epidemiol},\n\tjournal-full = {Genetic epidemiology},\n\tkeywords = {R Shiny; TWAS; fine-mapping; hematological traits},\n\tmesh = {Blood Cells; Female; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Phenotype; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Transcriptome},\n\tmonth = {Feb},\n\tnumber = {1},\n\tpages = {3-16},\n\tpmc = {PMC8887641},\n\tpmid = {34779012},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/34779012/},\n\tpst = {ppublish},\n\ttitle = {A large-scale transcriptome-wide association study (TWAS) of 10 blood cell phenotypes reveals complexities of TWAS fine-mapping},\n\tvolume = {46},\n\tyear = {2022},\n\tbdsk-url-1 = {https://doi.org/10.1002/gepi.22436}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits.\n \n \n \n \n\n\n \n Highland, H. M; Wojcik, G. L; Graff, M.; Nishimura, K. K; Hodonsky, C. J; Baldassari, A. R; Cote, A. C; Cheng, I.; Gignoux, C. R; Tao, R.; Li, Y.; Boerwinkle, E.; Fornage, M.; Haessler, J.; Hindorff, L. A; Hu, Y.; Justice, A. E; Lin, B. M; Lin, D.; Stram, D. O; Haiman, C. A; Kooperberg, C.; Le Marchand, L.; Matise, T. C; Kenny, E. E; Carlson, C. S; Stahl, E. A; Avery, C. L; North, K. E; Ambite, J. L.; Buyske, S.; Loos, R. J; Peters, U.; Young, K. L; Bien, S. A; and Huckins, L. M\n\n\n \n\n\n\n Am J Hum Genet, 109(4): 669-679. Apr 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Predicted$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{Highland:2022aa,\n\tabstract = {One mechanism by which genetic factors influence complex traits and diseases is altering gene expression. Direct measurement of gene expression in relevant tissues is rarely tenable; however, genetically regulated gene expression (GReX) can be estimated using prediction models derived from large multi-omic datasets. These approaches have led to the discovery of many gene-trait associations, but whether models derived from predominantly European ancestry (EA) reference panels can map novel associations in ancestrally diverse populations remains unclear. We applied PrediXcan to impute GReX in 51,520 ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) participants (35% African American, 45% Hispanic/Latino, 10% Asian, and 7% Hawaiian) across 25 key cardiometabolic traits and relevant tissues to identify 102 novel associations. We then compared associations in PAGE to those in a random subset of 50,000 White British participants from UK Biobank (UKBB50k) for height and body mass index (BMI). We identified 517 associations across 47 tissues in PAGE but not UKBB50k, demonstrating the importance of diverse samples in identifying trait-associated GReX. We observed that variants used in PrediXcan models were either more or less differentiated across continental-level populations than matched-control variants depending on the specific population reflecting sampling bias. Additionally, variants from identified genes specific to either PAGE or UKBB50k analyses were more ancestrally differentiated than those in genes detected in both analyses, underlining the value of population-specific discoveries. This suggests that while EA-derived transcriptome imputation models can identify new associations in non-EA populations, models derived from closely matched reference panels may yield further insights. Our findings call for more diversity in reference datasets of tissue-specific gene expression.},\n\tauthor = {Highland, Heather M and Wojcik, Genevieve L and Graff, Mariaelisa and Nishimura, Katherine K and Hodonsky, Chani J and Baldassari, Antoine R and Cote, Alanna C and Cheng, Iona and Gignoux, Christopher R and Tao, Ran and Li, Yuqing and Boerwinkle, Eric and Fornage, Myriam and Haessler, Jeffrey and Hindorff, Lucia A and Hu, Yao and Justice, Anne E and Lin, Bridget M and Lin, Danyu and Stram, Daniel O and Haiman, Christopher A and Kooperberg, Charles and Le Marchand, Loic and Matise, Tara C and Kenny, Eimear E and Carlson, Christopher S and Stahl, Eli A and Avery, Christy L and North, Kari E and Ambite, Jose Luis and Buyske, Steven and Loos, Ruth J and Peters, Ulrike and Young, Kristin L and Bien, Stephanie A and Huckins, Laura M},\n\tdate-added = {2022-09-26 11:25:18 -0400},\n\tdate-modified = {2022-09-26 11:25:18 -0400},\n\tdoi = {10.1016/j.ajhg.2022.02.013},\n\tjournal = {Am J Hum Genet},\n\tjournal-full = {American journal of human genetics},\n\tkeywords = {PrediXcan, TWAS, ancestrally diverse, gene expression, cardiometabolic traits, PAGE},\n\tmesh = {Cardiovascular Diseases; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Life Style; Polymorphism, Single Nucleotide; Transcriptome},\n\tmonth = {Apr},\n\tnumber = {4},\n\tpages = {669-679},\n\tpmc = {PMC9069067},\n\tpmid = {35263625},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/35263625/},\n\tpst = {ppublish},\n\ttitle = {Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits},\n\tvolume = {109},\n\tyear = {2022},\n\tbdsk-url-1 = {https://doi.org/10.1016/j.ajhg.2022.02.013}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries.\n \n \n \n \n\n\n \n Smith, S. P.; Shahamatdar, S.; Cheng, W.; Zhang, S.; Paik, J.; Graff, M.; Haiman, C.; Matise, T C; North, K. E; Peters, U.; Kenny, E.; Gignoux, C.; Wojcik, G.; Crawford, L.; and Ramachandran, S.\n\n\n \n\n\n\n Am J Hum Genet, 109(5): 871-884. May 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Enrichment$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

@article{Smith:2022aa,\n\tabstract = {Since 2005, genome-wide association (GWA) datasets have been largely biased toward sampling European ancestry individuals, and recent studies have shown that GWA results estimated from self-identified European individuals are not transferable to non-European individuals because of various confounding challenges. Here, we demonstrate that enrichment analyses that aggregate SNP-level association statistics at multiple genomic scales-from genes to genomic regions and pathways-have been underutilized in the GWA era and can generate biologically interpretable hypotheses regarding the genetic basis of complex trait architecture. We illustrate examples of the robust associations generated by enrichment analyses while studying 25 continuous traits assayed in 566,786 individuals from seven diverse self-identified human ancestries in the UK Biobank and the Biobank Japan as well as 44,348 admixed individuals from the PAGE consortium including cohorts of African American, Hispanic and Latin American, Native Hawaiian, and American Indian/Alaska Native individuals. We identify 1,000 gene-level associations that are genome-wide significant in at least two ancestry cohorts across these 25 traits as well as highly conserved pathway associations with triglyceride levels in European, East Asian, and Native Hawaiian cohorts.},\n\tauthor = {Smith, Samuel Pattillo and Shahamatdar, Sahar and Cheng, Wei and Zhang, Selena and Paik, Joseph and Graff, Misa and Haiman, Christopher and Matise, T C and North, Kari E and Peters, Ulrike and Kenny, Eimear and Gignoux, Chris and Wojcik, Genevieve and Crawford, Lorin and Ramachandran, Sohini},\n\tdate-added = {2022-09-26 11:23:41 -0400},\n\tdate-modified = {2022-09-26 11:23:41 -0400},\n\tdoi = {10.1016/j.ajhg.2022.03.005},\n\tjournal = {Am J Hum Genet},\n\tjournal-full = {American journal of human genetics},\n\tkeywords = {GWAS, multi-ancestry, enrichment analyses},\n\tmesh = {Genome-Wide Association Study; Humans; Multifactorial Inheritance; Phenotype; Polymorphism, Single Nucleotide; Racial Groups},\n\tmonth = {May},\n\tnumber = {5},\n\tpages = {871-884},\n\tpmc = {PMC9118115},\n\tpmid = {35349783},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/35349783/},\n\tpst = {ppublish},\n\ttitle = {Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries},\n\tvolume = {109},\n\tyear = {2022},\n\tbdsk-url-1 = {https://doi.org/10.1016/j.ajhg.2022.03.005}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Genetic pleiotropy underpinning adiposity and inflammation in self-identified Hispanic/Latino populations.\n \n \n \n \n\n\n \n Anwar, M. Y.; Baldassari, A. R; Polikowsky, H. G; Sitlani, C. M; Highland, H. M; Chami, N.; Chen, H.; Graff, M.; Howard, A. G.; Jung, S. Y.; Petty, L. E; Wang, Z.; Zhu, W.; Buyske, S.; Cheng, I.; Kaplan, R.; Kooperberg, C.; Loos, R. J F; Peters, U.; McCormick, J. B; Fisher-Hoch, S. P; Avery, C. L; Taylor, K. C; Below, J. E; and North, K. E\n\n\n \n\n\n\n BMC Med Genomics, 15(1): 192. Sep 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Genetic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@article{Anwar:2022aa,\n\tabstract = {BACKGROUND: Concurrent variation in adiposity and inflammation suggests potential shared functional pathways and pleiotropic disease underpinning. Yet, exploration of pleiotropy in the context of adiposity-inflammation has been scarce, and none has included self-identified Hispanic/Latino populations. Given the high level of ancestral diversity in Hispanic American population, genetic studies may reveal variants that are infrequent/monomorphic in more homogeneous populations.\nMETHODS: Using multi-trait Adaptive Sum of Powered Score (aSPU) method, we examined individual and shared genetic effects underlying inflammatory (CRP) and adiposity-related traits (Body Mass Index [BMI]), and central adiposity (Waist to Hip Ratio [WHR]) in HLA participating in the Population Architecture Using Genomics and Epidemiology (PAGE) cohort (N = 35,871) with replication of effects in the Cameron County Hispanic Cohort (CCHC) which consists of Mexican American individuals.\nRESULTS: Of the > 16 million SNPs tested, variants representing 7 independent loci were found to illustrate significant association with multiple traits. Two out of 7 variants were replicated at statistically significant level in multi-trait analyses in CCHC. The lead variant on APOE (rs439401) and rs11208712 were found to harbor multi-trait associations with adiposity and inflammation.\nCONCLUSIONS: Results from this study demonstrate the importance of considering pleiotropy for improving our understanding of the etiology of the various metabolic pathways that regulate cardiovascular disease development.},\n\tauthor = {Anwar, Mohammad Yaser and Baldassari, Antoine R and Polikowsky, Hannah G and Sitlani, Colleen M and Highland, Heather M and Chami, Nathalie and Chen, Hung-Hsin and Graff, Mariaelisa and Howard, Annie Green and Jung, Su Yon and Petty, Lauren E and Wang, Zhe and Zhu, Wanying and Buyske, Steven and Cheng, Iona and Kaplan, Robert and Kooperberg, Charles and Loos, Ruth J F and Peters, Ulrike and McCormick, Joseph B and Fisher-Hoch, Susan P and Avery, Christy L and Taylor, Kira C and Below, Jennifer E and North, Kari E},\n\tdate-added = {2022-09-26 11:22:56 -0400},\n\tdate-modified = {2022-09-26 11:22:56 -0400},\n\tdoi = {10.1186/s12920-022-01352-3},\n\tjournal = {BMC Med Genomics},\n\tjournal-full = {BMC medical genomics},\n\tkeywords = {Genetic pleiotropy; Hispanic Americans; Inflammation; Obesity},\n\tmesh = {Adiposity; Genetic Pleiotropy; Hispanic or Latino; Humans; Inflammation; Obesity},\n\tmonth = {Sep},\n\tnumber = {1},\n\tpages = {192},\n\tpmc = {PMC9464371},\n\tpmid = {36088317},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/36088317/},\tpst = {epublish},\n\ttitle = {Genetic pleiotropy underpinning adiposity and inflammation in self-identified Hispanic/Latino populations},\n\tvolume = {15},\n\tyear = {2022},\n\tbdsk-url-1 = {https://doi.org/10.1186/s12920-022-01352-3}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Genome-Wide Epistatic Interaction between DEF1B and APOL1 High-Risk Genotypes for Chronic Kidney Disease.\n \n \n \n \n\n\n \n Vy, H. M. T; Lin, B. M; Gulamali, F. F; Kooperberg, C.; Graff, M.; Wong, J.; Campbell, K. N; Matise, T. C; Coresh, J.; Thomas, F.; Reiner, A. P; Nassir, R.; Schnatz, P. F; Johns, T.; Buyske, S.; Haiman, C.; Cooper, R.; Loos, R. J F; Horowitz, C. R; Gutierrez, O. M; Do, R.; Franceschini, N.; and Nadkarni, G. N\n\n\n \n\n\n\n Clin J Am Soc Nephrol. Aug 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Genome-Wide$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@article{Vy:2022aa,\n\tauthor = {Vy, Ha My T and Lin, Bridget M and Gulamali, Faris F and Kooperberg, Charles and Graff, Mariaelisa and Wong, Jenny and Campbell, Kirk N and Matise, Tara C and Coresh, Josef and Thomas, Fridtjof and Reiner, Alexander P and Nassir, Rami and Schnatz, Peter F and Johns, Tanya and Buyske, Steven and Haiman, Christopher and Cooper, Richard and Loos, Ruth J F and Horowitz, Carol R and Gutierrez, Orlando M and Do, Ron and Franceschini, Nora and Nadkarni, Girish N},\n\tdate-added = {2022-09-26 11:22:08 -0400},\n\tdate-modified = {2022-09-26 11:22:08 -0400},\n\tdoi = {10.2215/CJN.03610322},\n\tjournal = {Clin J Am Soc Nephrol},\n\tjournal-full = {Clinical journal of the American Society of Nephrology : CJASN},\n\tkeywords = {APOL1; chronic kidney disease; disparity; genetic variation; human genetics},\n\tmonth = {Aug},\n\tpmid = {35948364},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/35948364/},\tpst = {aheadofprint},\n\ttitle = {Genome-Wide Epistatic Interaction between DEF1B and APOL1 High-Risk Genotypes for Chronic Kidney Disease},\n\tyear = {2022},\n\tbdsk-url-1 = {https://doi.org/10.2215/CJN.03610322}}\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Large-scale genome-wide association study of coronary artery disease in genetically diverse populations.\n \n \n \n \n\n\n \n Tcheandjieu, C.; Zhu, X.; Hilliard, A. T; Clarke, S. L; Napolioni, V.; Ma, S.; Lee, K. M.; Fang, H.; Chen, F.; Lu, Y.; Tsao, N. L; Raghavan, S.; Koyama, S.; Gorman, B. R; Vujkovic, M.; Klarin, D.; Levin, M. G; Sinnott-Armstrong, N.; Wojcik, G. L; Plomondon, M. E; Maddox, T. M; Waldo, S. W; Bick, A. G; Pyarajan, S.; Huang, J.; Song, R.; Ho, Y.; Buyske, S.; Kooperberg, C.; Haessler, J.; Loos, R. J F; Do, R.; Verbanck, M.; Chaudhary, K.; North, K. E; Avery, C. L; Graff, M.; Haiman, C. A; Le Marchand, L.; Wilkens, L. R; Bis, J. C; Leonard, H.; Shen, B.; Lange, L. A; Giri, A.; Dikilitas, O.; Kullo, I. J; Stanaway, I. B; Jarvik, G. P; Gordon, A. S; Hebbring, S.; Namjou, B.; Kaufman, K. M; Ito, K.; Ishigaki, K.; Kamatani, Y.; Verma, S. S; Ritchie, M. D; Kember, R. L; Baras, A.; Lotta, L. A; Regeneron Genetics Center; CARDIoGRAMplusC4D Consortium; Biobank Japan; Million Veteran Program; Kathiresan, S.; Hauser, E. R; Miller, D. R; Lee, J. S; Saleheen, D.; Reaven, P. D; Cho, K.; Gaziano, J M.; Natarajan, P.; Huffman, J. E; Voight, B. F; Rader, D. J; Chang, K.; Lynch, J. A; Damrauer, S. M; Wilson, P. W F; Tang, H.; Sun, Y. V; Tsao, P. S; O'Donnell, C. J; and Assimes, T. L\n\n\n \n\n\n\n Nat Med, 28(8): 1679-1692. Aug 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Large-scale$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{Tcheandjieu:2022aa,\n\tabstract = {We report a genome-wide association study (GWAS) of coronary artery disease (CAD) incorporating nearly a quarter of a million cases, in which existing studies are integrated with data from cohorts of white, Black and Hispanic individuals from the Million Veteran Program. We document near equivalent heritability of CAD across multiple ancestral groups, identify 95 novel loci, including nine on the X chromosome, detect eight loci of genome-wide significance in Black and Hispanic individuals, and demonstrate that two common haplotypes at the 9p21 locus are responsible for risk stratification in all populations except those of African origin, in which these haplotypes are virtually absent. Moreover, in the largest GWAS for angiographically derived coronary atherosclerosis performed to date, we find 15 loci of genome-wide significance that robustly overlap with established loci for clinical CAD. Phenome-wide association analyses of novel loci and polygenic risk scores (PRSs) augment signals related to insulin resistance, extend pleiotropic associations of these loci to include smoking and family history, and precisely document the markedly reduced transferability of existing PRSs to Black individuals. Downstream integrative analyses reinforce the critical roles of vascular endothelial, fibroblast, and smooth muscle cells in CAD susceptibility, but also point to a shared biology between atherosclerosis and oncogenesis. This study highlights the value of diverse populations in further characterizing the genetic architecture of CAD.},\n\tauthor = {Tcheandjieu, Catherine and Zhu, Xiang and Hilliard, Austin T and Clarke, Shoa L and Napolioni, Valerio and Ma, Shining and Lee, Kyung Min and Fang, Huaying and Chen, Fei and Lu, Yingchang and Tsao, Noah L and Raghavan, Sridharan and Koyama, Satoshi and Gorman, Bryan R and Vujkovic, Marijana and Klarin, Derek and Levin, Michael G and Sinnott-Armstrong, Nasa and Wojcik, Genevieve L and Plomondon, Mary E and Maddox, Thomas M and Waldo, Stephen W and Bick, Alexander G and Pyarajan, Saiju and Huang, Jie and Song, Rebecca and Ho, Yuk-Lam and Buyske, Steven and Kooperberg, Charles and Haessler, Jeffrey and Loos, Ruth J F and Do, Ron and Verbanck, Marie and Chaudhary, Kumardeep and North, Kari E and Avery, Christy L and Graff, Mariaelisa and Haiman, Christopher A and Le Marchand, Lo{\\"\\i}c and Wilkens, Lynne R and Bis, Joshua C and Leonard, Hampton and Shen, Botong and Lange, Leslie A and Giri, Ayush and Dikilitas, Ozan and Kullo, Iftikhar J and Stanaway, Ian B and Jarvik, Gail P and Gordon, Adam S and Hebbring, Scott and Namjou, Bahram and Kaufman, Kenneth M and Ito, Kaoru and Ishigaki, Kazuyoshi and Kamatani, Yoichiro and Verma, Shefali S and Ritchie, Marylyn D and Kember, Rachel L and Baras, Aris and Lotta, Luca A and {Regeneron Genetics Center} and {CARDIoGRAMplusC4D Consortium} and {Biobank Japan} and {Million Veteran Program} and Kathiresan, Sekar and Hauser, Elizabeth R and Miller, Donald R and Lee, Jennifer S and Saleheen, Danish and Reaven, Peter D and Cho, Kelly and Gaziano, J Michael and Natarajan, Pradeep and Huffman, Jennifer E and Voight, Benjamin F and Rader, Daniel J and Chang, Kyong-Mi and Lynch, Julie A and Damrauer, Scott M and Wilson, Peter W F and Tang, Hua and Sun, Yan V and Tsao, Philip S and O'Donnell, Christopher J and Assimes, Themistocles L},\n\tdate-added = {2022-09-26 11:20:21 -0400},\n\tdate-modified = {2022-09-26 11:20:21 -0400},\n\tdoi = {10.1038/s41591-022-01891-3},\n\tjournal = {Nat Med},\n\tjournal-full = {Nature medicine},\n\tmesh = {Coronary Artery Disease; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Polymorphism, Single Nucleotide; Risk Factors},\n\tmonth = {Aug},\n\tnumber = {8},\n\tpages = {1679-1692},\n\tpmc = {PMC9419655},\n\tpmid = {35915156},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/35915156/},\tpst = {ppublish},\n\ttitle = {Large-scale genome-wide association study of coronary artery disease in genetically diverse populations},\n\tvolume = {28},\n\tyear = {2022},\n\tbdsk-url-1 = {https://doi.org/10.1038/s41591-022-01891-3}}\n\n

\n\n\n\n\n\n

\n\n

\n \n 2021\n \n \n (9)\n \n \n

\n \n \n

\n \n\n \n \n \n \n \n \n Transcriptome-Wide Association Study of Blood Cell Traits in African Ancestry and Hispanic/Latino Populations.\n \n \n \n \n\n\n \n Wen, J.; Xie, M.; Rowland, B.; Rosen, J. D; Sun, Q.; Chen, J.; Tapia, A. L; Qian, H.; Kowalski, M. H; Shan, Y.; Young, K. L; Graff, M.; Argos, M.; Avery, C. L; Bien, S. A; Buyske, S.; Yin, J.; Choquet, H.; Fornage, M.; Hodonsky, C. J; Jorgenson, E.; Kooperberg, C.; Loos, R. J F; Liu, Y.; Moon, J.; North, K. E; Rich, S. S; Rotter, J. I; Smith, J. A; Zhao, W.; Shang, L.; Wang, T.; Zhou, X.; Reiner, A. P; Raffield, L. M; and Li, Y.\n\n\n \n\n\n\n Genes (Basel), 12(7). Jul 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Transcriptome-Wide$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@article{Wen:2021aa,\n\tabstract = {BACKGROUND: Thousands of genetic variants have been associated with hematological traits, though target genes remain unknown at most loci. Moreover, limited analyses have been conducted in African ancestry and Hispanic/Latino populations; hematological trait associated variants more common in these populations have likely been missed.\nMETHODS: To derive gene expression prediction models, we used ancestry-stratified datasets from the Multi-Ethnic Study of Atherosclerosis (MESA, including n = 229 African American and n = 381 Hispanic/Latino participants, monocytes) and the Depression Genes and Networks study (DGN, n = 922 European ancestry participants, whole blood). We then performed a transcriptome-wide association study (TWAS) for platelet count, hemoglobin, hematocrit, and white blood cell count in African (n = 27,955) and Hispanic/Latino (n = 28,324) ancestry participants.\nRESULTS: Our results revealed 24 suggestive signals (p < 1 × 10-4) that were conditionally distinct from known GWAS identified variants and successfully replicated these signals in European ancestry subjects from UK Biobank. We found modestly improved correlation of predicted and measured gene expression in an independent African American cohort (the Genetic Epidemiology Network of Arteriopathy (GENOA) study (n = 802), lymphoblastoid cell lines) using the larger DGN reference panel; however, some genes were well predicted using MESA but not DGN.\nCONCLUSIONS: These analyses demonstrate the importance of performing TWAS and other genetic analyses across diverse populations and of balancing sample size and ancestry background matching when selecting a TWAS reference panel.},\n\tauthor = {Wen, Jia and Xie, Munan and Rowland, Bryce and Rosen, Jonathan D and Sun, Quan and Chen, Jiawen and Tapia, Amanda L and Qian, Huijun and Kowalski, Madeline H and Shan, Yue and Young, Kristin L and Graff, Marielisa and Argos, Maria and Avery, Christy L and Bien, Stephanie A and Buyske, Steve and Yin, Jie and Choquet, H{\\'e}l{\\`e}ne and Fornage, Myriam and Hodonsky, Chani J and Jorgenson, Eric and Kooperberg, Charles and Loos, Ruth J F and Liu, Yongmei and Moon, Jee-Young and North, Kari E and Rich, Stephen S and Rotter, Jerome I and Smith, Jennifer A and Zhao, Wei and Shang, Lulu and Wang, Tao and Zhou, Xiang and Reiner, Alexander P and Raffield, Laura M and Li, Yun},\n\tdate-added = {2022-09-26 11:25:31 -0400},\n\tdate-modified = {2022-09-26 11:25:31 -0400},\n\tdoi = {10.3390/genes12071049},\n\tjournal = {Genes (Basel)},\n\tjournal-full = {Genes},\n\tkeywords = {TWAS (transcriptome-wide association study); ancestry; expression analysis; non-European populations},\n\tmesh = {African Americans; Blood Cells; Cohort Studies; Genetic Predisposition to Disease; Genome-Wide Association Study; Hispanic or Latino; Humans; Phenotype; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Transcriptome; Whites},\n\tmonth = {Jul},\n\tnumber = {7},\n\tpmc = {PMC8307403},\n\tpmid = {34356065},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/34356065/},\n\tpst = {epublish},\n\ttitle = {Transcriptome-Wide Association Study of Blood Cell Traits in African Ancestry and Hispanic/Latino Populations},\n\tvolume = {12},\n\tyear = {2021},\n\tbdsk-url-1 = {https://doi.org/10.3390/genes12071049}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Admixed Populations Improve Power for Variant Discovery and Portability in Genome-Wide Association Studies.\n \n \n \n \n\n\n \n Lin, M.; Park, D. S; Zaitlen, N. A; Henn, B. M; and Gignoux, C. R\n\n\n \n\n\n\n Front Genet, 12: 673167. 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Admixed$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@article{Lin:2021wh,\n\tabstract = {Genome-wide association studies (GWAS) are primarily conducted in single-ancestry settings. The low transferability of results has limited our understanding of human genetic architecture across a range of complex traits. In contrast to homogeneous populations, admixed populations provide an opportunity to capture genetic architecture contributed from multiple source populations and thus improve statistical power. Here, we provide a mechanistic simulation framework to investigate the statistical power and transferability of GWAS under directional polygenic selection or varying divergence. We focus on a two-way admixed population and show that GWAS in admixed populations can be enriched for power in discovery by up to 2-fold compared to the ancestral populations under similar sample size. Moreover, higher accuracy of cross-population polygenic score estimates is also observed if variants and weights are trained in the admixed group rather than in the ancestral groups. Common variant associations are also more likely to replicate if first discovered in the admixed group and then transferred to an ancestral population, than the other way around (across 50 iterations with 1,000 causal SNPs, training on 10,000 individuals, testing on 1,000 in each population, p = 3.78e-6, 6.19e-101, ∼0 for FST = 0.2, 0.5, 0.8, respectively). While some of these FST values may appear extreme, we demonstrate that they are found across the entire phenome in the GWAS catalog. This framework demonstrates that investigation of admixed populations harbors significant advantages over GWAS in single-ancestry cohorts for uncovering the genetic architecture of traits and will improve downstream applications such as personalized medicine across diverse populations.},\n\tauthor = {Lin, Meng and Park, Danny S and Zaitlen, Noah A and Henn, Brenna M and Gignoux, Christopher R},\n\tdate-added = {2022-01-11 18:35:50 -0500},\n\tdate-modified = {2022-01-11 18:35:50 -0500},\n\tdoi = {10.3389/fgene.2021.673167},\n\tjournal = {Front Genet},\n\tjournal-full = {Frontiers in genetics},\n\tkeywords = {admixture; complex trait genetics; genetic architecture; polygenic score; statistical power},\n\tpages = {673167},\n\tpmc = {PMC8181458},\n\tpmid = {34108994},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/34108994/},\tpst = {epublish},\n\ttitle = {Admixed Populations Improve Power for Variant Discovery and Portability in Genome-Wide Association Studies},\n\tvolume = {12},\n\tyear = {2021},\n\tbdsk-url-1 = {https://doi.org/10.3389/fgene.2021.673167}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Genome-wide association study of pancreatic fat: The Multiethnic Cohort Adiposity Phenotype Study.\n \n \n \n \n\n\n \n Streicher, S. A; Lim, U.; Park, S L.; Li, Y.; Sheng, X.; Hom, V.; Xia, L.; Pooler, L.; Shepherd, J.; Loo, L. W M; Darst, B. F; Highland, H. M; Polfus, L. M; Bogumil, D.; Ernst, T.; Buchthal, S.; Franke, A. A; Setiawan, V. W.; Tiirikainen, M.; Wilkens, L. R; Haiman, C. A; Stram, D. O; Cheng, I.; and Le Marchand, L.\n\n\n \n\n\n\n PLoS One, 16(7): e0249615. 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Genome-wide$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{Streicher:2021tu,\n\tabstract = {Several studies have found associations between higher pancreatic fat content and adverse health outcomes, such as diabetes and the metabolic syndrome, but investigations into the genetic contributions to pancreatic fat are limited. This genome-wide association study, comprised of 804 participants with MRI-assessed pancreatic fat measurements, was conducted in the ethnically diverse Multiethnic Cohort-Adiposity Phenotype Study (MEC-APS). Two genetic variants reaching genome-wide significance, rs73449607 on chromosome 13q21.2 (Beta = -0.67, P = 4.50x10-8) and rs7996760 on chromosome 6q14 (Beta = -0.90, P = 4.91x10-8) were associated with percent pancreatic fat on the log scale. Rs73449607 was most common in the African American population (13%) and rs79967607 was most common in the European American population (6%). Rs73449607 was also associated with lower risk of type 2 diabetes (OR = 0.95, 95% CI = 0.89-1.00, P = 0.047) in the Population Architecture Genomics and Epidemiology (PAGE) Study and the DIAbetes Genetics Replication and Meta-analysis (DIAGRAM), which included substantial numbers of non-European ancestry participants (53,102 cases and 193,679 controls). Rs73449607 is located in an intergenic region between GSX1 and PLUTO, and rs79967607 is in intron 1 of EPM2A. PLUTO, a lncRNA, regulates transcription of an adjacent gene, PDX1, that controls beta-cell function in the mature pancreas, and EPM2A encodes the protein laforin, which plays a critical role in regulating glycogen production. If validated, these variants may suggest a genetic component for pancreatic fat and a common etiologic link between pancreatic fat and type 2 diabetes.},\n\tauthor = {Streicher, Samantha A and Lim, Unhee and Park, S Lani and Li, Yuqing and Sheng, Xin and Hom, Victor and Xia, Lucy and Pooler, Loreall and Shepherd, John and Loo, Lenora W M and Darst, Burcu F and Highland, Heather M and Polfus, Linda M and Bogumil, David and Ernst, Thomas and Buchthal, Steven and Franke, Adrian A and Setiawan, Veronica Wendy and Tiirikainen, Maarit and Wilkens, Lynne R and Haiman, Christopher A and Stram, Daniel O and Cheng, Iona and Le Marchand, Lo{\\"\\i}c},\n\tdate-added = {2022-01-11 18:35:46 -0500},\n\tdate-modified = {2022-01-11 18:35:46 -0500},\n\tdoi = {10.1371/journal.pone.0249615},\n\tjournal = {PLoS One},\n\tjournal-full = {PloS one},\n\tmesh = {Adiposity; Aged; Chromosomes, Human, Pair 13; Chromosomes, Human, Pair 6; Diabetes Mellitus, Type 2; Ethnicity; Female; Genome-Wide Association Study; Humans; Magnetic Resonance Imaging; Male; Pancreas; Phenotype; Polymorphism, Single Nucleotide; Protein Tyrosine Phosphatases, Non-Receptor},\n\tnumber = {7},\n\tpages = {e0249615},\n\tpmc = {PMC8323875},\n\tpmid = {34329319},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/34329319/},\tpst = {epublish},\n\ttitle = {Genome-wide association study of pancreatic fat: The {Multiethnic Cohort Adiposity Phenotype Study}},\n\tvolume = {16},\n\tyear = {2021},\n\tbdsk-url-1 = {https://doi.org/10.1371/journal.pone.0249615}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Multi-ethnic GWAS and fine-mapping of glycaemic traits identify novel loci in the PAGE Study.\n \n \n \n \n\n\n \n Downie, C. G; Dimos, S. F; Bien, S. A; Hu, Y.; Darst, B. F; Polfus, L. M; Wang, Y.; Wojcik, G. L; Tao, R.; Raffield, L. M; Armstrong, N. D; Polikowsky, H. G; Below, J. E; Correa, A.; Irvin, M. R; Rasmussen-Torvik, L. J F; Carlson, C. S; Phillips, L. S; Liu, S.; Pankow, J. S; Rich, S. S; Rotter, J. I; Buyske, S.; Matise, T. C; North, K. E; Avery, C. L; Haiman, C. A; Loos, R. J F; Kooperberg, C.; Graff, M.; and Highland, H. M\n\n\n \n\n\n\n Diabetologia. Dec 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Multi-ethnic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@article{Downie:2021vd,\n\tabstract = {AIMS/HYPOTHESIS: Type 2 diabetes is a growing global public health challenge. Investigating quantitative traits, including fasting glucose, fasting insulin and HbA1c, that serve as early markers of type 2 diabetes progression may lead to a deeper understanding of the genetic aetiology of type 2 diabetes development. Previous genome-wide association studies (GWAS) have identified over 500 loci associated with type 2 diabetes, glycaemic traits and insulin-related traits. However, most of these findings were based only on populations of European ancestry. To address this research gap, we examined the genetic basis of fasting glucose, fasting insulin and HbA1c in participants of the diverse Population Architecture using Genomics and Epidemiology (PAGE) Study.\nMETHODS: We conducted a GWAS of fasting glucose (n = 52,267), fasting insulin (n = 48,395) and HbA1c (n = 23,357) in participants without diabetes from the diverse PAGE Study (23% self-reported African American, 46% Hispanic/Latino, 40% European, 4% Asian, 3% Native Hawaiian, 0.8% Native American), performing transethnic and population-specific GWAS meta-analyses, followed by fine-mapping to identify and characterise novel loci and independent secondary signals in known loci.\nRESULTS: Four novel associations were identified (p < 5 × 10-9), including three loci associated with fasting insulin, and a novel, low-frequency African American-specific locus associated with fasting glucose. Additionally, seven secondary signals were identified, including novel independent secondary signals for fasting glucose at the known GCK locus and for fasting insulin at the known PPP1R3B locus in transethnic meta-analysis.\nCONCLUSIONS/INTERPRETATION: Our findings provide new insights into the genetic architecture of glycaemic traits and highlight the continued importance of conducting genetic studies in diverse populations.\nDATA AVAILABILITY: Full summary statistics from each of the population-specific and transethnic results are available at NHGRI-EBI GWAS catalog ( https://www.ebi.ac.uk/gwas/downloads/summary-statistics ).},\n\tauthor = {Downie, Carolina G and Dimos, Sofia F and Bien, Stephanie A and Hu, Yao and Darst, Burcu F and Polfus, Linda M and Wang, Yujie and Wojcik, Genevieve L and Tao, Ran and Raffield, Laura M and Armstrong, Nicole D and Polikowsky, Hannah G and Below, Jennifer E and Correa, Adolfo and Irvin, Marguerite R and Rasmussen-Torvik, Laura J F and Carlson, Christopher S and Phillips, Lawrence S and Liu, Simin and Pankow, James S and Rich, Stephen S and Rotter, Jerome I and Buyske, Steven and Matise, Tara C and North, Kari E and Avery, Christy L and Haiman, Christopher A and Loos, Ruth J F and Kooperberg, Charles and Graff, Mariaelisa and Highland, Heather M},\n\tdate-added = {2022-01-11 18:35:25 -0500},\n\tdate-modified = {2022-01-11 18:35:25 -0500},\n\tdoi = {10.1007/s00125-021-05635-9},\n\tjournal = {Diabetologia},\n\tjournal-full = {Diabetologia},\n\tkeywords = {Fine-mapping; Genome-wide association study; Glucose; Glycaemic traits; HbA1c; Insulin; Transethnic population},\n\tmonth = {Dec},\n\tpmid = {34951656},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/34951656/},\n\tpst = {aheadofprint},\n\ttitle = {Multi-ethnic {GWAS} and fine-mapping of glycaemic traits identify novel loci in the {PAGE} Study},\n\tyear = {2021},\n\tbdsk-url-1 = {https://doi.org/10.1007/s00125-021-05635-9}}\n\n

\n\n\n

\n AIMS/HYPOTHESIS: Type 2 diabetes is a growing global public health challenge. Investigating quantitative traits, including fasting glucose, fasting insulin and HbA1c, that serve as early markers of type 2 diabetes progression may lead to a deeper understanding of the genetic aetiology of type 2 diabetes development. Previous genome-wide association studies (GWAS) have identified over 500 loci associated with type 2 diabetes, glycaemic traits and insulin-related traits. However, most of these findings were based only on populations of European ancestry. To address this research gap, we examined the genetic basis of fasting glucose, fasting insulin and HbA1c in participants of the diverse Population Architecture using Genomics and Epidemiology (PAGE) Study. METHODS: We conducted a GWAS of fasting glucose (n = 52,267), fasting insulin (n = 48,395) and HbA1c (n = 23,357) in participants without diabetes from the diverse PAGE Study (23% self-reported African American, 46% Hispanic/Latino, 40% European, 4% Asian, 3% Native Hawaiian, 0.8% Native American), performing transethnic and population-specific GWAS meta-analyses, followed by fine-mapping to identify and characterise novel loci and independent secondary signals in known loci. RESULTS: Four novel associations were identified (p < 5 × 10-9), including three loci associated with fasting insulin, and a novel, low-frequency African American-specific locus associated with fasting glucose. Additionally, seven secondary signals were identified, including novel independent secondary signals for fasting glucose at the known GCK locus and for fasting insulin at the known PPP1R3B locus in transethnic meta-analysis. CONCLUSIONS/INTERPRETATION: Our findings provide new insights into the genetic architecture of glycaemic traits and highlight the continued importance of conducting genetic studies in diverse populations. DATA AVAILABILITY: Full summary statistics from each of the population-specific and transethnic results are available at NHGRI-EBI GWAS catalog ( https://www.ebi.ac.uk/gwas/downloads/summary-statistics ).\n

\n\n\n

\n \n\n \n \n \n \n \n \n Multi-ethnic genome-wide association analyses of white blood cell and platelet traits in the Population Architecture using Genomics and Epidemiology (PAGE) study.\n \n \n \n \n\n\n \n Hu, Y.; Bien, S. A.; Nishimura, K. K.; Haessler, J.; Hodonsky, C. J.; Baldassari, A. R.; Highland, H. M.; Wang, Z.; Preuss, M.; Sitlani, C. M.; Wojcik, G. L.; Tao, R.; Graff, M.; Huckins, L. M.; Sun, Q.; Chen, M.; Mousas, A.; Auer, P. L.; Lettre, G.; the Blood Cell Consortium; Tang, W.; Qi, L.; Thyagarajan, B.; Buyske, S.; Fornage, M.; Hindorff, L. A.; Li, Y.; Lin, D.; Reiner, A. P.; North, K. E.; Loos, R. J. F.; Raffield, L. M.; Peters, U.; Avery, C. L.; and Kooperberg, C.\n\n\n \n\n\n\n BMC Genomics, 22(1). Jun 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Multi-ethnic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 15 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@article{Hu2021,\n\tabstract = {\n  Background: Circulating white blood cell and platelet traits are clinically linked to various disease outcomes and differ across individuals and ancestry groups. Genetic factors play an important role in determining these traits and many loci have been identified. However, most of these findings were identified in populations of European ancestry (EA), with African Americans (AA), Hispanics/Latinos (HL), and other races/ethnicities being severely underrepresented.\n  Results: We performed ancestry-combined and ancestry-specific genome-wide association studies (GWAS) for white blood cell and platelet traits in the ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) Study, including 16,201 AA, 21,347 HL, and 27,236 EA participants. We identified six novel findings at suggestive significance (P < 5E-8), which need confirmation, and independent signals at six previously established regions at genome-wide significance (P < 2E-9). We confirmed multiple previously reported genome-wide significant variants in the single variant association analysis and multiple genes using PrediXcan. Evaluation of loci reported from a Euro-centric GWAS indicated attenuation of effect estimates in AA and HL compared to EA populations.\n  Conclusions:Our results highlighted the potential to identify ancestry-specific and ancestry-agnostic variants in participants with diverse backgrounds and advocate for continued efforts in improving inclusion of racially/ethnically diverse populations in genetic association studies for complex traits.},\n\tauthor = {Yao Hu and Stephanie A. Bien and Katherine K. Nishimura and Jeffrey Haessler and Chani J. Hodonsky and Antoine R. Baldassari and Heather M. Highland and Zhe Wang and Michael Preuss and Colleen M. Sitlani and Genevieve L. Wojcik and Ran Tao and Mariaelisa Graff and Laura M. Huckins and Quan Sun and Ming-Huei Chen and Abdou Mousas and Paul L. Auer and Guillaume Lettre and {the Blood Cell Consortium} and Weihong Tang and Lihong Qi and Bharat Thyagarajan and Steve Buyske and Myriam Fornage and Lucia A. Hindorff and Yun Li and Danyu Lin and Alexander P. Reiner and Kari E. North and Ruth J. F. Loos and Laura M. Raffield and Ulrike Peters and Christy L. Avery and Charles Kooperberg},\n\tdoi = {10.1186/s12864-021-07745-5},\n\tjournal = {{BMC} Genomics},\n\tkeywords = {GWAS; Multi-ethnic; Platelets; White blood cells},\n\tmonth = {Jun},\n\tnumber = {1},\n\tpmc = {PMC8191001},\n\tpmid = {34107879},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/34107879/},\n\tpst = {epublish},\n\tpublisher = {Springer Science and Business Media {LLC}},\n\ttitle = {Multi-ethnic genome-wide association analyses of white blood cell and platelet traits in the {Population Architecture using Genomics and Epidemiology ({PAGE}) }study},\n\tvolume = {22},\n\tyear = {2021},\n\tbdsk-url-1 = {https://doi.org/10.1186/s12864-021-07745-5}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Genetic discovery and risk characterization in type 2 diabetes across diverse populations.\n \n \n \n \n\n\n \n Polfus, L. M.; Darst, B. F.; Highland, H.; Sheng, X.; Ng, M. C.; Below, J. E.; Petty, L.; Bien, S.; Sim, X.; Wang, W.; Fontanillas, P.; Patel, Y.; Preuss, M.; Schurmann, C.; Du, Z.; Lu, Y.; Rhie, S. K.; Mercader, J. M.; Tusie-Luna, T.; González-Villalpando, C.; Orozco, L.; Spracklen, C. N.; Cade, B. E.; Jensen, R. A.; Sun, M.; Joo, Y. Y.; An, P.; Yanek, L. R.; Bielak, L. F.; Tajuddin, S.; Nicolas, A.; Chen, G.; Raffield, L.; Guo, X.; Chen, W.; Nadkarni, G. N.; Graff, M.; Tao, R.; Pankow, J. S.; Daviglus, M.; Qi, Q.; Boerwinkle, E. A.; Liu, S.; Phillips, L. S.; Peters, U.; Carlson, C.; Wikens, L. R.; Le Marchand, L.; North, K. E.; Buyske, S.; Kooperberg, C.; Loos, R. J.; Stram, D. O.; and Haiman, C. A.\n\n\n \n\n\n\n Human Genetics and Genomics Advances, 2(2): 100029. 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Genetic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 12 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n\n\n\n

@article{POLFUS2021100029,\n\tabstract = {Summary\nGenomic discovery and characterization of risk loci for type 2 diabetes (T2D) have been conducted primarily in individuals of European ancestry. We conducted a multiethnic genome-wide association study of T2D among 53,102 cases and 193,679 control subjects from African, Hispanic, Asian, Native Hawaiian, and European population groups in the Population Architecture Genomics and Epidemiology (PAGE) and Diabetes Genetics Replication and Meta-analysis (DIAGRAM) Consortia. In individuals of African ancestry, we discovered a risk variant in the TGFB1 gene (rs11466334, risk allele frequency (RAF) = 6.8%, odds ratio [OR] = 1.27, p = 2.06 × 10−8), which replicated in independent studies of African ancestry (p = 6.26 × 10−23). We identified a multiethnic risk variant in the BACE2 gene (rs13052926, RAF = 14.1%, OR = 1.08, p = 5.75 × 10−9), which also replicated in independent studies (p = 3.45 × 10−4). We also observed a significant difference in the performance of a multiethnic genetic risk score (GRS) across population groups (pheterogeneity = 3.85 × 10−20). Comparing individuals in the top GRS risk category (40%--60%), the OR was highest in Asians (OR = 3.08) and European (OR = 2.94) ancestry populations, followed by Hispanic (OR = 2.39), Native Hawaiian (OR = 2.02), and African ancestry (OR = 1.57) populations. These findings underscore the importance of genetic discovery and risk characterization in diverse populations and the urgent need to further increase representation of non-European ancestry individuals in genetics research to improve genetic-based risk prediction across populations.},\n\tauthor = {Linda M. Polfus and Burcu F. Darst and Heather Highland and Xin Sheng and Maggie C.Y. Ng and Jennifer E. Below and Lauren Petty and Stephanie Bien and Xueling Sim and Wei Wang and Pierre Fontanillas and Yesha Patel and Michael Preuss and Claudia Schurmann and Zhaohui Du and Yingchang Lu and Suhn K. Rhie and Joseph M. Mercader and Teresa Tusie-Luna and Clicerio Gonz{\\'a}lez-Villalpando and Lorena Orozco and Cassandra N. Spracklen and Brian E. Cade and Richard A. Jensen and Meng Sun and Yoonjung Yoonie Joo and Ping An and Lisa R. Yanek and Lawrence F. Bielak and Salman Tajuddin and Aude Nicolas and Guanjie Chen and Laura Raffield and Xiuqing Guo and Wei-Min Chen and Girish N. Nadkarni and Mariaelisa Graff and Ran Tao and James S. Pankow and Martha Daviglus and Qibin Qi and Eric A. Boerwinkle and Simin Liu and Lawrence S. Phillips and Ulrike Peters and Chris Carlson and Lynne R. Wikens and Loic {Le Marchand} and Kari E. North and Steven Buyske and Charles Kooperberg and Ruth J.F. Loos and Daniel O. Stram and Christopher A. Haiman},\n\tdoi = {https://doi.org/10.1016/j.xhgg.2021.100029},\n\tissn = {2666-2477},\n\tjournal = {Human Genetics and Genomics Advances},\n\tkeywords = {Type 2 Diabetes, Genetic Risk Score},\n\tnumber = {2},\n\tpages = {100029},\n\tpmid = {34604815},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/34604815/},\n\n\ttitle = {Genetic discovery and risk characterization in type 2 diabetes across diverse populations},\n\tvolume = {2},\n\tyear = {2021},\n\tbdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S2666247721000105},\n\tbdsk-url-2 = {https://doi.org/10.1016/j.xhgg.2021.100029}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Genome-wide association study identifying novel variant for fasting insulin and allelic heterogeneity in known glycemic loci in Chilean adolescents: The Santiago Longitudinal Study.\n \n \n \n \n\n\n \n Buchanan, V. L; Wang, Y.; Blanco, E.; Graff, M.; Albala, C.; Burrows, R.; Santos, J. L; Angel, B.; Lozoff, B.; Voruganti, V. S.; Guo, X.; Taylor, K. D; Chen, Y. I.; Yao, J.; Tan, J.; Downie, C.; Highland, H. M; Justice, A. E; Gahagan, S.; and North, K. E\n\n\n \n\n\n\n Pediatr Obes, 16(7): e12765. Jul 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Genome-wide$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@article{Buchanan:2021tt,\n\tabstract = {BACKGROUND: The genetic underpinnings of glycemic traits have been understudied in adolescent and Hispanic/Latino (H/L) populations in comparison to adults and populations of European ancestry.\nOBJECTIVE: To identify genetic factors underlying glycemic traits in an adolescent H/L population.\nMETHODS: We conducted a genome-wide association study ({GWAS}) of fasting glucose (FG) and fasting insulin (FI) in H/L adolescents from the Santiago Longitudinal Study.\nRESULTS: We identified one novel variant positioned in the CSMD1 gene on chromosome 8 (rs77465890, effect allele frequency = 0.10) that was associated with FI (β = -0.299, SE = 0.054, p = 2.72×10-8 ) and was only slightly attenuated after adjusting for body mass index z-scores (β = -0.252, SE = 0.047, p = 1.03×10-7 ). We demonstrated directionally consistent, but not statistically significant results in African and Hispanic adults of the Population Architecture Using Genomics and Epidemiology Consortium. We also identified secondary signals for two FG loci after conditioning on known variants, which demonstrate allelic heterogeneity in well-known glucose loci.\nCONCLUSION: Our results exemplify the importance of including populations with diverse ancestral origin and adolescent participants in {GWAS} of glycemic traits to uncover novel risk loci and expand our understanding of disease aetiology.},\n\tauthor = {Buchanan, Victoria L and Wang, Yujie and Blanco, Estela and Graff, Mariaelisa and Albala, Cecilia and Burrows, Raquel and Santos, Jos{\\'e} L and Angel, B{\\'a}rbara and Lozoff, Betsy and Voruganti, Venkata Saroja and Guo, Xiuqing and Taylor, Kent D and Chen, Yii-Der Ida and Yao, Jie and Tan, Jingyi and Downie, Carolina and Highland, Heather M and Justice, Anne E and Gahagan, Sheila and North, Kari E},\n\tdate-added = {2021-06-16 09:59:17 -0400},\n\tdate-modified = {2021-06-16 09:59:17 -0400},\n\tdoi = {10.1111/ijpo.12765},\n\tjournal = {Pediatr Obes},\n\tjournal-full = {Pediatric obesity},\n\tkeywords = {{GWAS}; adolescent; glucose; insulin},\n\tmonth = {Jul},\n\tnumber = {7},\n\tpages = {e12765},\n\tpmid = {33381925},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/33381925/},\n\tpst = {ppublish},\n\ttitle = {Genome-wide association study identifying novel variant for fasting insulin and allelic heterogeneity in known glycemic loci in {C}hilean adolescents: The {Santiago Longitudinal Study}},\n\tvolume = {16},\n\tyear = {2021},\n\tbdsk-url-1 = {https://doi.org/10.1111/ijpo.12765}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Rapid detection of identity-by-descent tracts for mega-scale datasets.\n \n \n \n \n\n\n \n Shemirani, R.; Belbin, G. M; Avery, C. L; Kenny, E. E; Gignoux, C. R; and Ambite, J. L.\n\n\n \n\n\n\n Nat Commun, 12(1): 3546. Jun 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Rapid$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{Shemirani:2021wk,\n\tabstract = {The ability to identify segments of genomes identical-by-descent (IBD) is a part of standard workflows in both statistical and population genetics. However, traditional methods for finding local IBD across all pairs of individuals scale poorly leading to a lack of adoption in very large-scale datasets. Here, we present iLASH, an algorithm based on similarity detection techniques that shows equal or improved accuracy in simulations compared to current leading methods and speeds up analysis by several orders of magnitude on genomic datasets, making IBD estimation tractable for millions of individuals. We apply iLASH to the PAGE dataset of ~52,000 multi-ethnic participants, including several founder populations with elevated IBD sharing, identifying IBD segments in ~3 minutes per chromosome compared to over 6 days for a state-of-the-art algorithm. iLASH enables efficient analysis of very large-scale datasets, as we demonstrate by computing IBD across the UK Biobank (~500,000 individuals), detecting 12.9 billion pairwise connections.},\n\tauthor = {Shemirani, Ruhollah and Belbin, Gillian M and Avery, Christy L and Kenny, Eimear E and Gignoux, Christopher R and Ambite, Jos{\\'e} Luis},\n\tdate-added = {2021-06-15 23:00:14 -0400},\n\tdate-modified = {2021-06-15 23:00:14 -0400},\n\tdoi = {10.1038/s41467-021-22910-w},\n\tjournal = {Nat Commun},\n\tjournal-full = {Nature communications},\n\tmonth = {Jun},\n\tnumber = {1},\n\tpages = {3546},\n\tpmid = {34112768},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/34112768/},\n\tpst = {epublish},\n\ttitle = {Rapid detection of identity-by-descent tracts for mega-scale datasets},\n\tvolume = {12},\n\tyear = {2021},\n\tbdsk-url-1 = {https://doi.org/10.1038/s41467-021-22910-w}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Comparison of adaptive multiple phenotype association tests using summary statistics in genome-wide association studies.\n \n \n \n \n\n\n \n Sitlani, C. M.; Baldassari, A. R.; Highland, H. M.; Hodonsky, C. J.; McKnight, B.; and Avery, C. L.\n\n\n \n\n\n\n Human molecular genetics. May 2021.\n \n\n\n\n
\n\n\n\n \n \n $\"Comparison$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{SitlaniBaldassariHighlandEtAl2021,\n\tabstract = {Genome-wide association studies have been successful mapping loci for individual phenotypes, but few studies have comprehensively interrogated evidence of shared genetic effects across multiple phenotypes simultaneously. Statistical methods have been proposed for analyzing multiple phenotypes using summary statistics, which enables studies of shared genetic effects while avoiding challenges associated with individual-level data sharing. Adaptive tests have been developed to maintain power against multiple alternative hypotheses because the most powerful single-alternative test depends on the underlying structure of the associations between the multiple phenotypes and a single nucleotide polymorphism ({SNP}). Here we compare the performance of six such adaptive tests: two adaptive sum of powered scores (aSPU) tests, the unified score association test (metaUSAT), the adaptive test in a mixed-models framework (mixAda), and two principal-component-based adaptive tests (PCAQ and PCO). Our simulations highlight practical challenges that arise when multivariate distributions of phenotypes do not satisfy assumptions of multivariate normality. Previous reports in this context focus on low minor allele count (MAC) and omit the aSPU test, which relies less than other methods on asymptotic and distributional assumptions. When these assumptions are not satisfied, particularly when MAC is low and/or phenotype covariance matrices are singular or nearly singular, aSPU better preserves type I error, sometimes at the cost of decreased power. We illustrate this tradeoff with multiple phenotype analyses of six quantitative electrocardiogram traits in the {Population Architecture using Genomics and Epidemiology} (PAGE) study.},\n\tauthor = {Sitlani, Colleen M. and Baldassari, Antoine R. and Highland, Heather M. and Hodonsky, Chani J. and McKnight, Barbara and Avery, Christy L.},\n\tcitation-subset = {IM},\n\tcountry = {England},\n\tdoi = {10.1093/hmg/ddab126},\n\tissn = {1460-2083},\n\tissn-linking = {0964-6906},\n\tjournal = {Human molecular genetics},\n\tmonth = may,\n\tnlm-id = {9208958},\n\towner = {NLM},\n\tpii = {ddab126},\n\tpmid = {33949650},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/33949650/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {aheadofprint},\n\trevised = {2021-05-05},\n\ttitle = {Comparison of adaptive multiple phenotype association tests using summary statistics in genome-wide association studies.},\n\tyear = {2021},\n\tbdsk-url-1 = {https://doi.org/10.1093/hmg/ddab126}}\n

\n\n\n\n\n\n

\n\n

\n \n 2020\n \n \n (5)\n \n \n

\n \n \n

\n \n\n \n \n \n \n \n \n Importance of Genetic Studies of Cardiometabolic Disease in Diverse Populations.\n \n \n \n \n\n\n \n Fernández-Rhodes, L.; Young, K. L; Lilly, A. G; Raffield, L. M; Highland, H. M; Wojcik, G. L; Agler, C.; Love, S. M; Okello, S.; Petty, L. E; Graff, M.; Below, J. E; Divaris, K.; and North, K. E\n\n\n \n\n\n\n Circ Res, 126(12): 1816-1840. Jun 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Importance$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@article{Fernandez-Rhodes:2020te,\n\tabstract = {Genome-wide association studies have revolutionized our understanding of the genetic underpinnings of cardiometabolic disease. Yet, the inadequate representation of individuals of diverse ancestral backgrounds in these studies may undercut their ultimate potential for both public health and precision medicine. The goal of this review is to describe the imperativeness of studying the populations who are most affected by cardiometabolic disease, to the aim of better understanding the genetic underpinnings of the disease. We support this premise by describing the current variation in the global burden of cardiometabolic disease and emphasize the importance of building a globally and ancestrally representative genetics evidence base for the identification of population-specific variants, fine-mapping, and polygenic risk score estimation. We discuss the important ethical, legal, and social implications of increasing ancestral diversity in genetic studies of cardiometabolic disease and the challenges that arise from the (1) lack of diversity in current reference populations and available analytic samples and the (2) unequal generation of health-associated genomic data and their prediction accuracies. Despite these challenges, we conclude that additional, unprecedented opportunities lie ahead for public health genomics and the realization of precision medicine, provided that the gap in diversity can be systematically addressed. Achieving this goal will require concerted efforts by social, academic, professional and regulatory stakeholders and communities, and these efforts must be based on principles of equity and social justice.},\n\tauthor = {Fern{\\'a}ndez-Rhodes, Lindsay and Young, Kristin L and Lilly, Adam G and Raffield, Laura M and Highland, Heather M and Wojcik, Genevieve L and Agler, Cary and Love, Shelly-Ann M and Okello, Samson and Petty, Lauren E and Graff, Mariaelisa and Below, Jennifer E and Divaris, Kimon and North, Kari E},\n\tdate-added = {2021-06-15 23:03:58 -0400},\n\tdate-modified = {2021-06-15 23:03:58 -0400},\n\tdoi = {10.1161/CIRCRESAHA.120.315893},\n\tjournal = {Circ Res},\n\tjournal-full = {Circulation research},\n\tkeywords = {cardiovascular diseases; genomics; global burden of disease; metabolic diseases; minority health; precision medicine; social justice},\n\tmesh = {Gene Frequency; Genome-Wide Association Study; Humans; Metabolic Syndrome; Polymorphism, Genetic},\n\tmonth = {Jun},\n\tnumber = {12},\n\tpages = {1816-1840},\n\tpmc = {PMC7285892},\n\tpmid = {32496918},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/32496918/},\n\tpst = {ppublish},\n\ttitle = {Importance of Genetic Studies of Cardiometabolic Disease in Diverse Populations},\n\tvolume = {126},\n\tyear = {2020},\n\tbdsk-url-1 = {https://doi.org/10.1161/CIRCRESAHA.120.315893}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Minority-centric meta-analyses of blood lipid levels identify novel loci in the Population Architecture using Genomics and Epidemiology (PAGE) study.\n \n \n \n \n\n\n \n Hu, Y.; Graff, M.; Haessler, J.; Buyske, S.; Bien, S. A; Tao, R.; Highland, H. M; Nishimura, K. K; Zubair, N.; Lu, Y.; Verbanck, M.; Hilliard, A. T; Klarin, D.; Damrauer, S. M; Ho, Y.; VA Million Veteran Program; Wilson, P. W F; Chang, K.; Tsao, P. S; Cho, K.; O'Donnell, C. J; Assimes, T. L; Petty, L. E; Below, J. E; Dikilitas, O.; Schaid, D. J; Kosel, M. L; Kullo, I. J; Rasmussen-Torvik, L. J; Jarvik, G. P; Feng, Q.; Wei, W.; Larson, E. B; Mentch, F. D; Almoguera, B.; Sleiman, P. M; Raffield, L. M; Correa, A.; Martin, L. W; Daviglus, M.; Matise, T. C; Ambite, J. L.; Carlson, C. S; Do, R.; Loos, R. J F; Wilkens, L. R; Le Marchand, L.; Haiman, C.; Stram, D. O; Hindorff, L. A; North, K. E; Kooperberg, C.; Cheng, I.; and Peters, U.\n\n\n \n\n\n\n PLoS Genet, 16(3): e1008684. Mar 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Minority-centric$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{Hu:2020uj,\n\tabstract = {Lipid levels are important markers for the development of cardio-metabolic diseases. Although hundreds of associated loci have been identified through genetic association studies, the contribution of genetic factors to variation in lipids is not fully understood, particularly in U.S. minority groups. We performed genome-wide association analyses for four lipid traits in over 45,000 ancestrally diverse participants from the {Population Architecture using Genomics and Epidemiology} (PAGE) Study, followed by a meta-analysis with several European ancestry studies. We identified nine novel lipid loci, five of which showed evidence of replication in independent studies. Furthermore, we discovered one novel gene in a PrediXcan analysis, minority-specific independent signals at eight previously reported loci, and potential functional variants at two known loci through fine-mapping. Systematic examination of known lipid loci revealed smaller effect estimates in African American and Hispanic ancestry populations than those in Europeans, and better performance of polygenic risk scores based on minority-specific effect estimates. Our findings provide new insight into the genetic architecture of lipid traits and highlight the importance of conducting genetic studies in diverse populations in the era of precision medicine.},\n\tauthor = {Hu, Yao and Graff, Mariaelisa and Haessler, Jeffrey and Buyske, Steven and Bien, Stephanie A and Tao, Ran and Highland, Heather M and Nishimura, Katherine K and Zubair, Niha and Lu, Yingchang and Verbanck, Marie and Hilliard, Austin T and Klarin, Derek and Damrauer, Scott M and Ho, Yuk-Lam and {VA Million Veteran Program} and Wilson, Peter W F and Chang, Kyong-Mi and Tsao, Philip S and Cho, Kelly and O'Donnell, Christopher J and Assimes, Themistocles L and Petty, Lauren E and Below, Jennifer E and Dikilitas, Ozan and Schaid, Daniel J and Kosel, Matthew L and Kullo, Iftikhar J and Rasmussen-Torvik, Laura J and Jarvik, Gail P and Feng, Qiping and Wei, Wei-Qi and Larson, Eric B and Mentch, Frank D and Almoguera, Berta and Sleiman, Patrick M and Raffield, Laura M and Correa, Adolfo and Martin, Lisa W and Daviglus, Martha and Matise, Tara C and Ambite, Jose Luis and Carlson, Christopher S and Do, Ron and Loos, Ruth J F and Wilkens, Lynne R and Le Marchand, Loic and Haiman, Chris and Stram, Daniel O and Hindorff, Lucia A and North, Kari E and Kooperberg, Charles and Cheng, Iona and Peters, Ulrike},\n\tdate-added = {2021-06-15 23:01:04 -0400},\n\tdate-modified = {2021-06-15 23:01:04 -0400},\n\tdoi = {10.1371/journal.pgen.1008684},\n\tjournal = {PLoS Genet},\n\tjournal-full = {PLoS genetics},\n\tmesh = {Continental Population Groups; Databases, Genetic; Female; Genome-Wide Association Study; Genotype; Humans; Lipids; Male; Metagenomics; Minority Groups; Multifactorial Inheritance; Phenotype; Polymorphism, Single Nucleotide; United States},\n\tmonth = {Mar},\n\tnumber = {3},\n\tpages = {e1008684},\n\tpmc = {PMC7145272},\n\tpmid = {32226016},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/32226016/},\n\tpst = {epublish},\n\ttitle = {Minority-centric meta-analyses of blood lipid levels identify novel loci in the {Population Architecture using Genomics and Epidemiology} ({PAGE}) study},\n\tvolume = {16},\n\tyear = {2020},\n\tbdsk-url-1 = {https://doi.org/10.1371/journal.pgen.1008684}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n On the cross-population generalizability of gene expression prediction models.\n \n \n \n \n\n\n \n Keys, K. L; Mak, A. C Y; White, M. J; Eckalbar, W. L; Dahl, A. W; Mefford, J.; Mikhaylova, A. V; Contreras, M.; Elhawary, J. R; Eng, C.; Hu, D.; Huntsman, S.; Oh, S. S; Salazar, S.; Lenoir, M. A; Ye, J. C; Thornton, T. A; Zaitlen, N.; Burchard, E. G; and Gignoux, C. R\n\n\n \n\n\n\n PLoS Genet, 16(8): e1008927. Aug 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"On$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{Keys:2020wg,\n\tabstract = {The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression prediction.},\n\tauthor = {Keys, Kevin L and Mak, Angel C Y and White, Marquitta J and Eckalbar, Walter L and Dahl, Andrew W and Mefford, Joel and Mikhaylova, Anna V and Contreras, Mar{\\'\\i}a G and Elhawary, Jennifer R and Eng, Celeste and Hu, Donglei and Huntsman, Scott and Oh, Sam S and Salazar, Sandra and Lenoir, Michael A and Ye, Jimmie C and Thornton, Timothy A and Zaitlen, Noah and Burchard, Esteban G and Gignoux, Christopher R},\n\tdate-added = {2021-06-15 23:00:41 -0400},\n\tdate-modified = {2021-06-15 23:00:41 -0400},\n\tdoi = {10.1371/journal.pgen.1008927},\n\tjournal = {PLoS Genet},\n\tjournal-full = {PLoS genetics},\n\tmesh = {African Americans; Gene Expression Profiling; Genome-Wide Association Study; Humans; Models, Genetic; Quantitative Trait Loci; RNA-Seq; Reference Standards; Transcriptome},\n\tmonth = {Aug},\n\tnumber = {8},\n\tpages = {e1008927},\n\tpmc = {PMC7449671},\n\tpmid = {32797036},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/32797036/},\n\tpst = {epublish},\n\ttitle = {On the cross-population generalizability of gene expression prediction models},\n\tvolume = {16},\n\tyear = {2020},\n\tbdsk-url-1 = {https://doi.org/10.1371/journal.pgen.1008927}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Ancestry-specific associations identified in genome-wide combined-phenotype study of red blood cell traits emphasize benefits of diversity in genomics.\n \n \n \n \n\n\n \n Hodonsky, C. J.; Baldassari, A. R.; Bien, S. A.; Raffield, L. M.; Highland, H. M.; Sitlani, C. M.; Wojcik, G. L.; Tao, R.; Graff, M.; Tang, W.; Thyagarajan, B.; Buyske, S.; Fornage, M.; Hindorff, L. A.; Li, Y.; Lin, D.; Reiner, A. P.; North, K. E.; Loos, R. J. F.; Kooperberg, C.; and Avery, C. L.\n\n\n \n\n\n\n BMC genomics, 21: 228. March 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Ancestry-specific$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 8 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@article{HodonskyBaldassariBienEtAl2020,\n\tabstract = {Quantitative red blood cell (RBC) traits are highly polygenic clinically relevant traits, with approximately 500 reported {GWAS} loci. The majority of RBC trait {GWAS} have been performed in European- or East Asian-ancestry populations, despite evidence that rare or ancestry-specific variation contributes substantially to RBC trait heritability. Recently developed combined-phenotype methods which leverage genetic trait correlation to improve statistical power have not yet been applied to these traits. Here we leveraged correlation of seven quantitative RBC traits in performing a combined-phenotype analysis in a multi-ethnic study population. We used the adaptive sum of powered scores (aSPU) test to assess combined-phenotype associations between ~ 21 million SNPs and seven RBC traits in a multi-ethnic population (maximum n = 67,885 participants; 24% African American, 30% Hispanic/Latino, and 43% European American; 76% female). Thirty-nine loci in our multi-ethnic population contained at least one significant association signal (p < 5E-9), with lead SNPs at nine loci significantly associated with three or more RBC traits. A majority of the lead SNPs were common (MAF > 5%) across all ancestral populations. Nineteen additional independent association signals were identified at seven known loci (HFE, KIT, HBS1L/MYB, CITED2/FILNC1, ABO, HBA1/2, and PLIN4/5). For example, the HBA1/2 locus contained 14 conditionally independent association signals, 11 of which were previously unreported and are specific to African and Amerindian ancestries. One variant in this region was common in all ancestries, but exhibited a narrower LD block in African Americans than European Americans or Hispanics/Latinos. GTEx eQTL analysis of all independent lead SNPs yielded 31 significant associations in relevant tissues, over half of which were not at the gene immediately proximal to the lead {SNP}. This work identified seven loci containing multiple independent association signals for RBC traits using a combined-phenotype approach, which may improve discovery in genetically correlated traits. Highly complex genetic architecture at the HBA1/2 locus was only revealed by the inclusion of African Americans and Hispanics/Latinos, underscoring the continued importance of expanding large {GWAS} to include ancestrally diverse populations.},\n\tauthor = {Hodonsky, Chani J. and Baldassari, Antoine R. and Bien, Stephanie A. and Raffield, Laura M. and Highland, Heather M. and Sitlani, Colleen M. and Wojcik, Genevieve L. and Tao, Ran and Graff, Marielisa and Tang, Weihong and Thyagarajan, Bharat and Buyske, Steve and Fornage, Myriam and Hindorff, Lucia A. and Li, Yun and Lin, Danyu and Reiner, Alex P. and North, Kari E. and Loos, Ruth J. F. and Kooperberg, Charles and Avery, Christy L.},\n\tcitation-subset = {IM},\n\tcountry = {England},\n\tdoi = {10.1186/s12864-020-6626-9},\n\tissn = {1471-2164},\n\tissn-linking = {1471-2164},\n\tissue = {1},\n\tjournal = {BMC genomics},\n\tkeywords = {Blood cell traits; Combined-phenotype analysis; Diversity; {GWAS}; Multi-ethnic; Pleiotropy},\n\tmonth = mar,\n\tnlm-id = {100965258},\n\towner = {NLM},\n\tpages = {228},\n\tpii = {10.1186/s12864-020-6626-9},\n\tpmc = {PMC7071748},\n\tpmid = {32171239},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/32171239/},\n\n\tpubmodel = {Electronic},\n\tpubstate = {epublish},\n\trevised = {2020-09-04},\n\ttitle = {Ancestry-specific associations identified in genome-wide combined-phenotype study of red blood cell traits emphasize benefits of diversity in genomics.},\n\tvolume = {21},\n\tyear = {2020},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/32171239/},\n\tbdsk-url-2 = {https://doi.org/10.1186/s12864-020-6626-9}}\n\n

\n\n\n

\n Quantitative red blood cell (RBC) traits are highly polygenic clinically relevant traits, with approximately 500 reported GWAS loci. The majority of RBC trait GWAS have been performed in European- or East Asian-ancestry populations, despite evidence that rare or ancestry-specific variation contributes substantially to RBC trait heritability. Recently developed combined-phenotype methods which leverage genetic trait correlation to improve statistical power have not yet been applied to these traits. Here we leveraged correlation of seven quantitative RBC traits in performing a combined-phenotype analysis in a multi-ethnic study population. We used the adaptive sum of powered scores (aSPU) test to assess combined-phenotype associations between 21 million SNPs and seven RBC traits in a multi-ethnic population (maximum n = 67,885 participants; 24% African American, 30% Hispanic/Latino, and 43% European American; 76% female). Thirty-nine loci in our multi-ethnic population contained at least one significant association signal (p < 5E-9), with lead SNPs at nine loci significantly associated with three or more RBC traits. A majority of the lead SNPs were common (MAF > 5%) across all ancestral populations. Nineteen additional independent association signals were identified at seven known loci (HFE, KIT, HBS1L/MYB, CITED2/FILNC1, ABO, HBA1/2, and PLIN4/5). For example, the HBA1/2 locus contained 14 conditionally independent association signals, 11 of which were previously unreported and are specific to African and Amerindian ancestries. One variant in this region was common in all ancestries, but exhibited a narrower LD block in African Americans than European Americans or Hispanics/Latinos. GTEx eQTL analysis of all independent lead SNPs yielded 31 significant associations in relevant tissues, over half of which were not at the gene immediately proximal to the lead SNP. This work identified seven loci containing multiple independent association signals for RBC traits using a combined-phenotype approach, which may improve discovery in genetically correlated traits. Highly complex genetic architecture at the HBA1/2 locus was only revealed by the inclusion of African Americans and Hispanics/Latinos, underscoring the continued importance of expanding large GWAS to include ancestrally diverse populations.\n

\n\n\n

\n \n\n \n \n \n \n \n \n Multi-Ethnic Genome-Wide Association Study of Decomposed Cardioelectric Phenotypes Illustrates Strategies to Identify and Characterize Evidence of Shared Genetic Effects for Complex Traits.\n \n \n \n \n\n\n \n Baldassari, A. R.; Sitlani, C. M.; Highland, H. M.; Arking, D. E.; Buyske, S.; Darbar, D.; Gondalia, R.; Graff, M.; Guo, X.; Heckbert, S. R.; Hindorff, L. A.; Hodonsky, C. J.; Ida Chen, Y.; Kaplan, R. C.; Peters, U.; Post, W.; Reiner, A. P.; Rotter, J. I.; Shohet, R. V.; Seyerle, A. A.; Sotoodehnia, N.; Tao, R.; Taylor, K. D.; Wojcik, G. L.; Yao, J.; Kenny, E. E.; Lin, H. J.; Soliman, E. Z.; Whitsel, E. A.; North, K. E.; Kooperberg, C.; and Avery, C. L.\n\n\n \n\n\n\n Circulation. Genomic and precision medicine, 13: e002680. August 2020.\n \n\n\n\n
\n\n\n\n \n \n $\"Multi-Ethnic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@article{BaldassariSitlaniHighlandEtAl2020,\n\tabstract = {We examined how expanding electrocardiographic trait genome-wide association studies to include ancestrally diverse populations, prioritize more precise phenotypic measures, and evaluate evidence for shared genetic effects enabled the detection and characterization of loci. We decomposed 10 seconds, 12-lead electrocardiograms from 34 668 multi-ethnic participants (15% Black; 30% Hispanic/Latino) into 6 contiguous, physiologically distinct (P wave, PR segment, QRS interval, ST segment, T wave, and TP segment) and 2 composite, conventional (PR interval and {QT} interval) interval scale traits and conducted multivariable-adjusted, trait-specific univariate genome-wide association studies using 1000-G imputed single-nucleotide polymorphisms. Evidence of shared genetic effects was evaluated by aggregating meta-analyzed univariate results across the 6 continuous electrocardiographic traits using the combined phenotype adaptive sum of powered scores test. We identified 6 novels ( , and  ) and 87 known loci (adaptive sum of powered score test  <5×10 ). Lead single-nucleotide polymorphism rs3211938 at   was common in Blacks (minor allele frequency=10%), near monomorphic in European Americans, and had effects on the {QT} interval and TP segment that ranked among the largest reported to date for common variants. The other 5 novel loci were observed when evaluating the contiguous but not the composite electrocardiographic traits. Combined phenotype testing did not identify novel electrocardiographic loci unapparent using traditional univariate approaches, although this approach did assist with the characterization of known loci. Despite including one-third as many participants as published electrocardiographic trait genome-wide association studies, our study identified 6 novel loci, emphasizing the importance of ancestral diversity and phenotype resolution in this era of ever-growing genome-wide association studies.},\n\tauthor = {Baldassari, Antoine R. and Sitlani, Colleen M. and Highland, Heather M. and Arking, Dan E. and Buyske, Steve and Darbar, Dawood and Gondalia, Rahul and Graff, Misa and Guo, Xiuqing and Heckbert, Susan R. and Hindorff, Lucia A. and Hodonsky, Chani J. and Ida Chen, Yii-Der and Kaplan, Robert C. and Peters, Ulrike and Post, Wendy and Reiner, Alex P. and Rotter, Jerome I. and Shohet, Ralph V. and Seyerle, Amanda A. and Sotoodehnia, Nona and Tao, Ran and Taylor, Kent D. and Wojcik, Genevieve L. and Yao, Jie and Kenny, Eimear E. and Lin, Henry J. and Soliman, Elsayed Z. and Whitsel, Eric A. and North, Kari E. and Kooperberg, Charles and Avery, Christy L.},\n\tcitation-subset = {IM},\n\tcountry = {United States},\n\tdoi = {10.1161/CIRCGEN.119.002680},\n\tissn = {2574-8300},\n\tissn-linking = {2574-8300},\n\tissue = {4},\n\tjournal = {Circulation. Genomic and precision medicine},\n\tkeywords = {cardiovascular diseases; electrophysiology; epidemiology; genome-wide association study; population},\n\tmid = {NIHMS1612182},\n\tmonth = aug,\n\tnlm-id = {101714113},\n\towner = {NLM},\n\tpages = {e002680},\n\tpmc = {PMC7520945},\n\tpmid = {32602732},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/32602732/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2020-09-30},\n\ttitle = {Multi-Ethnic Genome-Wide Association Study of Decomposed Cardioelectric Phenotypes Illustrates Strategies to Identify and Characterize Evidence of Shared Genetic Effects for Complex Traits.},\n\tvolume = {13},\n\tyear = {2020},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/32602732/},\n\tbdsk-url-2 = {https://doi.org/10.1161/CIRCGEN.119.002680}}\n\n

\n\n\n\n\n\n

\n\n

\n \n 2019\n \n \n (5)\n \n \n

\n \n \n

\n \n\n \n \n \n \n \n \n Use of $>$100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations.\n \n \n \n \n\n\n \n Kowalski, M. H; Qian, H.; Hou, Z.; Rosen, J. D; Tapia, A. L; Shan, Y.; Jain, D.; Argos, M.; Arnett, D. K; Avery, C.; Barnes, K. C; Becker, L. C; Bien, S. A; Bis, J. C; Blangero, J.; Boerwinkle, E.; Bowden, D. W; Buyske, S.; Cai, J.; Cho, M. H; Choi, S. H.; Choquet, H.; Cupples, L A.; Cushman, M.; Daya, M.; de Vries, P. S; Ellinor, P. T; Faraday, N.; Fornage, M.; Gabriel, S.; Ganesh, S. K; Graff, M.; Gupta, N.; He, J.; Heckbert, S. R; Hidalgo, B.; Hodonsky, C. J; Irvin, M. R; Johnson, A. D; Jorgenson, E.; Kaplan, R.; Kardia, S. L R; Kelly, T. N; Kooperberg, C.; Lasky-Su, J. A; Loos, R. J F; Lubitz, S. A; Mathias, R. A; McHugh, C. P; Montgomery, C.; Moon, J.; Morrison, A. C; Palmer, N. D; Pankratz, N.; Papanicolaou, G. J; Peralta, J. M; Peyser, P. A; Rich, S. S; Rotter, J. I; Silverman, E. K; Smith, J. A; Smith, N. L; Taylor, K. D; Thornton, T. A; Tiwari, H. K; Tracy, R. P; Wang, T.; Weiss, S. T; Weng, L.; Wiggins, K. L; Wilson, J. G; Yanek, L. R; Zöllner, S.; North, K. E; Auer, P. L; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; TOPMed Hematology & Hemostasis Working Group; Raffield, L. M; Reiner, A. P; and Li, Y.\n\n\n \n\n\n\n PLoS Genet, 15(12): e1008500. Dec 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Use$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{Kowalski:2019vu,\n\tabstract = {Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11-34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count <10 (including singletons) in the imputation target samples, average information content rescued was >86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations.},\n\tauthor = {Kowalski, Madeline H and Qian, Huijun and Hou, Ziyi and Rosen, Jonathan D and Tapia, Amanda L and Shan, Yue and Jain, Deepti and Argos, Maria and Arnett, Donna K and Avery, Christy and Barnes, Kathleen C and Becker, Lewis C and Bien, Stephanie A and Bis, Joshua C and Blangero, John and Boerwinkle, Eric and Bowden, Donald W and Buyske, Steve and Cai, Jianwen and Cho, Michael H and Choi, Seung Hoan and Choquet, H{\\'e}l{\\`e}ne and Cupples, L Adrienne and Cushman, Mary and Daya, Michelle and de Vries, Paul S and Ellinor, Patrick T and Faraday, Nauder and Fornage, Myriam and Gabriel, Stacey and Ganesh, Santhi K and Graff, Misa and Gupta, Namrata and He, Jiang and Heckbert, Susan R and Hidalgo, Bertha and Hodonsky, Chani J and Irvin, Marguerite R and Johnson, Andrew D and Jorgenson, Eric and Kaplan, Robert and Kardia, Sharon L R and Kelly, Tanika N and Kooperberg, Charles and Lasky-Su, Jessica A and Loos, Ruth J F and Lubitz, Steven A and Mathias, Rasika A and McHugh, Caitlin P and Montgomery, Courtney and Moon, Jee-Young and Morrison, Alanna C and Palmer, Nicholette D and Pankratz, Nathan and Papanicolaou, George J and Peralta, Juan M and Peyser, Patricia A and Rich, Stephen S and Rotter, Jerome I and Silverman, Edwin K and Smith, Jennifer A and Smith, Nicholas L and Taylor, Kent D and Thornton, Timothy A and Tiwari, Hemant K and Tracy, Russell P and Wang, Tao and Weiss, Scott T and Weng, Lu-Chen and Wiggins, Kerri L and Wilson, James G and Yanek, Lisa R and Z{\\"o}llner, Sebastian and North, Kari E and Auer, Paul L and {NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium} and {TOPMed Hematology \\& Hemostasis Working Group} and Raffield, Laura M and Reiner, Alexander P and Li, Yun},\n\tdate-added = {2021-06-16 13:37:42 -0400},\n\tdate-modified = {2021-06-16 13:37:42 -0400},\n\tdoi = {10.1371/journal.pgen.1008500},\n\tjournal = {PLoS Genet},\n\tjournal-full = {PLoS genetics},\n\tmesh = {Adult; African Americans; Aged; Aged, 80 and over; Computational Biology; Databases, Genetic; Female; Gene Frequency; Genetic Predisposition to Disease; Genetics, Population; Genome-Wide Association Study; Genotyping Techniques; Hispanic Americans; Humans; Linkage Disequilibrium; Male; Middle Aged; Precision Medicine; United States; Whole Genome Sequencing; beta-Globins},\n\tmonth = {Dec},\n\tnumber = {12},\n\tpages = {e1008500},\n\tpmc = {PMC6953885},\n\tpmid = {31869403},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/31869403/},\n\tpst = {epublish},\n\ttitle = {Use of $>$100,000 {NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium} whole genome sequences improves imputation quality and detection of rare variant associations in admixed {African} and {Hispanic/Latino} populations},\n\tvolume = {15},\n\tyear = {2019},\n\tbdsk-url-1 = {https://doi.org/10.1371/journal.pgen.1008500}}\n\n

\n\n\n

\n Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11-34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count <10 (including singletons) in the imputation target samples, average information content rescued was >86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in 21,600 African-ancestry and 21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations.\n

\n\n\n

\n \n\n \n \n \n \n \n \n The Future of Genomic Studies Must Be Globally Representative: Perspectives from PAGE.\n \n \n \n \n\n\n \n Bien, S. A.; Wojcik, G. L.; Hodonsky, C. J.; Gignoux, C. R.; Cheng, I.; Matise, T. C.; Peters, U.; Kenny, E. E.; and North, K. E.\n\n\n \n\n\n\n Annual review of genomics and human genetics, 20: 181–200. Aug 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{BienWojcikHodonskyEtAl2019,\n\tabstract = {The past decade has seen a technological revolution in human genetics that has empowered population-level investigations into genetic associations with phenotypes. Although these discoveries rely on genetic variation across individuals, association studies have overwhelmingly been performed in populations of European descent. In this review, we describe limitations faced by single-population studies and provide an overview of strategies to improve global representation in existing data sets and future human genomics research via diversity-focused, multiethnic studies. We highlight the successes of individual studies and meta-analysis consortia that have provided unique knowledge. Additionally, we outline the approach taken by the Population Architecture Using Genomics and Epidemiology (PAGE) study to develop best practices for performing genetic epidemiology in multiethnic contexts. Finally, we discuss how limiting investigations to single populations impairs findings in the clinical domain for both rare-variant identification and genetic risk prediction.},\n\tauthor = {Bien, Stephanie A. and Wojcik, Genevieve L. and Hodonsky, Chani J. and Gignoux, Christopher R. and Cheng, Iona and Matise, Tara C. and Peters, Ulrike and Kenny, Eimear E. and North, Kari E.},\n\tcitation-subset = {IM},\n\tcompleted = {2020-05-08},\n\tcountry = {United States},\n\tdoi = {10.1146/annurev-genom-091416-035517},\n\tissn = {1545-293X},\n\tissn-linking = {1527-8204},\n\tjournal = {Annual review of genomics and human genetics},\n\tkeywords = {Bias; Continental Population Groups, genetics; Databases, Factual; Ethnic Groups, genetics; Genetic Variation; Genome, Human; Genome-Wide Association Study; Genotype; Human Genetics, trends; Humans; Metagenomics, trends; Molecular Epidemiology, trends; Phenotype; PAGE; diversity; fine mapping; genomics; multiethnic; transethnic},\n\tmid = {NIHMS1064810},\n\tmonth = {Aug},\n\tnlm-id = {100911346},\n\towner = {NLM},\n\tpages = {181--200},\n\tpmc = {PMC7012212},\n\tpmid = {30978304},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/30978304/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2020-09-01},\n\ttitle = {The Future of Genomic Studies Must Be Globally Representative: Perspectives from {PAGE}.},\n\tvolume = {20},\n\tyear = {2019},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/30978304/},\n\tbdsk-url-2 = {https://doi.org/10.1146/annurev-genom-091416-035517}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n A phenome-wide association study (PheWAS) in the Population Architecture using Genomics and Epidemiology (PAGE) study reveals potential pleiotropy in African Americans.\n \n \n \n \n\n\n \n Pendergrass, S. A.; Buyske, S.; Jeff, J. M.; Frase, A.; Dudek, S.; Bradford, Y.; Ambite, J.; Avery, C. L.; Buzkova, P.; Deelman, E.; Fesinmeyer, M. D.; Haiman, C.; Heiss, G.; Hindorff, L. A.; Hsu, C.; Jackson, R. D.; Lin, Y.; Le Marchand, L.; Matise, T. C.; Monroe, K. R.; Moreland, L.; North, K. E.; Park, S. L.; Reiner, A.; Wallace, R.; Wilkens, L. R.; Kooperberg, C.; Ritchie, M. D.; and Crawford, D. C.\n\n\n \n\n\n\n PloS one, 14: e0226771. 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{PendergrassBuyskeJeffEtAl2019,\n\tabstract = {We performed a hypothesis-generating phenome-wide association study (PheWAS) to identify and characterize cross-phenotype associations, where one {SNP} is associated with two or more phenotypes, between thousands of genetic variants assayed on the Metabochip and hundreds of phenotypes in 5,897 African Americans as part of the {Population Architecture using Genomics and Epidemiology} (PAGE) I study. The PAGE I study was a National Human Genome Research Institute-funded collaboration of four study sites accessing diverse epidemiologic studies genotyped on the Metabochip, a custom genotyping chip that has dense coverage of regions in the genome previously associated with cardio-metabolic traits and outcomes in mostly European-descent populations. Here we focus on identifying novel phenome-genome relationships, where SNPs are associated with more than one phenotype. To do this, we performed a PheWAS, testing each {SNP} on the Metabochip for an association with up to 273 phenotypes in the participating PAGE I study sites. We identified 133 putative pleiotropic variants, defined as SNPs associated at an empirically derived p-value threshold of p<0.01 in two or more PAGE study sites for two or more phenotype classes. We further annotated these PheWAS-identified variants using publicly available functional data and local genetic ancestry. Amongst our novel findings is SPARC rs4958487, associated with increased glucose levels and hypertension. SPARC has been implicated in the pathogenesis of diabetes and is also known to have a potential role in fibrosis, a common consequence of multiple conditions including hypertension. The SPARC example and others highlight the potential that PheWAS approaches have in improving our understanding of complex disease architecture by identifying novel relationships between genetic variants and an array of common human phenotypes.},\n\tauthor = {Pendergrass, Sarah A. and Buyske, Steven and Jeff, Janina M. and Frase, Alex and Dudek, Scott and Bradford, Yuki and Ambite, Jose-Luis and Avery, Christy L. and Buzkova, Petra and Deelman, Ewa and Fesinmeyer, Megan D. and Haiman, Christopher and Heiss, Gerardo and Hindorff, Lucia A. and Hsu, Chun-Nan and Jackson, Rebecca D. and Lin, Yi and Le Marchand, Loic and Matise, Tara C. and Monroe, Kristine R. and Moreland, Larry and North, Kari E. and Park, Sungshim L. and Reiner, Alex and Wallace, Robert and Wilkens, Lynne R. and Kooperberg, Charles and Ritchie, Marylyn D. and Crawford, Dana C.},\n\tcitation-subset = {IM},\n\tcompleted = {2020-04-06},\n\tcountry = {United States},\n\tdoi = {10.1371/journal.pone.0226771},\n\tissn = {1932-6203},\n\tissn-linking = {1932-6203},\n\tissue = {12},\n\tjournal = {PloS one},\n\tkeywords = {African Americans, genetics; Aged; Atherosclerosis, genetics; Epidemiologic Studies; Female; Genetic Pleiotropy; Genome-Wide Association Study; Humans; Male; Metagenomics; Middle Aged; Phenomics; Polymorphism, Single Nucleotide},\n\tnlm-id = {101285081},\n\towner = {NLM},\n\tpages = {e0226771},\n\tpii = {PONE-D-19-33442},\n\tpmc = {PMC6938343},\n\tpmid = {31891604},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/31891604/},\n\n\tpubmodel = {Electronic-eCollection},\n\tpubstate = {epublish},\n\trevised = {2020-04-30},\n\ttitle = {A phenome-wide association study ({PheWAS}) in the {Population Architecture using Genomics and Epidemiology} ({PAGE}) study reveals potential pleiotropy in {African Americans}.},\n\tvolume = {14},\n\tyear = {2019},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/31891604/},\n\tbdsk-url-2 = {https://doi.org/10.1371/journal.pone.0226771}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Genetics of Chronic Kidney Disease Stages Across Ancestries: The PAGE Study.\n \n \n \n \n\n\n \n Lin, B. M.; Nadkarni, G. N.; Tao, R.; Graff, M.; Fornage, M.; Buyske, S.; Matise, T. C.; Highland, H. M.; Wilkens, L. R.; Carlson, C. S.; Park, S. L.; Setiawan, V. W.; Ambite, J. L.; Heiss, G.; Boerwinkle, E.; Lin, D.; Morris, A. P.; Loos, R. J. F.; Kooperberg, C.; North, K. E.; Wassel, C. L.; and Franceschini, N.\n\n\n \n\n\n\n Frontiers in genetics, 10: 494. 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Genetics$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@article{LinNadkarniTaoEtAl2019,\n\tabstract = {Chronic kidney disease (CKD) is common and disproportionally burdens United States ethnic minorities. Its genetic determinants may differ by disease severity and clinical stages. To uncover genetic factors associated CKD severity among high-risk ethnic groups, we performed genome-wide association studies ({GWAS}) in diverse populations within the {Population Architecture using Genomics and Epidemiology} (PAGE) study. We assembled multi-ethnic genome-wide imputed data on CKD non-overlapping cases [4,150 mild to moderate CKD, 1,105 end-stage kidney disease (ESKD)] and non-CKD controls for up to 41,041 PAGE participants (African Americans, Hispanics/Latinos, East Asian, Native Hawaiian, and American Indians). We implemented a generalized estimating equation approach for {GWAS} using ancestry combined data while adjusting for age, sex, principal components, study, and ethnicity. The {GWAS} identified a novel genome-wide associated locus for mild to moderate CKD nearby   (rs10906850,   = 3.7 × 10 ) that replicated in the United Kingdom Biobank white British (  = 0.008). Several variants at the   locus were associated with ESKD including the   G1 rs73885319 (  = 1.2 × 10 ). There was no overlap among associated loci for CKD and ESKD traits, even at the previously reported   locus (  = 0.76 for CKD). Several additional loci were associated with CKD or ESKD at  -values below the genome-wide threshold. These loci were often driven by variants more common in non-European ancestry. Our genetic study identified a novel association at   for CKD and showed for the first time strong associations of the   variants with ESKD across multi-ethnic populations. Our findings suggest differences in genetic effects across CKD severity and provide information for study design of genetic studies of CKD in diverse populations.},\n\tauthor = {Lin, Bridget M. and Nadkarni, Girish N. and Tao, Ran and Graff, Mariaelisa and Fornage, Myriam and Buyske, Steven and Matise, Tara C. and Highland, Heather M. and Wilkens, Lynne R. and Carlson, Christopher S. and Park, S. Lani and Setiawan, V. Wendy and Ambite, Jose Luis and Heiss, Gerardo and Boerwinkle, Eric and Lin, Dan-Yu and Morris, Andrew P. and Loos, Ruth J. F. and Kooperberg, Charles and North, Kari E. and Wassel, Christina L. and Franceschini, Nora},\n\tcountry = {Switzerland},\n\tdoi = {10.3389/fgene.2019.00494},\n\tissn = {1664-8021},\n\tissn-linking = {1664-8021},\n\tjournal = {Frontiers in genetics},\n\tkeywords = {APOL1; chronic kidney disease stages; diverse populations; end stage kidney disease; genetics; genome-wide association studies; single nucleotide polymorphisms},\n\tnlm-id = {101560621},\n\towner = {NLM},\n\tpages = {494},\n\tpmc = {PMC6544117},\n\tpmid = {31178898},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/31178898/},\n\n\tpubmodel = {Electronic-eCollection},\n\tpubstate = {epublish},\n\trevised = {2020-09-28},\n\ttitle = {Genetics of Chronic Kidney Disease Stages Across Ancestries: The {PAGE} Study.},\n\tvolume = {10},\n\tyear = {2019},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/31178898/},\n\tbdsk-url-2 = {https://doi.org/10.3389/fgene.2019.00494}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Genetic analyses of diverse populations improves discovery for complex traits.\n \n \n \n \n\n\n \n Wojcik, G. L.; Graff, M.; Nishimura, K. K.; Tao, R.; Haessler, J.; Gignoux, C. R.; Highland, H. M.; Patel, Y. M.; Sorokin, E. P.; Avery, C. L.; Belbin, G. M.; Bien, S. A.; Cheng, I.; Cullina, S.; Hodonsky, C. J.; Hu, Y.; Huckins, L. M.; Jeff, J.; Justice, A. E.; Kocarnik, J. M.; Lim, U.; Lin, B. M.; Lu, Y.; Nelson, S. C.; Park, S. L.; Poisner, H.; Preuss, M. H.; Richard, M. A.; Schurmann, C.; Setiawan, V. W.; Sockell, A.; Vahi, K.; Verbanck, M.; Vishnu, A.; Walker, R. W.; Young, K. L.; Zubair, N.; Acuña-Alonso, V.; Ambite, J. L.; Barnes, K. C.; Boerwinkle, E.; Bottinger, E. P.; Bustamante, C. D.; Caberto, C.; Canizales-Quinteros, S.; Conomos, M. P.; Deelman, E.; Do, R.; Doheny, K.; Fernández-Rhodes, L.; Fornage, M.; Hailu, B.; Heiss, G.; Henn, B. M.; Hindorff, L. A.; Jackson, R. D.; Laurie, C. A.; Laurie, C. C.; Li, Y.; Lin, D.; Moreno-Estrada, A.; Nadkarni, G.; Norman, P. J.; Pooler, L. C.; Reiner, A. P.; Romm, J.; Sabatti, C.; Sandoval, K.; Sheng, X.; Stahl, E. A.; Stram, D. O.; Thornton, T. A.; Wassel, C. L.; Wilkens, L. R.; Winkler, C. A.; Yoneyama, S.; Buyske, S.; Haiman, C. A.; Kooperberg, C.; Le Marchand, L.; Loos, R. J. F.; Matise, T. C.; North, K. E.; Peters, U.; Kenny, E. E.; and Carlson, C. S.\n\n\n \n\n\n\n Nature, 570: 514–518. June 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Genetic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 8 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{WojcikGraffNishimuraEtAl2019,\n\tabstract = {Genome-wide association studies ({GWAS}) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry . In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific . Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations . Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The {Population Architecture using Genomics and Epidemiology} (PAGE) study conducted a {GWAS} of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 {GWAS} catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published {GWAS} associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States-where minority populations have a disproportionately higher burden of chronic conditions -the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.},\n\tauthor = {Wojcik, Genevieve L. and Graff, Mariaelisa and Nishimura, Katherine K. and Tao, Ran and Haessler, Jeffrey and Gignoux, Christopher R. and Highland, Heather M. and Patel, Yesha M. and Sorokin, Elena P. and Avery, Christy L. and Belbin, Gillian M. and Bien, Stephanie A. and Cheng, Iona and Cullina, Sinead and Hodonsky, Chani J. and Hu, Yao and Huckins, Laura M. and Jeff, Janina and Justice, Anne E. and Kocarnik, Jonathan M. and Lim, Unhee and Lin, Bridget M. and Lu, Yingchang and Nelson, Sarah C. and Park, Sung-Shim L. and Poisner, Hannah and Preuss, Michael H. and Richard, Melissa A. and Schurmann, Claudia and Setiawan, Veronica W. and Sockell, Alexandra and Vahi, Karan and Verbanck, Marie and Vishnu, Abhishek and Walker, Ryan W. and Young, Kristin L. and Zubair, Niha and Acu{\\~n}a-Alonso, Victor and Ambite, Jose Luis and Barnes, Kathleen C. and Boerwinkle, Eric and Bottinger, Erwin P. and Bustamante, Carlos D. and Caberto, Christian and Canizales-Quinteros, Samuel and Conomos, Matthew P. and Deelman, Ewa and Do, Ron and Doheny, Kimberly and Fern{\\'a}ndez-Rhodes, Lindsay and Fornage, Myriam and Hailu, Benyam and Heiss, Gerardo and Henn, Brenna M. and Hindorff, Lucia A. and Jackson, Rebecca D. and Laurie, Cecelia A. and Laurie, Cathy C. and Li, Yuqing and Lin, Dan-Yu and Moreno-Estrada, Andres and Nadkarni, Girish and Norman, Paul J. and Pooler, Loreall C. and Reiner, Alexander P. and Romm, Jane and Sabatti, Chiara and Sandoval, Karla and Sheng, Xin and Stahl, Eli A. and Stram, Daniel O. and Thornton, Timothy A. and Wassel, Christina L. and Wilkens, Lynne R. and Winkler, Cheryl A. and Yoneyama, Sachi and Buyske, Steven and Haiman, Christopher A. and Kooperberg, Charles and Le Marchand, Loic and Loos, Ruth J. F. and Matise, Tara C. and North, Kari E. and Peters, Ulrike and Kenny, Eimear E. and Carlson, Christopher S.},\n\tcitation-subset = {IM},\n\tcompleted = {2020-03-04},\n\tcountry = {England},\n\tdoi = {10.1038/s41586-019-1310-4},\n\tissn = {1476-4687},\n\tissn-linking = {0028-0836},\n\tissue = {7762},\n\tjournal = {Nature},\n\tkeywords = {African Continental Ancestry Group, genetics; Asian Continental Ancestry Group, genetics; Body Height, genetics; Cohort Studies; Female; Genetics, Medical, methods; Genome-Wide Association Study, methods; Health Equity, trends; Health Status Disparities; Hispanic Americans, genetics; Humans; Male; Minority Groups; Multifactorial Inheritance, genetics; United States; Women's Health},\n\tmid = {NIHMS1050617},\n\tmonth = jun,\n\tnlm-id = {0410462},\n\towner = {NLM},\n\tpages = {514--518},\n\tpii = {10.1038/s41586-019-1310-4},\n\tpmc = {PMC6785182},\n\tpmid = {31217584},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/31217584/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2020-09-29},\n\ttitle = {Genetic analyses of diverse populations improves discovery for complex traits.},\n\tvolume = {570},\n\tyear = {2019},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/31217584/},\n\tbdsk-url-2 = {https://doi.org/10.1038/s41586-019-1310-4}}\n\n

\n\n\n

\n Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry . In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific . Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations . Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States-where minority populations have a disproportionately higher burden of chronic conditions -the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.\n

\n\n\n

\n\n\n\n\n\n

\n\n

\n \n 2018\n \n \n (7)\n \n \n

\n \n \n

\n \n\n \n \n \n \n \n \n Worldwide Frequencies of APOL1 Renal Risk Variants.\n \n \n \n \n\n\n \n Nadkarni, G. N.; Gignoux, C. R.; Sorokin, E. P.; Daya, M.; Rahman, R.; Barnes, K. C.; Wassel, C. L.; and Kenny, E. E.\n\n\n \n\n\n\n The New England journal of medicine, 379: 2571–2572. Dec 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Worldwide$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{NadkarniGignouxSorokinEtAl2018,\n\tauthor = {Nadkarni, Girish N. and Gignoux, Christopher R. and Sorokin, Elena P. and Daya, Michelle and Rahman, Rayees and Barnes, Kathleen C. and Wassel, Christina L. and Kenny, Eimear E.},\n\tchemicals = {APOL1 protein, human, Apolipoprotein L1},\n\tcitation-subset = {AIM, IM},\n\tcompleted = {2019-01-11},\n\tcountry = {United States},\n\tdoi = {10.1056/NEJMc1800748},\n\tissn = {1533-4406},\n\tissn-linking = {0028-4793},\n\tissue = {26},\n\tjournal = {The New England journal of medicine},\n\tkeywords = {African Americans, genetics; Apolipoprotein L1, genetics; Genetic Predisposition to Disease; Genetic Variation; Genotype; Humans; Kidney Diseases, ethnology, genetics; Risk},\n\tmid = {NIHMS1020201},\n\tmonth = {Dec},\n\tnlm-id = {0255562},\n\towner = {NLM},\n\tpages = {2571--2572},\n\tpmc = {PMC6482949},\n\tpmid = {30586505},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/30586505/},\n\n\tpubmodel = {Print},\n\tpubstate = {ppublish},\n\trevised = {2020-06-04},\n\ttitle = {Worldwide Frequencies of {APOL1} Renal Risk Variants.},\n\tvolume = {379},\n\tyear = {2018},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/30586505/},\n\tbdsk-url-2 = {https://doi.org/10.1056/NEJMc1800748}}\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Imputation-Aware Tag SNP Selection To Improve Power for Large-Scale, Multi-ethnic Association Studies.\n \n \n \n \n\n\n \n Wojcik, G. L.; Fuchsberger, C.; Taliun, D.; Welch, R.; Martin, A. R.; Shringarpure, S.; Carlson, C. S.; Abecasis, G.; Kang, H. M.; Boehnke, M.; Bustamante, C. D.; Gignoux, C. R.; and Kenny, E. E.\n\n\n \n\n\n\n G3 (Bethesda, Md.), 8: 3255–3267. October 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Imputation-Aware$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{WojcikFuchsbergerTaliunEtAl2018,\n\tabstract = {The emergence of very large cohorts in genomic research has facilitated a focus on genotype-imputation strategies to power rare variant association. These strategies have benefited from improvements in imputation methods and association tests, however little attention has been paid to ways in which array design can increase rare variant association power. Therefore, we developed a novel framework to select tag SNPs using the reference panel of 26 populations from Phase 3 of the 1000 Genomes Project. We evaluate tag {SNP} performance   mean imputed r  at untyped sites using leave-one-out internal validation and standard imputation methods, rather than pairwise linkage disequilibrium. Moving beyond pairwise metrics allows us to account for haplotype diversity across the genome for improve imputation accuracy and demonstrates population-specific biases from pairwise estimates. We also examine array design strategies that contrast multi-ethnic cohorts   single populations, and show a boost in performance for the former can be obtained by prioritizing tag SNPs that contribute information across multiple populations simultaneously. Using our framework, we demonstrate increased imputation accuracy for rare variants (frequency < 1%) by 0.5-3.1% for an array of one million sites and 0.7-7.1% for an array of 500,000 sites, depending on the population. Finally, we show how recent explosive growth in non-African populations means tag SNPs capture on average 30% fewer other variants than in African populations. The unified framework presented here will enable investigators to make informed decisions for the design of new arrays, and help empower the next phase of rare variant association for global health.},\n\tauthor = {Wojcik, Genevieve L. and Fuchsberger, Christian and Taliun, Daniel and Welch, Ryan and Martin, Alicia R. and Shringarpure, Suyash and Carlson, Christopher S. and Abecasis, Goncalo and Kang, Hyun Min and Boehnke, Michael and Bustamante, Carlos D. and Gignoux, Christopher R. and Kenny, Eimear E.},\n\tcitation-subset = {IM},\n\tcompleted = {2019-01-18},\n\tcountry = {United States},\n\tdoi = {10.1534/g3.118.200502},\n\tissn = {2160-1836},\n\tissn-linking = {2160-1836},\n\tissue = {10},\n\tjournal = {G3 (Bethesda, Md.)},\n\tkeywords = {Computational Biology, methods; Databases, Nucleic Acid; Ethnic Groups, genetics; Genetic Association Studies; Genetics, Population; Genome-Wide Association Study; Humans; Linkage Disequilibrium; Models, Genetic; Polymorphism, Single Nucleotide; Reproducibility of Results; Selection, Genetic; Genomics; Imputation; Statistical Genetics; array design; tag SNPs},\n\tmonth = oct,\n\tnlm-id = {101566598},\n\towner = {NLM},\n\tpages = {3255--3267},\n\tpii = {g3.118.200502},\n\tpmc = {PMC6169386},\n\tpmid = {30131328},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/30131328/},\n\n\tpubmodel = {Electronic},\n\tpubstate = {epublish},\n\trevised = {2020-04-11},\n\ttitle = {Imputation-Aware Tag {SNP} Selection To Improve Power for Large-Scale, Multi-ethnic Association Studies.},\n\tvolume = {8},\n\tyear = {2018},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/30131328/},\n\tbdsk-url-2 = {https://doi.org/10.1534/g3.118.200502}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n The genetic underpinnings of variation in ages at menarche and natural menopause among women from the multi-ethnic Population Architecture using Genomics and Epidemiology (PAGE) Study: A trans-ethnic meta-analysis.\n \n \n \n \n\n\n \n Fernández-Rhodes, L.; Malinowski, J. R.; Wang, Y.; Tao, R.; Pankratz, N.; Jeff, J. M.; Yoneyama, S.; Carty, C. L.; Setiawan, V. W.; Le Marchand, L.; Haiman, C.; Corbett, S.; Demerath, E.; Heiss, G.; Gross, M.; Buzkova, P.; Crawford, D. C.; Hunt, S. C.; Rao, D. C.; Schwander, K.; Chakravarti, A.; Gottesman, O.; Abul-Husn, N. S.; Bottinger, E. P.; Loos, R. J. F.; Raffel, L. J.; Yao, J.; Guo, X.; Bielinski, S. J.; Rotter, J. I.; Vaidya, D.; Chen, Y. I.; Castañeda, S. F.; Daviglus, M.; Kaplan, R.; Talavera, G. A.; Ryckman, K. K.; Peters, U.; Ambite, J. L.; Buyske, S.; Hindorff, L.; Kooperberg, C.; Matise, T.; Franceschini, N.; and North, K. E.\n\n\n \n\n\n\n PloS one, 13: e0200486. 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{FernandezRhodesMalinowskiWangEtAl2018,\n\tabstract = {Current knowledge of the genetic architecture of key reproductive events across the female life course is largely based on association studies of European descent women. The relevance of known loci for age at menarche (AAM) and age at natural menopause (ANM) in diverse populations remains unclear. We investigated 32 AAM and 14 ANM previously-identified loci and sought to identify novel loci in a trans-ethnic array-wide study of 196,483 SNPs on the MetaboChip (Illumina, Inc.). A total of 45,364 women of diverse ancestries (African, Hispanic/Latina, Asian American and American Indian/Alaskan Native) in the {Population Architecture using Genomics and Epidemiology} (PAGE) Study were included in cross-sectional analyses of AAM and ANM. Within each study we conducted a linear regression of {SNP} associations with self-reported or medical record-derived AAM or ANM (in years), adjusting for birth year, population stratification, and center/region, as appropriate, and meta-analyzed results across studies using multiple meta-analytic techniques. For both AAM and ANM, we observed more directionally consistent associations with the previously reported risk alleles than expected by chance (p-valuesbinomial≤0.01). Eight densely genotyped reproductive loci generalized significantly to at least one non-European population. We identified one trans-ethnic array-wide {SNP} association with AAM and two significant associations with ANM, which have not been described previously. Additionally, we observed evidence of independent secondary signals at three of six AAM trans-ethnic loci. Our findings support the transferability of reproductive trait loci discovered in European women to women of other race/ethnicities and indicate the presence of additional trans-ethnic associations both at both novel and established loci. These findings suggest the benefit of including diverse populations in future studies of the genetic architecture of female growth and development.},\n\tauthor = {Fern{\\'a}ndez-Rhodes, Lindsay and Malinowski, Jennifer R. and Wang, Yujie and Tao, Ran and Pankratz, Nathan and Jeff, Janina M. and Yoneyama, Sachiko and Carty, Cara L. and Setiawan, V. Wendy and Le Marchand, Loic and Haiman, Christopher and Corbett, Steven and Demerath, Ellen and Heiss, Gerardo and Gross, Myron and Buzkova, Petra and Crawford, Dana C. and Hunt, Steven C. and Rao, D. C. and Schwander, Karen and Chakravarti, Aravinda and Gottesman, Omri and Abul-Husn, Noura S. and Bottinger, Erwin P. and Loos, Ruth J. F. and Raffel, Leslie J. and Yao, Jie and Guo, Xiuqing and Bielinski, Suzette J. and Rotter, Jerome I. and Vaidya, Dhananjay and Chen, Yii-Der Ida and Casta{\\~n}eda, Sheila F. and Daviglus, Martha and Kaplan, Robert and Talavera, Gregory A. and Ryckman, Kelli K. and Peters, Ulrike and Ambite, Jose Luis and Buyske, Steven and Hindorff, Lucia and Kooperberg, Charles and Matise, Tara and Franceschini, Nora and North, Kari E.},\n\tcitation-subset = {IM},\n\tcompleted = {2019-01-11},\n\tcountry = {United States},\n\tdoi = {10.1371/journal.pone.0200486},\n\tissn = {1932-6203},\n\tissn-linking = {1932-6203},\n\tissue = {7},\n\tjournal = {PloS one},\n\tkeywords = {Age Factors; Alleles; Biological Variation, Population, ethnology, genetics; Female; Genetic Loci, genetics; Genotype; Humans; Menarche, ethnology, genetics; Menopause, ethnology, genetics; Phenotype; Polymorphism, Single Nucleotide},\n\tnlm-id = {101285081},\n\towner = {NLM},\n\tpages = {e0200486},\n\tpii = {PONE-D-18-02039},\n\tpmc = {PMC6059436},\n\tpmid = {30044860},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/30044860/},\n\n\tpubmodel = {Electronic-eCollection},\n\tpubstate = {epublish},\n\trevised = {2020-03-09},\n\ttitle = {The genetic underpinnings of variation in ages at menarche and natural menopause among women from the multi-ethnic {Population Architecture using Genomics and Epidemiology} ({PAGE}) Study: A trans-ethnic meta-analysis.},\n\tvolume = {13},\n\tyear = {2018},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/30044860/},\n\tbdsk-url-2 = {https://doi.org/10.1371/journal.pone.0200486}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Generalization and fine mapping of red blood cell trait genetic associations to multi-ethnic populations: The PAGE Study.\n \n \n \n \n\n\n \n Hodonsky, C. J.; Schurmann, C.; Schick, U. M.; Kocarnik, J.; Tao, R.; van Rooij, F. J.; Wassel, C.; Buyske, S.; Fornage, M.; Hindorff, L. A.; Floyd, J. S.; Ganesh, S. K.; Lin, D.; North, K. E.; Reiner, A. P.; Loos, R. J.; Kooperberg, C.; and Avery, C. L.\n\n\n \n\n\n\n American journal of hematology. June 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Generalization$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@article{JoHodonskySchurmannSchickEtAl2018,\n\tabstract = {Red blood cell (RBC) traits provide insight into a wide range of physiological states and exhibit moderate to high heritability, making them excellent candidates for genetic studies to inform underlying biologic mechanisms. Previous RBC trait genome-wide association studies were performed primarily in European- or Asian-ancestry populations, missing opportunities to inform understanding of RBC genetic architecture in diverse populations and reduce intervals surrounding putative functional SNPs through fine-mapping. Here, we report the first fine-mapping of six correlated (Pearson's r range: |0.04 - 0.92|) RBC traits in up to 19,036 African Americans and 19,562 Hispanic/Latinos participants of the {Population Architecture using Genomics and Epidemiology} (PAGE) consortium. Trans-ethnic meta-analysis of race/ethnic- and study-specific estimates for approximately 11,000 SNPs flanking 13 previously identified association signals as well as 150,000 additional array-wide SNPs was performed using inverse-variance meta-analysis after adjusting for study and clinical covariates. Approximately half of previously reported index {SNP}-RBC trait associations generalized to the trans-ethnic study population (p<1.7x10  ); previously unreported independent association signals within the ABO region reinforce the potential for multiple functional variants affecting the same locus. Trans-ethnic fine-mapping did not reveal additional signals at the HFE locus independent of the known functional variants. Finally, we identified a potential novel association in the Hispanic/Latino study population at the HECTD4/RPL6 locus for RBC count (p=1.9x10  ). The identification of a previously unknown association, generalization of a large proportion of known association signals, and refinement of known association signals all exemplify the benefits of genetic studies in diverse populations. This article is protected by copyright. All rights reserved.},\n\tauthor = {Hodonsky, Chani Jo and Schurmann, Claudia and Schick, Ursula M. and Kocarnik, Jonathan and Tao, Ran and van Rooij, Frank Ja and Wassel, Christina and Buyske, Steve and Fornage, Myriam and Hindorff, Lucia A. and Floyd, James S. and Ganesh, Santhi K. and Lin, Dan-Yu and North, Kari E. and Reiner, Alex P. and Loos, Ruth Jf and Kooperberg, Charles and Avery, Christy L.},\n\tcountry = {United States},\n\tdoi = {10.1002/ajh.25161},\n\tissn = {1096-8652},\n\tissn-linking = {0361-8609},\n\tjournal = {American journal of hematology},\n\tkeywords = {Genomics; RBC traits; fine-mapping; generalization; trans-ethnic meta-analysis},\n\tmid = {NIHMS972578},\n\tmonth = jun,\n\tnlm-id = {7610369},\n\towner = {NLM},\n\tpmc = {PMC6300146},\n\tpmid = {29905378},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/29905378/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {aheadofprint},\n\trevised = {2020-03-06},\n\ttitle = {Generalization and fine mapping of red blood cell trait genetic associations to multi-ethnic populations: The {PAGE} Study.},\n\tyear = {2018},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/29905378/},\n\tbdsk-url-2 = {https://doi.org/10.1002/ajh.25161}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Discovery, fine-mapping, and conditional analyses of genetic variants associated with C-reactive protein in multiethnic populations using the Metabochip in the Population Architecture using Genomics and Epidemiology (PAGE) study.\n \n \n \n \n\n\n \n Kocarnik, J. M.; Richard, M.; Graff, M.; Haessler, J.; Bien, S.; Carlson, C.; Carty, C. L.; Reiner, A. P.; Avery, C. L.; Ballantyne, C. M.; LaCroix, A. Z.; Assimes, T. L.; Barbalic, M.; Pankratz, N.; Tang, W.; Tao, R.; Chen, D.; Talavera, G. A.; Daviglus, M. L.; Chirinos-Medina, D. A.; Pereira, R.; Nishimura, K.; Bu ̌zková, P.; Best, L. G.; Ambite, J. L.; Cheng, I.; Crawford, D. C.; Hindorff, L. A.; Fornage, M.; Heiss, G.; North, K. E.; Haiman, C. A.; Peters, U.; Le Marchand, L.; and Kooperberg, C.\n\n\n \n\n\n\n Human molecular genetics, 27: 2940–2953. August 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Discovery,$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{KocarnikRichardGraffEtAl2018,\n\tabstract = {C-reactive protein (CRP) is a circulating biomarker indicative of systemic inflammation. We aimed to evaluate genetic associations with CRP levels among non-European-ancestry populations through discovery, fine-mapping and conditional analyses. A total of 30 503 non-European-ancestry participants from 6 studies participating in the {Population Architecture using Genomics and Epidemiology} study had serum high-sensitivity CRP measurements and ∼200 000 single nucleotide polymorphisms (SNPs) genotyped on the Metabochip. We evaluated the association between each {SNP} and log-transformed CRP levels using multivariate linear regression, with additive genetic models adjusted for age, sex, the first four principal components of genetic ancestry, and study-specific factors. Differential linkage disequilibrium patterns between race/ethnicity groups were used to fine-map regions associated with CRP levels. Conditional analyses evaluated for multiple independent signals within genetic regions. One hundred and sixty-three unique variants in 12 loci in overall or race/ethnicity-stratified Metabochip-wide scans reached a Bonferroni-corrected P-value <2.5E-7. Three loci have no (HACL1, OLFML2B) or only limited (PLA2G6) previous associations with CRP levels. Six loci had different top hits in race/ethnicity-specific versus overall analyses. Fine-mapping refined the signal in six loci, particularly in HNF1A. Conditional analyses provided evidence for secondary signals in LEPR, IL1RN and HNF1A, and for multiple independent signals in CRP and APOE. We identified novel variants and loci associated with CRP levels, generalized known CRP associations to a multiethnic study population, refined association signals at several loci and found evidence for multiple independent signals at several well-known loci. This study demonstrates the benefit of conducting inclusive genetic association studies in large multiethnic populations.},\n\tauthor = {Kocarnik, Jonathan M. and Richard, Melissa and Graff, Misa and Haessler, Jeffrey and Bien, Stephanie and Carlson, Chris and Carty, Cara L. and Reiner, Alexander P. and Avery, Christy L. and Ballantyne, Christie M. and LaCroix, Andrea Z. and Assimes, Themistocles L. and Barbalic, Maja and Pankratz, Nathan and Tang, Weihong and Tao, Ran and Chen, Dongquan and Talavera, Gregory A. and Daviglus, Martha L. and Chirinos-Medina, Diana A. and Pereira, Rocio and Nishimura, Katie and Bu{\\v z}kov{\\'a}, Petra and Best, Lyle G. and Ambite, Jos{\\'e} Luis and Cheng, Iona and Crawford, Dana C. and Hindorff, Lucia A. and Fornage, Myriam and Heiss, Gerardo and North, Kari E. and Haiman, Christopher A. and Peters, Ulrike and Le Marchand, Loic and Kooperberg, Charles},\n\tchemicals = {Glycoproteins, OLFML3 protein, human, C-Reactive Protein, Group VI Phospholipases A2, PLA2G6 protein, human, HACL1 protein, human, Enoyl-CoA Hydratase},\n\tcitation-subset = {IM},\n\tcompleted = {2019-03-08},\n\tcountry = {England},\n\tdoi = {10.1093/hmg/ddy211},\n\tissn = {1460-2083},\n\tissn-linking = {0964-6906},\n\tissue = {16},\n\tjournal = {Human molecular genetics},\n\tkeywords = {C-Reactive Protein, genetics; Enoyl-CoA Hydratase, genetics; European Continental Ancestry Group, genetics; Female; Genome-Wide Association Study; Glycoproteins, genetics; Group VI Phospholipases A2, genetics; Humans; Linkage Disequilibrium; Male; Metagenomics; Molecular Epidemiology, methods; Polymorphism, Single Nucleotide},\n\tmonth = aug,\n\tnlm-id = {9208958},\n\towner = {NLM},\n\tpages = {2940--2953},\n\tpii = {5033382},\n\tpmc = {PMC6077792},\n\tpmid = {29878111},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/29878111/},\n\n\tpubmodel = {Print},\n\tpubstate = {ppublish},\n\trevised = {2020-04-16},\n\ttitle = {Discovery, fine-mapping, and conditional analyses of genetic variants associated with {C}-reactive protein in multiethnic populations using the {Metabochip} in the {Population Architecture using Genomics and Epidemiology} ({PAGE}) study.},\n\tvolume = {27},\n\tyear = {2018},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/29878111/},\n\tbdsk-url-2 = {https://doi.org/10.1093/hmg/ddy211}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Evaluation of 71 Coronary Artery Disease Risk Variants in a Multiethnic Cohort.\n \n \n \n \n\n\n \n Ke, W.; Rand, K. A.; Conti, D. V.; Setiawan, V. W.; Stram, D. O.; Wilkens, L.; Le Marchand, L.; Assimes, T. L.; and Haiman, C. A.\n\n\n \n\n\n\n Frontiers in cardiovascular medicine, 5: 19. 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@article{KeRandContiEtAl2018,\n\tabstract = {Coronary heart disease (CHD) is the most common cause of death worldwide. Previous studies have identified numerous common CHD susceptibility loci, with the vast majority identified in populations of European ancestry. How well these findings transfer to other racial/ethnic populations remains unclear. We examined the generalizability of the associations with 71 known CHD loci in African American, Latino and Japanese men and women in the Multiethnic Cohort (6,035 cases and 11,251 controls). In the combined multiethnic sample, 78% of the loci demonstrated odds ratios that were directionally consistent with those previously reported (  = 2 × 10 ), with this fraction ranging from 59% in Japanese to 70% in Latinos. The number of nominally significant associations across all susceptibility regions ranged from only 1 in Japanese to 11 in African Americans with the most statistically significant association observed through locus fine-mapping noted for rs3832016 (OR = 1.16,   = 2.5×10 ) in the   region on chromosome  . Lastly, we examined the cumulative predictive effect of CHD SNPs across populations with improved power by creating genetic risk scores (GRSs) that summarize an individual's aggregated exposure to risk variants. We found the GRSs to be significantly associated with risk in African Americans (OR = 1.03 per allele;   = 4.1×10 ) and Latinos (OR = 1.03;   = 2.2 × 10 ), but not in Japanese (OR = 1.01;   = 0.11). While a sizable fraction of the known CHD loci appear to generalize in these populations, larger fine-mapping studies will be needed to localize the functional alleles and better define their contribution to CHD risk in these populations.},\n\tauthor = {Ke, Wangjing and Rand, Kristin A. and Conti, David V. and Setiawan, Veronica W. and Stram, Daniel O. and Wilkens, Lynne and Le Marchand, Loic and Assimes, Themistocles L. and Haiman, Christopher A.},\n\tcountry = {Switzerland},\n\tdoi = {10.3389/fcvm.2018.00019},\n\tissn = {2297-055X},\n\tissn-linking = {2297-055X},\n\tjournal = {Frontiers in cardiovascular medicine},\n\tkeywords = {African Americans; Japanese Americans; Latino American; SORT1; coronary heart disease; genome wide association study ({GWAS}); multi-ethnic},\n\tnlm-id = {101653388},\n\towner = {NLM},\n\tpages = {19},\n\tpmc = {PMC5931137},\n\tpmid = {29740590},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/29740590/},\n\n\tpubmodel = {Electronic-eCollection},\n\tpubstate = {epublish},\n\trevised = {2019-11-20},\n\ttitle = {Evaluation of 71 Coronary Artery Disease Risk Variants in a Multiethnic Cohort.},\n\tvolume = {5},\n\tyear = {2018},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/29740590/},\n\tbdsk-url-2 = {https://doi.org/10.3389/fcvm.2018.00019}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Trans-ethnic analysis of Metabochip data identifies two new loci associated with BMI.\n \n \n \n \n\n\n \n Gong, J.; Nishimura, K. K.; Fernandez-Rhodes, L.; Haessler, J.; Bien, S.; Graff, M.; Lim, U.; Lu, Y.; Gross, M.; Fornage, M.; Yoneyama, S.; Isasi, C. R.; Buzkova, P.; Daviglus, M.; Lin, D.; Tao, R.; Goodloe, R.; Bush, W. S.; Farber-Eger, E.; Boston, J.; Dilks, H. H.; Ehret, G.; Gu, C. C.; Lewis, C. E.; Nguyen, K. H.; Cooper, R.; Leppert, M.; Irvin, M. R.; Bottinger, E. P.; Wilkens, L. R.; Haiman, C. A.; Park, L.; Monroe, K. R.; Cheng, I.; Stram, D. O.; Carlson, C. S.; Jackson, R.; Kuller, L.; Houston, D.; Kooperberg, C.; Buyske, S.; Hindorff, L. A.; Crawford, D. C.; Loos, R. J. F.; Le Marchand, L.; Matise, T. C.; North, K. E.; and Peters, U.\n\n\n \n\n\n\n International journal of obesity (2005), 42: 384–390. March 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Trans-ethnic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{GongNishimuraFernandezRhodesEtAl2018,\n\tabstract = {Body mass index (BMI) is commonly used to assess obesity, which is associated with numerous diseases and negative health outcomes. BMI has been shown to be a heritable, polygenic trait, with close to 100 loci previously identified and replicated in multiple populations. We aim to replicate known BMI loci and identify novel associations in a trans-ethnic study population. Using eligible participants from the {Population Architecture using Genomics and Epidemiology} consortium, we conducted a trans-ethnic meta-analysis of 102 514 African Americans, Hispanics, Asian/Native Hawaiian, Native Americans and European Americans. Participants were genotyped on over 200 000 SNPs on the Illumina Metabochip custom array, or imputed into the 1000 Genomes Project (Phase I). Linear regression of the natural log of BMI, adjusting for age, sex, study site (if applicable), and ancestry principal components, was conducted for each race/ethnicity within each study cohort. Race/ethnicity-specific, and combined meta-analyses used fixed-effects models. We replicated 15 of 21 BMI loci included on the Metabochip, and identified two novel BMI loci at 1q41 (rs2820436) and 2q31.1 (rs10930502) at the Metabochip-wide significance threshold (P<2.5 × 10 ). Bioinformatic functional investigation of SNPs at these loci suggests a possible impact on pathways that regulate metabolism and adipose tissue. Conducting studies in genetically diverse populations continues to be a valuable strategy for replicating known loci and uncovering novel BMI associations.},\n\tauthor = {Gong, J. and Nishimura, K. K. and Fernandez-Rhodes, L. and Haessler, J. and Bien, S. and Graff, M. and Lim, U. and Lu, Y. and Gross, M. and Fornage, M. and Yoneyama, S. and Isasi, C. R. and Buzkova, P. and Daviglus, M. and Lin, D.-Y. and Tao, R. and Goodloe, R. and Bush, W. S. and Farber-Eger, E. and Boston, J. and Dilks, H. H. and Ehret, G. and Gu, C. C. and Lewis, C. E. and Nguyen, K.-D. H. and Cooper, R. and Leppert, M. and Irvin, M. R. and Bottinger, E. P. and Wilkens, L. R. and Haiman, C. A. and Park, L. and Monroe, K. R. and Cheng, I. and Stram, D. O. and Carlson, C. S. and Jackson, R. and Kuller, L. and Houston, D. and Kooperberg, C. and Buyske, S. and Hindorff, L. A. and Crawford, D. C. and Loos, R. J. F. and Le Marchand, L. and Matise, T. C. and North, K. E. and Peters, U.},\n\tcitation-subset = {IM},\n\tcompleted = {2019-03-15},\n\tcountry = {England},\n\tdoi = {10.1038/ijo.2017.304},\n\tissn = {1476-5497},\n\tissn-linking = {0307-0565},\n\tissue = {3},\n\tjournal = {International journal of obesity (2005)},\n\tkeywords = {Body Mass Index; Continental Population Groups, genetics, statistics & numerical data; Genome-Wide Association Study; Genomics; Humans; Polymorphism, Single Nucleotide, genetics},\n\tmid = {NIHMS922308},\n\tmonth = mar,\n\tnlm-id = {101256108},\n\towner = {NLM},\n\tpages = {384--390},\n\tpii = {ijo2017304},\n\tpmc = {PMC5876082},\n\tpmid = {29381148},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/29381148/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2019-12-20},\n\ttitle = {Trans-ethnic analysis of {Metabochip} data identifies two new loci associated with {BMI}.},\n\tvolume = {42},\n\tyear = {2018},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/29381148/},\n\tbdsk-url-2 = {https://doi.org/10.1038/ijo.2017.304}}\n\n

\n\n\n\n\n\n

\n\n

\n \n 2017\n \n \n (6)\n \n \n

\n \n \n

\n \n\n \n \n \n \n \n \n Transethnic insight into the genetics of glycaemic traits: fine-mapping results from the Population Architecture using Genomics and Epidemiology (PAGE) consortium.\n \n \n \n \n\n\n \n Bien, S. A.; Pankow, J. S.; Haessler, J.; Lu, Y.; Pankratz, N.; Rohde, R. R.; Tamuno, A.; Carlson, C. S.; Schumacher, F. R.; B ̊u ̌zková, P.; Daviglus, M. L.; Lim, U.; Fornage, M.; Fernandez-Rhodes, L.; Avilés-Santa, L.; Buyske, S.; Gross, M. D.; Graff, M.; Isasi, C. R.; Kuller, L. H.; Manson, J. E.; Matise, T. C.; Prentice, R. L.; Wilkens, L. R.; Yoneyama, S.; Loos, R. J. F.; Hindorff, L. A.; Le Marchand, L.; North, K. E.; Haiman, C. A.; Peters, U.; and Kooperberg, C.\n\n\n \n\n\n\n Diabetologia, 60: 2384–2398. December 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Transethnic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{BienPankowHaesslerEtAl2017,\n\tabstract = {Elevated levels of fasting glucose and fasting insulin in non-diabetic individuals are markers of dysregulation of glucose metabolism and are strong risk factors for type 2 diabetes. Genome-wide association studies have discovered over 50 SNPs associated with these traits. Most of these loci were discovered in European populations and have not been tested in a well-powered multi-ethnic study. We hypothesised that a large, ancestrally diverse, fine-mapping genetic study of glycaemic traits would identify novel and population-specific associations that were previously undetectable by European-centric studies. A multiethnic study of up to 26,760 unrelated individuals without diabetes, of predominantly Hispanic/Latino and African ancestries, were genotyped using the Metabochip. Transethnic meta-analysis of racial/ethnic-specific linear regression analyses were performed for fasting glucose and fasting insulin. We attempted to replicate 39 fasting glucose and 17 fasting insulin loci. Genetic fine-mapping was performed through sequential conditional analyses in 15 regions that included both the initially reported {SNP} association(s) and denser coverage of {SNP} markers. In addition, Metabochip-wide analyses were performed to discover novel fasting glucose and fasting insulin loci. The most significant {SNP} associations were further examined using bioinformatic functional annotation. Previously reported {SNP} associations were significantly replicated (p ≤ 0.05) in 31/39 fasting glucose loci and 14/17 fasting insulin loci. Eleven glycaemic trait loci were refined to a smaller list of potentially causal variants through transethnic meta-analysis. Stepwise conditional analysis identified two loci with independent secondary signals (G6PC2-rs477224 and GCK-rs2908290), which had not previously been reported. Population-specific conditional analyses identified an independent signal in G6PC2 tagged by the rare variant rs77719485 in African ancestry. Further Metabochip-wide analysis uncovered one novel fasting insulin locus at SLC17A2-rs75862513. These findings suggest that while glycaemic trait loci often have generalisable effects across the studied populations, transethnic genetic studies help to prioritise likely functional SNPs, identify novel associations that may be population-specific and in turn have the potential to influence screening efforts or therapeutic discoveries. The summary statistics from each of the ancestry-specific and transethnic (combined ancestry) results can be found under the PAGE study on dbGaP here: https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000356.v1.p1.},\n\tauthor = {Bien, Stephanie A. and Pankow, James S. and Haessler, Jeffrey and Lu, Yinchang and Pankratz, Nathan and Rohde, Rebecca R. and Tamuno, Alfred and Carlson, Christopher S. and Schumacher, Fredrick R. and B{\\r u}{\\v z}kov{\\'a}, Petra and Daviglus, Martha L. and Lim, Unhee and Fornage, Myriam and Fernandez-Rhodes, Lindsay and Avil{\\'e}s-Santa, Larissa and Buyske, Steven and Gross, Myron D. and Graff, Mariaelisa and Isasi, Carmen R. and Kuller, Lewis H. and Manson, JoAnn E. and Matise, Tara C. and Prentice, Ross L. and Wilkens, Lynne R. and Yoneyama, Sachiko and Loos, Ruth J. F. and Hindorff, Lucia A. and Le Marchand, Loic and North, Kari E. and Haiman, Christopher A. and Peters, Ulrike and Kooperberg, Charles},\n\tchemicals = {Blood Glucose, Insulin},\n\tcitation-subset = {IM},\n\tcompleted = {2018-06-25},\n\tcountry = {Germany},\n\tdoi = {10.1007/s00125-017-4405-1},\n\tissn = {1432-0428},\n\tissn-linking = {0012-186X},\n\tissue = {12},\n\tjournal = {Diabetologia},\n\tkeywords = {Blood Glucose, metabolism; Diabetes Mellitus, Type 2, blood, genetics, metabolism; European Continental Ancestry Group; Fasting, blood; Female; Genome-Wide Association Study; Humans; Insulin, blood; Male; Polymorphism, Single Nucleotide, genetics; Fine-mapping; Genetic; Glucose; Glycaemic; Insulin; Multiethnic; Page; Transethnic; Type 2 diabetes},\n\tmid = {NIHMS948154},\n\tmonth = dec,\n\tnlm-id = {0006777},\n\towner = {NLM},\n\tpages = {2384--2398},\n\tpii = {10.1007/s00125-017-4405-1},\n\tpmc = {PMC5918310},\n\tpmid = {28905132},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/28905132/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2019-11-15},\n\ttitle = {Transethnic insight into the genetics of glycaemic traits: fine-mapping results from the {Population Architecture using Genomics and Epidemiology} ({PAGE}) consortium.},\n\tvolume = {60},\n\tyear = {2017},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/28905132/},\n\tbdsk-url-2 = {https://doi.org/10.1007/s00125-017-4405-1}}\n\n

\n\n\n

\n Elevated levels of fasting glucose and fasting insulin in non-diabetic individuals are markers of dysregulation of glucose metabolism and are strong risk factors for type 2 diabetes. Genome-wide association studies have discovered over 50 SNPs associated with these traits. Most of these loci were discovered in European populations and have not been tested in a well-powered multi-ethnic study. We hypothesised that a large, ancestrally diverse, fine-mapping genetic study of glycaemic traits would identify novel and population-specific associations that were previously undetectable by European-centric studies. A multiethnic study of up to 26,760 unrelated individuals without diabetes, of predominantly Hispanic/Latino and African ancestries, were genotyped using the Metabochip. Transethnic meta-analysis of racial/ethnic-specific linear regression analyses were performed for fasting glucose and fasting insulin. We attempted to replicate 39 fasting glucose and 17 fasting insulin loci. Genetic fine-mapping was performed through sequential conditional analyses in 15 regions that included both the initially reported SNP association(s) and denser coverage of SNP markers. In addition, Metabochip-wide analyses were performed to discover novel fasting glucose and fasting insulin loci. The most significant SNP associations were further examined using bioinformatic functional annotation. Previously reported SNP associations were significantly replicated (p ≤ 0.05) in 31/39 fasting glucose loci and 14/17 fasting insulin loci. Eleven glycaemic trait loci were refined to a smaller list of potentially causal variants through transethnic meta-analysis. Stepwise conditional analysis identified two loci with independent secondary signals (G6PC2-rs477224 and GCK-rs2908290), which had not previously been reported. Population-specific conditional analyses identified an independent signal in G6PC2 tagged by the rare variant rs77719485 in African ancestry. Further Metabochip-wide analysis uncovered one novel fasting insulin locus at SLC17A2-rs75862513. These findings suggest that while glycaemic trait loci often have generalisable effects across the studied populations, transethnic genetic studies help to prioritise likely functional SNPs, identify novel associations that may be population-specific and in turn have the potential to influence screening efforts or therapeutic discoveries. The summary statistics from each of the ancestry-specific and transethnic (combined ancestry) results can be found under the PAGE study on dbGaP here: https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000356.v1.p1.\n

\n\n\n

\n \n\n \n \n \n \n \n \n Genetic identification of a common collagen disease in Puerto Ricans via identity-by-descent mapping in a health system.\n \n \n \n \n\n\n \n Belbin, G. M.; Odgis, J.; Sorokin, E. P.; Yee, M.; Kohli, S.; Glicksberg, B. S.; Gignoux, C. R.; Wojcik, G. L.; Van Vleck, T.; Jeff, J. M.; Linderman, M.; Schurmann, C.; Ruderfer, D.; Cai, X.; Merkelson, A.; Justice, A. E.; Young, K. L.; Graff, M.; North, K. E.; Peters, U.; James, R.; Hindorff, L.; Kornreich, R.; Edelmann, L.; Gottesman, O.; Stahl, E. E.; Cho, J. H.; Loos, R. J.; Bottinger, E. P.; Nadkarni, G. N.; Abul-Husn, N. S.; and Kenny, E. E.\n\n\n \n\n\n\n eLife, 6. September 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Genetic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{BelbinOdgisSorokinEtAl2017,\n\tabstract = {Achieving confidence in the causality of a disease locus is a complex task that often requires supporting data from both statistical genetics and clinical genomics. Here we describe a combined approach to identify and characterize a genetic disorder that leverages distantly related patients in a health system and population-scale mapping. We utilize genomic data to uncover components of distant pedigrees, in the absence of recorded pedigree information, in the multi-ethnic Bio  biobank in New York City. By linking to medical records, we discover a locus associated with both elevated genetic relatedness and extreme short stature. We link the gene,  , with a little-known genetic disease, previously thought to be rare and recessive. We demonstrate that disease manifests in both heterozygotes and homozygotes, indicating a common collagen disorder impacting up to 2% of individuals of Puerto Rican ancestry, leading to a better understanding of the continuum of complex and Mendelian disease.},\n\tauthor = {Belbin, Gillian Morven and Odgis, Jacqueline and Sorokin, Elena P. and Yee, Muh-Ching and Kohli, Sumita and Glicksberg, Benjamin S. and Gignoux, Christopher R. and Wojcik, Genevieve L. and Van Vleck, Tielman and Jeff, Janina M. and Linderman, Michael and Schurmann, Claudia and Ruderfer, Douglas and Cai, Xiaoqiang and Merkelson, Amanda and Justice, Anne E. and Young, Kristin L. and Graff, Misa and North, Kari E. and Peters, Ulrike and James, Regina and Hindorff, Lucia and Kornreich, Ruth and Edelmann, Lisa and Gottesman, Omri and Stahl, Eli Ea and Cho, Judy H. and Loos, Ruth Jf and Bottinger, Erwin P. and Nadkarni, Girish N. and Abul-Husn, Noura S. and Kenny, Eimear E.},\n\tchemicals = {COL27A1 protein, human, Fibrillar Collagens},\n\tcitation-subset = {IM},\n\tcompleted = {2018-05-09},\n\tcountry = {England},\n\tdoi = {10.7554/eLife.25060},\n\tissn = {2050-084X},\n\tissn-linking = {2050-084X},\n\tjournal = {eLife},\n\tkeywords = {Adolescent; Adult; Aged; Child; Collagen Diseases, epidemiology, genetics; Female; Fibrillar Collagens, genetics; Genotype; Heterozygote; Hispanic Americans; Homozygote; Humans; Male; Middle Aged; Molecular Epidemiology; Multigene Family; Musculoskeletal Diseases, epidemiology, genetics; New York City, epidemiology, ethnology; Pedigree; Whole Genome Sequencing; Young Adult; Electronic Health Records; {GWAS}; collagen disorder; evolutionary biology; genomics; human; human biology; medical genetics; medicine; population genetics},\n\tmonth = sep,\n\tnlm-id = {101579614},\n\towner = {NLM},\n\tpii = {e25060},\n\tpmc = {PMC5595434},\n\tpmid = {28895531},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/28895531/},\n\n\tpubmodel = {Electronic},\n\tpubstate = {epublish},\n\trevised = {2019-05-30},\n\ttitle = {Genetic identification of a common collagen disease in {Puerto Ricans} via identity-by-descent mapping in a health system.},\n\tvolume = {6},\n\tyear = {2017},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/28895531/},\n\tbdsk-url-2 = {https://doi.org/10.7554/eLife.25060}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Trans-ethnic fine-mapping of genetic loci for body mass index in the diverse ancestral populations of the Population Architecture using Genomics and Epidemiology (PAGE) Study reveals evidence for multiple signals at established loci.\n \n \n \n \n\n\n \n Fernández-Rhodes, L.; Gong, J.; Haessler, J.; Franceschini, N.; Graff, M.; Nishimura, K. K.; Wang, Y.; Highland, H. M.; Yoneyama, S.; Bush, W. S.; Goodloe, R.; Ritchie, M. D.; Crawford, D.; Gross, M.; Fornage, M.; Buzkova, P.; Tao, R.; Isasi, C.; Avilés-Santa, L.; Daviglus, M.; Mackey, R. H.; Houston, D.; Gu, C. C.; Ehret, G.; Nguyen, K. H.; Lewis, C. E.; Leppert, M.; Irvin, M. R.; Lim, U.; Haiman, C. A.; Le Marchand, L.; Schumacher, F.; Wilkens, L.; Lu, Y.; Bottinger, E. P.; Loos, R. J. L.; Sheu, W. H.; Guo, X.; Lee, W.; Hai, Y.; Hung, Y.; Absher, D.; Wu, I.; Taylor, K. D.; Lee, I.; Liu, Y.; Wang, T.; Quertermous, T.; Juang, J. J.; Rotter, J. I.; Assimes, T.; Hsiung, C. A.; Chen, Y. I.; Prentice, R.; Kuller, L. H.; Manson, J. E.; Kooperberg, C.; Smokowski, P.; Robinson, W. R.; Gordon-Larsen, P.; Li, R.; Hindorff, L.; Buyske, S.; Matise, T. C.; Peters, U.; and North, K. E.\n\n\n \n\n\n\n Human genetics, 136: 771–800. June 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Trans-ethnic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{FernandezRhodesGongHaesslerEtAl2017,\n\tabstract = {Most body mass index (BMI) genetic loci have been identified in studies of primarily European ancestries. The effect of these loci in other racial/ethnic groups is less clear. Thus, we aimed to characterize the generalizability of 170 established BMI variants, or their proxies, to diverse US populations and trans-ethnically fine-map 36 BMI loci using a sample of >102,000 adults of African, Hispanic/Latino, Asian, European and American Indian/Alaskan Native descent from the {Population Architecture using Genomics and Epidemiology} Study. We performed linear regression of the natural log of BMI (18.5-70 kg/m ) on the additive single nucleotide polymorphisms (SNPs) at BMI loci on the MetaboChip (Illumina, Inc.), adjusting for age, sex, population stratification, study site, or relatedness. We then performed fixed-effect meta-analyses and a Bayesian trans-ethnic meta-analysis to empirically cluster by allele frequency differences. Finally, we approximated conditional and joint associations to test for the presence of secondary signals. We noted directional consistency with the previously reported risk alleles beyond what would have been expected by chance (binomial p < 0.05). Nearly, a quarter of the previously described BMI index SNPs and 29 of 36 densely-genotyped BMI loci on the MetaboChip replicated/generalized in trans-ethnic analyses. We observed multiple signals at nine loci, including the description of seven loci with novel multiple signals. This study supports the generalization of most common genetic loci to diverse ancestral populations and emphasizes the importance of dense multiethnic genomic data in refining the functional variation at genetic loci of interest and describing several loci with multiple underlying genetic variants.},\n\tauthor = {Fern{\\'a}ndez-Rhodes, Lindsay and Gong, Jian and Haessler, Jeffrey and Franceschini, Nora and Graff, Mariaelisa and Nishimura, Katherine K. and Wang, Yujie and Highland, Heather M. and Yoneyama, Sachiko and Bush, William S. and Goodloe, Robert and Ritchie, Marylyn D. and Crawford, Dana and Gross, Myron and Fornage, Myriam and Buzkova, Petra and Tao, Ran and Isasi, Carmen and Avil{\\'e}s-Santa, Larissa and Daviglus, Martha and Mackey, Rachel H. and Houston, Denise and Gu, C. Charles and Ehret, Georg and Nguyen, Khanh-Dung H. and Lewis, Cora E. and Leppert, Mark and Irvin, Marguerite R. and Lim, Unhee and Haiman, Christopher A. and Le Marchand, Loic and Schumacher, Fredrick and Wilkens, Lynne and Lu, Yingchang and Bottinger, Erwin P. and Loos, Ruth J. L. and Sheu, Wayne H.-H. and Guo, Xiuqing and Lee, Wen-Jane and Hai, Yang and Hung, Yi-Jen and Absher, Devin and Wu, I.-Chien and Taylor, Kent D. and Lee, I.-Te and Liu, Yeheng and Wang, Tzung-Dau and Quertermous, Thomas and Juang, Jyh-Ming J. and Rotter, Jerome I. and Assimes, Themistocles and Hsiung, Chao A. and Chen, Yii-Der Ida and Prentice, Ross and Kuller, Lewis H. and Manson, JoAnn E. and Kooperberg, Charles and Smokowski, Paul and Robinson, Whitney R. and Gordon-Larsen, Penny and Li, Rongling and Hindorff, Lucia and Buyske, Steven and Matise, Tara C. and Peters, Ulrike and North, Kari E.},\n\tcitation-subset = {IM},\n\tcompleted = {2017-06-22},\n\tcountry = {Germany},\n\tdoi = {10.1007/s00439-017-1787-6},\n\tissn = {1432-1203},\n\tissn-linking = {0340-6717},\n\tissue = {6},\n\tjournal = {Human genetics},\n\tkeywords = {Body Mass Index; Ethnic Groups, genetics; Genetics, Population; Humans; Obesity, epidemiology, genetics},\n\tmid = {NIHMS866991},\n\tmonth = jun,\n\tnlm-id = {7613873},\n\towner = {NLM},\n\tpages = {771--800},\n\tpii = {10.1007/s00439-017-1787-6},\n\tpmc = {PMC5485655},\n\tpmid = {28391526},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/28391526/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2020-04-21},\n\ttitle = {Trans-ethnic fine-mapping of genetic loci for body mass index in the diverse ancestral populations of the {Population Architecture using Genomics and Epidemiology} ({PAGE}) Study reveals evidence for multiple signals at established loci.},\n\tvolume = {136},\n\tyear = {2017},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/28391526/},\n\tbdsk-url-2 = {https://doi.org/10.1007/s00439-017-1787-6}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Fine mapping of QT interval regions in global populations refines previously identified QT interval loci and identifies signals unique to African and Hispanic descent populations.\n \n \n \n \n\n\n \n Avery, C. L.; Wassel, C. L.; Richard, M. A.; Highland, H. M.; Bien, S.; Zubair, N.; Soliman, E. Z.; Fornage, M.; Bielinski, S. J.; Tao, R.; Seyerle, A. A.; Shah, S. J.; Lloyd-Jones, D. M.; Buyske, S.; Rotter, J. I.; Post, W. S.; Rich, S. S.; Hindorff, L. A.; Jeff, J. M.; Shohet, R. V.; Sotoodehnia, N.; Lin, D. Y.; Whitsel, E. A.; Peters, U.; Haiman, C. A.; Crawford, D. C.; Kooperberg, C.; and North, K. E.\n\n\n \n\n\n\n Heart rhythm, 14: 572–580. April 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Fine$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{AveryWasselRichardEtAl2017,\n\tabstract = {The electrocardiographically measured {QT} interval ({QT}) is heritable and its prolongation is an established risk factor for several cardiovascular diseases. Yet, most {QT} genetic studies have been performed in European ancestral populations, possibly reducing their global relevance. To leverage diversity and improve biological insight, we fine mapped 16 of the 35 previously identified {QT} loci (46%) in populations of African American (n = 12,410) and Hispanic/Latino (n = 14,837) ancestry. Racial/ethnic-specific multiple linear regression analyses adjusted for heart rate and clinical covariates were examined separately and in combination after inverse-variance weighted trans-ethnic meta-analysis. The 16 fine-mapped {QT} loci included on the Illumina Metabochip represented 21 independent signals, of which 16 (76%) were significantly (P-value≤9.1×10 ) associated with {QT}. Through sequential conditional analysis we also identified three trans-ethnic novel SNPs at ATP1B1, SCN5A-SCN10A, and KCNQ1 and three Hispanic/Latino-specific novel SNPs at NOS1AP and SCN5A-SCN10A (two novel SNPs) with evidence of associations with {QT} independent of previous identified {GWAS} lead SNPs. Linkage disequilibrium patterns helped to narrow the region likely to contain the functional variants at several loci, including NOS1AP, USP50-TRPM7, and PRKCA, although intervals surrounding SLC35F1-PLN and CNOT1 remained broad in size (>100 kb). Finally, bioinformatics-based functional characterization suggested a regulatory function in cardiac tissues for the majority of independent signals that generalized and the novel SNPs. Our findings suggest that a majority of identified SNPs implicate gene regulatory dysfunction in {QT} prolongation, that the same loci influence variation in {QT} across global populations, and that additional, novel, population-specific {QT} signals exist.},\n\tauthor = {Avery, Christy L. and Wassel, Christina L. and Richard, Melissa A. and Highland, Heather M. and Bien, Stephanie and Zubair, Niha and Soliman, Elsayed Z. and Fornage, Myriam and Bielinski, Suzette J. and Tao, Ran and Seyerle, Amanda A. and Shah, Sanjiv J. and Lloyd-Jones, Donald M. and Buyske, Steven and Rotter, Jerome I. and Post, Wendy S. and Rich, Stephen S. and Hindorff, Lucia A. and Jeff, Janina M. and Shohet, Ralph V. and Sotoodehnia, Nona and Lin, Dan Yu and Whitsel, Eric A. and Peters, Ulrike and Haiman, Christopher A. and Crawford, Dana C. and Kooperberg, Charles and North, Kari E.},\n\tcitation-subset = {IM},\n\tcompleted = {2018-01-24},\n\tcountry = {United States},\n\tdoi = {10.1016/j.hrthm.2016.12.021},\n\tissn = {1556-3871},\n\tissn-linking = {1547-5271},\n\tissue = {4},\n\tjournal = {Heart rhythm},\n\tkeywords = {African Americans, genetics; Electrocardiography, methods; Genome-Wide Association Study; Heart Conduction System, physiology, physiopathology; Hispanic Americans, genetics; Humans; Linkage Disequilibrium; Long {QT} Syndrome, ethnology, genetics; Polymorphism, Single Nucleotide; Sequence Analysis; United States; African American; Electrocardiography; Fine mapping; Hispanic/Latino; {QT} interval},\n\tmid = {NIHMS855406},\n\tmonth = apr,\n\tnlm-id = {101200317},\n\towner = {NLM},\n\tpages = {572--580},\n\tpii = {S1547-5271(16)31212-7},\n\tpmc = {PMC5448160},\n\tpmid = {27988371},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/27988371/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2019-10-01},\n\ttitle = {Fine mapping of {QT} interval regions in global populations refines previously identified {QT} interval loci and identifies signals unique to {African} and {Hispanic} descent populations.},\n\tvolume = {14},\n\tyear = {2017},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/27988371/},\n\tbdsk-url-2 = {https://doi.org/10.1016/j.hrthm.2016.12.021}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Generalization and fine mapping of European ancestry-based central adiposity variants in African ancestry populations.\n \n \n \n \n\n\n \n Yoneyama, S.; Yao, J.; Guo, X.; Fernandez-Rhodes, L.; Lim, U.; Boston, J.; Buzková, P.; Carlson, C. S.; Cheng, I.; Cochran, B.; Cooper, R.; Ehret, G.; Fornage, M.; Gong, J.; Gross, M.; Gu, C. C.; Haessler, J.; Haiman, C. A.; Henderson, B.; Hindorff, L. A.; Houston, D.; Irvin, M. R.; Jackson, R.; Kuller, L.; Leppert, M.; Lewis, C. E.; Li, R.; Le Marchand, L.; Matise, T. C.; Nguyen, K.; Chakravarti, A.; Pankow, J. S.; Pankratz, N.; Pooler, L.; Ritchie, M. D.; Bien, S. A.; Wassel, C. L.; Chen, Y. I.; Taylor, K. D.; Allison, M.; Rotter, J. I.; Schreiner, P. J.; Schumacher, F.; Wilkens, L.; Boerwinkle, E.; Kooperberg, C.; Peters, U.; Buyske, S.; Graff, M.; and North, K. E.\n\n\n \n\n\n\n International journal of obesity (2005), 41: 324–331. February 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Generalization$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{YoneyamaYaoGuoEtAl2017,\n\tabstract = {Central adiposity measures such as waist circumference (WC) and waist-to-hip ratio (WHR) are associated with cardiometabolic disorders independently of body mass index (BMI) and are gaining clinically utility. Several studies report genetic variants associated with central adiposity, but most utilize only European ancestry populations. Understanding whether the genetic associations discovered among mainly European descendants are shared with African ancestry populations will help elucidate the biological underpinnings of abdominal fat deposition. To identify the underlying functional genetic determinants of body fat distribution, we conducted an array-wide association meta-analysis among persons of African ancestry across seven studies/consortia participating in the {Population Architecture using Genomics and Epidemiology} (PAGE) consortium. We used the Metabochip array, designed for fine-mapping cardiovascular-associated loci, to explore novel array-wide associations with WC and WHR among 15 945 African descendants using all and sex-stratified groups. We further interrogated 17 known WHR regions for African ancestry-specific variants. Of the 17 WHR loci, eight single-nucleotide polymorphisms (SNPs) located in four loci were replicated in the sex-combined or sex-stratified meta-analyses. Two of these eight independently associated with WHR after conditioning on the known variant in European descendants (rs12096179 in TBX15-WARS2 and rs2059092 in ADAMTS9). In the fine-mapping assessment, the putative functional region was reduced across all four loci but to varying degrees (average 40% drop in number of putative SNPs and 20% drop in genomic region). Similar to previous studies, the significant SNPs in the female-stratified analysis were stronger than the significant SNPs from the sex-combined analysis. No novel associations were detected in the array-wide analyses. Of 17 previously identified loci, four loci replicated in the African ancestry populations of this study. Utilizing different linkage disequilibrium patterns observed between European and African ancestries, we narrowed the suggestive region containing causative variants for all four loci.},\n\tauthor = {Yoneyama, S. and Yao, J. and Guo, X. and Fernandez-Rhodes, L. and Lim, U. and Boston, J. and Buzkov{\\'a}, P. and Carlson, C. S. and Cheng, I. and Cochran, B. and Cooper, R. and Ehret, G. and Fornage, M. and Gong, J. and Gross, M. and Gu, C. C. and Haessler, J. and Haiman, C. A. and Henderson, B. and Hindorff, L. A. and Houston, D. and Irvin, M. R. and Jackson, R. and Kuller, L. and Leppert, M. and Lewis, C. E. and Li, R. and Le Marchand, L. and Matise, T. C. and Nguyen, K.-Dh and Chakravarti, A. and Pankow, J. S. and Pankratz, N. and Pooler, L. and Ritchie, M. D. and Bien, S. A. and Wassel, C. L. and Chen, Y.-D. I. and Taylor, K. D. and Allison, M. and Rotter, J. I. and Schreiner, P. J. and Schumacher, F. and Wilkens, L. and Boerwinkle, E. and Kooperberg, C. and Peters, U. and Buyske, S. and Graff, M. and North, K. E.},\n\tcitation-subset = {IM},\n\tcompleted = {2018-02-19},\n\tcountry = {England},\n\tdoi = {10.1038/ijo.2016.207},\n\tissn = {1476-5497},\n\tissn-linking = {0307-0565},\n\tissue = {2},\n\tjournal = {International journal of obesity (2005)},\n\tkeywords = {Adiposity, genetics; Adult; African Continental Ancestry Group, genetics; Body Fat Distribution; European Continental Ancestry Group, genetics; Female; Genetic Predisposition to Disease, ethnology; Genetic Variation; Genome-Wide Association Study; Genotype; Humans; Male; Obesity, Abdominal, ethnology, genetics; Polymorphism, Single Nucleotide, genetics; Waist-Hip Ratio},\n\tmid = {NIHMS825343},\n\tmonth = feb,\n\tnlm-id = {101256108},\n\towner = {NLM},\n\tpages = {324--331},\n\tpii = {ijo2016207},\n\tpmc = {PMC5296276},\n\tpmid = {27867202},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/27867202/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2019-12-20},\n\ttitle = {Generalization and fine mapping of European ancestry-based central adiposity variants in {African} ancestry populations.},\n\tvolume = {41},\n\tyear = {2017},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/27867202/},\n\tbdsk-url-2 = {https://doi.org/10.1038/ijo.2016.207}}\n\n

\n\n\n

\n Central adiposity measures such as waist circumference (WC) and waist-to-hip ratio (WHR) are associated with cardiometabolic disorders independently of body mass index (BMI) and are gaining clinically utility. Several studies report genetic variants associated with central adiposity, but most utilize only European ancestry populations. Understanding whether the genetic associations discovered among mainly European descendants are shared with African ancestry populations will help elucidate the biological underpinnings of abdominal fat deposition. To identify the underlying functional genetic determinants of body fat distribution, we conducted an array-wide association meta-analysis among persons of African ancestry across seven studies/consortia participating in the Population Architecture using Genomics and Epidemiology (PAGE) consortium. We used the Metabochip array, designed for fine-mapping cardiovascular-associated loci, to explore novel array-wide associations with WC and WHR among 15 945 African descendants using all and sex-stratified groups. We further interrogated 17 known WHR regions for African ancestry-specific variants. Of the 17 WHR loci, eight single-nucleotide polymorphisms (SNPs) located in four loci were replicated in the sex-combined or sex-stratified meta-analyses. Two of these eight independently associated with WHR after conditioning on the known variant in European descendants (rs12096179 in TBX15-WARS2 and rs2059092 in ADAMTS9). In the fine-mapping assessment, the putative functional region was reduced across all four loci but to varying degrees (average 40% drop in number of putative SNPs and 20% drop in genomic region). Similar to previous studies, the significant SNPs in the female-stratified analysis were stronger than the significant SNPs from the sex-combined analysis. No novel associations were detected in the array-wide analyses. Of 17 previously identified loci, four loci replicated in the African ancestry populations of this study. Utilizing different linkage disequilibrium patterns observed between European and African ancestries, we narrowed the suggestive region containing causative variants for all four loci.\n

\n\n\n

\n \n\n \n \n \n \n \n \n Correction to: Transethnic insight into the genetics of glycaemic traits: fine-mapping results from the Population Architecture using Genomics and Epidemiology (PAGE) consortium.\n \n \n \n \n\n\n \n Bien, S. A.; Pankow, J. S.; Haessler, J.; Lu, Y.; Pankratz, N.; Rohde, R. R.; Tamuno, A.; Carlson, C. S.; Schumacher, F. R.; B ̊u ̌zková, P.; Daviglus, M. L.; Lim, U.; Fornage, M.; Fernandez-Rhodes, L.; Avilés-Santa, L.; Buyske, S.; Gross, M. D.; Graff, M.; Isasi, C. R.; Kuller, L. H.; Manson, J. E.; Matise, T. C.; Prentice, R. L.; Wilkens, L. R.; Yoneyama, S.; Loos, R. J. F.; Hindorff, L. A.; Le Marchand, L.; North, K. E.; Haiman, C. A.; Peters, U.; and Kooperberg, C.\n\n\n \n\n\n\n Diabetologia, 60: 2542–2543. December 2017.\n \n\n\n\n
\n\n\n\n \n \n $\"Correction$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{BienPankowHaesslerEtAl2017b,\n\tabstract = {The authors regret that Yinchang Lu's name incorrectly included a middle initial in the author list. The details given in this erratum are correct.},\n\tauthor = {Bien, Stephanie A. and Pankow, James S. and Haessler, Jeffrey and Lu, Yinchang and Pankratz, Nathan and Rohde, Rebecca R. and Tamuno, Alfred and Carlson, Christopher S. and Schumacher, Fredrick R. and B{\\r u}{\\v z}kov{\\'a}, Petra and Daviglus, Martha L. and Lim, Unhee and Fornage, Myriam and Fernandez-Rhodes, Lindsay and Avil{\\'e}s-Santa, Larissa and Buyske, Steven and Gross, Myron D. and Graff, Mariaelisa and Isasi, Carmen R. and Kuller, Lewis H. and Manson, JoAnn E. and Matise, Tara C. and Prentice, Ross L. and Wilkens, Lynne R. and Yoneyama, Sachiko and Loos, Ruth J. F. and Hindorff, Lucia A. and Le Marchand, Loic and North, Kari E. and Haiman, Christopher A. and Peters, Ulrike and Kooperberg, Charles},\n\tcountry = {Germany},\n\tdoi = {10.1007/s00125-017-4476-z},\n\tissn = {1432-0428},\n\tissn-linking = {0012-186X},\n\tissue = {12},\n\tjournal = {Diabetologia},\n\tmid = {NIHMS982666},\n\tmonth = dec,\n\tnlm-id = {0006777},\n\towner = {NLM},\n\tpages = {2542--2543},\n\tpii = {10.1007/s00125-017-4476-z},\n\tpmc = {PMC6145818},\n\tpmid = {29038867},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/29038867/},\n\n\tpubmodel = {Print},\n\tpubstate = {ppublish},\n\trevised = {2020-05-11},\n\ttitle = {Correction to: Transethnic insight into the genetics of glycaemic traits: fine-mapping results from the {Population Architecture using Genomics and Epidemiology} ({PAGE}) consortium.},\n\tvolume = {60},\n\tyear = {2017},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/29038867/},\n\tbdsk-url-2 = {https://doi.org/10.1007/s00125-017-4476-z}}\n\n

\n\n\n\n\n\n

\n\n

\n \n 2016\n \n \n (6)\n \n \n

\n \n \n

\n \n\n \n \n \n \n \n \n Ethnicity: Diversity is future for genetic analysis.\n \n \n \n \n\n\n \n Carlson, C. S.\n\n\n \n\n\n\n Nature, 540: 341. December 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Ethnicity:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

@article{Carlson2016,\n\tauthor = {Carlson, Christopher S.},\n\tcitation-subset = {IM},\n\tcompleted = {2017-03-31},\n\tcountry = {England},\n\tdoi = {10.1038/540341d},\n\tissn = {1476-4687},\n\tissn-linking = {0028-0836},\n\tissue = {7633},\n\tjournal = {Nature},\n\tkeywords = {Continental Population Groups, genetics; Ethnic Groups, genetics; Forecasting; Genetic Testing; Genetic Variation; Humans},\n\tmonth = dec,\n\tnlm-id = {0410462},\n\towner = {NLM},\n\tpages = {341},\n\tpii = {540341d},\n\tpmid = {27974770},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/27974770/},\n\n\tpubmodel = {Print},\n\tpubstate = {ppublish},\n\trevised = {2018-12-02},\n\ttitle = {Ethnicity: Diversity is future for genetic analysis.},\n\tvolume = {540},\n\tyear = {2016},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/27974770/},\n\tbdsk-url-2 = {https://doi.org/10.1038/540341d}}\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Strategies for Enriching Variant Coverage in Candidate Disease Loci on a Multiethnic Genotyping Array.\n \n \n \n \n\n\n \n Bien, S. A.; Wojcik, G. L.; Zubair, N.; Gignoux, C. R.; Martin, A. R.; Kocarnik, J. M.; Martin, L. W.; Buyske, S.; Haessler, J.; Walker, R. W.; Cheng, I.; Graff, M.; Xia, L.; Franceschini, N.; Matise, T.; James, R.; Hindorff, L.; Le Marchand, L.; North, K. E.; Haiman, C. A.; Peters, U.; Loos, R. J. F.; Kooperberg, C. L.; Bustamante, C. D.; Kenny, E. E.; Carlson, C. S.; and Study, P. A. G. E.\n\n\n \n\n\n\n PloS one, 11: e0167758. 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Strategies$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{BienWojcikZubairEtAl2016,\n\tabstract = {Investigating genetic architecture of complex traits in ancestrally diverse populations is imperative to understand the etiology of disease. However, the current paucity of genetic research in people of African and Latin American ancestry, Hispanic and indigenous peoples in the United States is likely to exacerbate existing health disparities for many common diseases. The {Population Architecture using Genomics and Epidemiology}, Phase II (PAGE II), Study was initiated in 2013 by the National Human Genome Research Institute to expand our understanding of complex trait loci in ethnically diverse and well characterized study populations. To meet this goal, the Multi-Ethnic Genotyping Array (MEGA) was designed to substantially improve fine-mapping and functional discovery by increasing variant coverage across multiple ethnicities at known loci for metabolic, cardiovascular, renal, inflammatory, anthropometric, and a variety of lifestyle traits. Studying the frequency distribution of clinically relevant mutations, putative risk alleles, and known functional variants across multiple populations will provide important insight into the genetic architecture of complex diseases and facilitate the discovery of novel, sometimes population-specific, disease associations. DNA samples from 51,650 self-identified African ancestry (17,328), Hispanic/Latino (22,379), Asian/Pacific Islander (8,640), and American Indian (653) and an additional 2,650 participants of either South Asian or European ancestry, and other reference panels have been genotyped on MEGA by PAGE II. MEGA was designed as a new resource for studying ancestrally diverse populations. Here, we describe the methodology for selecting trait-specific content for use in multi-ethnic populations and how enriching MEGA for this content may contribute to deeper biological understanding of the genetic etiology of complex disease.},\n\tauthor = {Bien, Stephanie A. and Wojcik, Genevieve L. and Zubair, Niha and Gignoux, Christopher R. and Martin, Alicia R. and Kocarnik, Jonathan M. and Martin, Lisa W. and Buyske, Steven and Haessler, Jeffrey and Walker, Ryan W. and Cheng, Iona and Graff, Mariaelisa and Xia, Lucy and Franceschini, Nora and Matise, Tara and James, Regina and Hindorff, Lucia and Le Marchand, Loic and North, Kari E. and Haiman, Christopher A. and Peters, Ulrike and Loos, Ruth J. F. and Kooperberg, Charles L. and Bustamante, Carlos D. and Kenny, Eimear E. and Carlson, Christopher S. and Study, P. A. G. E.},\n\tcitation-subset = {IM},\n\tcompleted = {2017-07-18},\n\tcountry = {United States},\n\tdoi = {10.1371/journal.pone.0167758},\n\tissn = {1932-6203},\n\tissn-linking = {1932-6203},\n\tissue = {12},\n\tjournal = {PloS one},\n\tkeywords = {Alleles; Anthropometry; Chromosome Mapping; Ethnic Groups, genetics; Exome; Female; Genetic Variation; Genome, Human; Genome-Wide Association Study; Genomics, methods; Genotype; Humans; Male; Mutation; United States},\n\tnlm-id = {101285081},\n\towner = {NLM},\n\tpages = {e0167758},\n\tpii = {PONE-D-16-31376},\n\tpmc = {PMC5156387},\n\tpmid = {27973554},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/27973554/},\n\n\tpubmodel = {Electronic-eCollection},\n\tpubstate = {epublish},\n\trevised = {2019-10-01},\n\ttitle = {Strategies for Enriching Variant Coverage in Candidate Disease Loci on a Multiethnic Genotyping Array.},\n\tvolume = {11},\n\tyear = {2016},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/27973554/},\n\tbdsk-url-2 = {https://doi.org/10.1371/journal.pone.0167758}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Fine-mapping, novel loci identification, and SNP association transferability in a genome-wide association study of QRS duration in African Americans.\n \n \n \n \n\n\n \n Evans, D. S.; Avery, C. L.; Nalls, M. A.; Li, G.; Barnard, J.; Smith, E. N.; Tanaka, T.; Butler, A. M.; Buxbaum, S. G.; Alonso, A.; Arking, D. E.; Berenson, G. S.; Bis, J. C.; Buyske, S.; Carty, C. L.; Chen, W.; Chung, M. K.; Cummings, S. R.; Deo, R.; Eaton, C. B.; Fox, E. R.; Heckbert, S. R.; Heiss, G.; Hindorff, L. A.; Hsueh, W.; Isaacs, A.; Jamshidi, Y.; Kerr, K. F.; Liu, F.; Liu, Y.; Lohman, K. K.; Magnani, J. W.; Maher, J. F.; Mehra, R.; Meng, Y. A.; Musani, S. K.; Newton-Cheh, C.; North, K. E.; Psaty, B. M.; Redline, S.; Rotter, J. I.; Schnabel, R. B.; Schork, N. J.; Shohet, R. V.; Singleton, A. B.; Smith, J. D.; Soliman, E. Z.; Srinivasan, S. R.; Taylor, H. A.; Van Wagoner, D. R.; Wilson, J. G.; Young, T.; Zhang, Z.; Zonderman, A. B.; Evans, M. K.; Ferrucci, L.; Murray, S. S.; Tranah, G. J.; Whitsel, E. A.; Reiner, A. P.; CHARGE QRS Consortium; and Sotoodehnia, N.\n\n\n \n\n\n\n Human molecular genetics, 25: 4350–4368. October 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Fine-mapping,$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{EvansAveryNallsEtAl2016,\n\tabstract = {The electrocardiographic QRS duration, a measure of ventricular depolarization and conduction, is associated with cardiovascular mortality. While single nucleotide polymorphisms (SNPs) associated with QRS duration have been identified at 22 loci in populations of European descent, the genetic architecture of QRS duration in non-European populations is largely unknown. We therefore performed a genome-wide association study ({GWAS}) meta-analysis of QRS duration in 13,031 African Americans from ten cohorts and a transethnic {GWAS} meta-analysis with additional results from populations of European descent. In the African American {GWAS}, a single genome-wide significant {SNP} association was identified (rs3922844, P = 4 × 10 ) in intron 16 of SCN5A, a voltage-gated cardiac sodium channel gene. The QRS-prolonging rs3922844 C allele was also associated with decreased SCN5A RNA expression in human atrial tissue (P = 1.1 × 10 ). High density genotyping revealed that the SCN5A association region in African Americans was confined to intron 16. Transethnic {GWAS} meta-analysis identified novel {SNP} associations on chromosome 18 in MYL12A (rs1662342, P = 4.9 × 10 ) and chromosome 1 near CD1E and SPTA1 (rs7547997, P = 7.9 × 10 ). The 22 QRS loci previously identified in populations of European descent were enriched for significant {SNP} associations with QRS duration in African Americans (P = 9.9 × 10 ), and index {SNP} associations in or near SCN5A, SCN10A, CDKN1A, NFIA, HAND1, TBX5 and SETBP1 replicated in African Americans. In summary, rs3922844 was associated with QRS duration and SCN5A expression, two novel QRS loci were identified using transethnic meta-analysis, and a significant proportion of QRS-{SNP} associations discovered in populations of European descent were transferable to African Americans when adequate power was achieved.},\n\tauthor = {Evans, Daniel S. and Avery, Christy L. and Nalls, Mike A. and Li, Guo and Barnard, John and Smith, Erin N. and Tanaka, Toshiko and Butler, Anne M. and Buxbaum, Sarah G. and Alonso, Alvaro and Arking, Dan E. and Berenson, Gerald S. and Bis, Joshua C. and Buyske, Steven and Carty, Cara L. and Chen, Wei and Chung, Mina K. and Cummings, Steven R. and Deo, Rajat and Eaton, Charles B. and Fox, Ervin R. and Heckbert, Susan R. and Heiss, Gerardo and Hindorff, Lucia A. and Hsueh, Wen-Chi and Isaacs, Aaron and Jamshidi, Yalda and Kerr, Kathleen F. and Liu, Felix and Liu, Yongmei and Lohman, Kurt K. and Magnani, Jared W. and Maher, Joseph F. and Mehra, Reena and Meng, Yan A. and Musani, Solomon K. and Newton-Cheh, Christopher and North, Kari E. and Psaty, Bruce M. and Redline, Susan and Rotter, Jerome I. and Schnabel, Renate B. and Schork, Nicholas J. and Shohet, Ralph V. and Singleton, Andrew B. and Smith, Jonathan D. and Soliman, Elsayed Z. and Srinivasan, Sathanur R. and Taylor, Herman A. and Van Wagoner, David R. and Wilson, James G. and Young, Taylor and Zhang, Zhu-Ming and Zonderman, Alan B. and Evans, Michele K. and Ferrucci, Luigi and Murray, Sarah S. and Tranah, Gregory J. and Whitsel, Eric A. and Reiner, Alex P. and {CHARGE QRS Consortium} and Sotoodehnia, Nona},\n\tchemicals = {NAV1.5 Voltage-Gated Sodium Channel, SCN5A protein, human},\n\tcitation-subset = {IM},\n\tcompleted = {2017-06-09},\n\tcountry = {England},\n\tdoi = {10.1093/hmg/ddw284},\n\tissn = {1460-2083},\n\tissn-linking = {0964-6906},\n\tissue = {19},\n\tjournal = {Human molecular genetics},\n\tkeywords = {African Americans, genetics; Alleles; Cardiovascular Diseases, genetics, mortality, physiopathology; Electrocardiography; European Continental Ancestry Group, genetics; Female; Genome-Wide Association Study; Genotype; Heart Ventricles, physiopathology; Humans; Male; Myocardium, pathology; NAV1.5 Voltage-Gated Sodium Channel, genetics; Polymorphism, Single Nucleotide, genetics},\n\tmonth = oct,\n\tnlm-id = {9208958},\n\towner = {NLM},\n\tpages = {4350--4368},\n\tpii = {ddw284},\n\tpmc = {PMC5291202},\n\tpmid = {27577874},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/27577874/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2020-06-14},\n\ttitle = {Fine-mapping, novel loci identification, and {SNP} association transferability in a genome-wide association study of {QRS} duration in {African Americans}.},\n\tvolume = {25},\n\tyear = {2016},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/27577874/},\n\tbdsk-url-2 = {https://doi.org/10.1093/hmg/ddw284}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Variant Discovery and Fine Mapping of Genetic Loci Associated with Blood Pressure Traits in Hispanics and African Americans.\n \n \n \n \n\n\n \n Franceschini, N.; Carty, C. L.; Lu, Y.; Tao, R.; Sung, Y. J.; Manichaikul, A.; Haessler, J.; Fornage, M.; Schwander, K.; Zubair, N.; Bien, S.; Hindorff, L. A.; Guo, X.; Bielinski, S. J.; Ehret, G.; Kaufman, J. D.; Rich, S. S.; Carlson, C. S.; Bottinger, E. P.; North, K. E.; Rao, D. C.; Chakravarti, A.; Barrett, P. Q.; Loos, R. J. F.; Buyske, S.; and Kooperberg, C.\n\n\n \n\n\n\n PloS one, 11: e0164132. 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Variant$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{FranceschiniCartyLuEtAl2016,\n\tabstract = {Despite the substantial burden of hypertension in US minority populations, few genetic studies of blood pressure have been conducted in Hispanics and African Americans, and it is unclear whether many of the established loci identified in European-descent populations contribute to blood pressure variation in non-European descent populations. Using the Metabochip array, we sought to characterize the genetic architecture of previously identified blood pressure loci, and identify novel cardiometabolic variants related to systolic and diastolic blood pressure in a multi-ethnic US population including Hispanics (n = 19,706) and African Americans (n = 18,744). Several known blood pressure loci replicated in African Americans and Hispanics. Fourteen variants in three loci (KCNK3, FGF5, ATXN2-SH2B3) were significantly associated with blood pressure in Hispanics. The most significant diastolic blood pressure variant identified in our analysis, rs2586886/KCNK3 (P = 5.2 x 10-9), also replicated in independent Hispanic and European-descent samples. African American and trans-ethnic meta-analysis data identified novel variants in the FGF5, ULK4 and HOXA-EVX1 loci, which have not been previously associated with blood pressure traits. Our identification and independent replication of variants in KCNK3, a gene implicated in primary hyperaldosteronism, as well as a variant in HOTTIP (HOXA-EVX1) suggest that further work to clarify the roles of these genes may be warranted. Overall, our findings suggest that loci identified in European descent populations also contribute to blood pressure variation in diverse populations including Hispanics and African Americans-populations that are understudied for hypertension genetic risk factors.},\n\tauthor = {Franceschini, Nora and Carty, Cara L. and Lu, Yingchang and Tao, Ran and Sung, Yun Ju and Manichaikul, Ani and Haessler, Jeff and Fornage, Myriam and Schwander, Karen and Zubair, Niha and Bien, Stephanie and Hindorff, Lucia A. and Guo, Xiuqing and Bielinski, Suzette J. and Ehret, Georg and Kaufman, Joel D. and Rich, Stephen S. and Carlson, Christopher S. and Bottinger, Erwin P. and North, Kari E. and Rao, D. C. and Chakravarti, Aravinda and Barrett, Paula Q. and Loos, Ruth J. F. and Buyske, Steven and Kooperberg, Charles},\n\tchemicals = {Nerve Tissue Proteins, Potassium Channels, Tandem Pore Domain, RNA, Long Noncoding, long noncoding RNA HOTTIP, human, potassium channel subfamily K member 3},\n\tcitation-subset = {IM},\n\tcompleted = {2017-05-19},\n\tcountry = {United States},\n\tdoi = {10.1371/journal.pone.0164132},\n\tissn = {1932-6203},\n\tissn-linking = {1932-6203},\n\tissue = {10},\n\tjournal = {PloS one},\n\tkeywords = {African Americans, genetics; Blood Pressure, genetics; Genetic Variation; Genome-Wide Association Study, methods; Hispanic Americans, genetics; Humans; Nerve Tissue Proteins, genetics; Potassium Channels, Tandem Pore Domain, genetics; Quantitative Trait Loci; RNA, Long Noncoding, genetics},\n\tnlm-id = {101285081},\n\towner = {NLM},\n\tpages = {e0164132},\n\tpii = {PONE-D-16-12505},\n\tpmc = {PMC5063457},\n\tpmid = {27736895},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/27736895/},\n\n\tpubmodel = {Electronic-eCollection},\n\tpubstate = {epublish},\n\trevised = {2019-12-22},\n\ttitle = {Variant Discovery and Fine Mapping of Genetic Loci Associated with Blood Pressure Traits in {Hispanics} and {African Americans}.},\n\tvolume = {11},\n\tyear = {2016},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/27736895/},\n\tbdsk-url-2 = {https://doi.org/10.1371/journal.pone.0164132}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Fine-mapping of lipid regions in global populations discovers ethnic-specific signals and refines previously identified lipid loci.\n \n \n \n \n\n\n \n Zubair, N.; Graff, M.; Luis Ambite, J.; Bush, W. S.; Kichaev, G.; Lu, Y.; Manichaikul, A.; Sheu, W. H.; Absher, D.; Assimes, T. L.; Bielinski, S. J.; Bottinger, E. P.; Buzkova, P.; Chuang, L.; Chung, R.; Cochran, B.; Dumitrescu, L.; Gottesman, O.; Haessler, J. W.; Haiman, C.; Heiss, G.; Hsiung, C. A.; Hung, Y.; Hwu, C.; Juang, J. J.; Le Marchand, L.; Lee, I.; Lee, W.; Lin, L.; Lin, D.; Lin, S.; Mackey, R. H.; Martin, L. W.; Pasaniuc, B.; Peters, U.; Predazzi, I.; Quertermous, T.; Reiner, A. P.; Robinson, J.; Rotter, J. I.; Ryckman, K. K.; Schreiner, P. J.; Stahl, E.; Tao, R.; Tsai, M. Y.; Waite, L. L.; Wang, T.; Buyske, S.; Ida Chen, Y.; Cheng, I.; Crawford, D. C.; Loos, R. J. F.; Rich, S. S.; Fornage, M.; North, K. E.; Kooperberg, C.; and Carty, C. L.\n\n\n \n\n\n\n Human molecular genetics, 25: 5500–5512. December 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Fine-mapping$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{ZubairGraffLuisAmbiteEtAl2016,\n\tabstract = {Genome-wide association studies have identified over 150 loci associated with lipid traits, however, no large-scale studies exist for Hispanics and other minority populations. Additionally, the genetic architecture of lipid-influencing loci remains largely unknown. We performed one of the most racially/ethnically diverse fine-mapping genetic studies of HDL-C, LDL-C, and triglycerides to-date using SNPs on the MetaboChip array on 54,119 individuals: 21,304 African Americans, 19,829 Hispanic Americans, 12,456 Asians, and 530 American Indians. The majority of signals found in these groups generalize to European Americans. While we uncovered signals unique to racial/ethnic populations, we also observed systematically consistent lipid associations across these groups. In African Americans, we identified three novel signals associated with HDL-C (LPL, APOA5, LCAT) and two associated with LDL-C (ABCG8, DHODH). In addition, using this population, we refined the location for 16 out of the 58 known MetaboChip lipid loci. These results can guide tailored screening efforts, reveal population-specific responses to lipid-lowering medications, and aid in the development of new targeted drug therapies.},\n\tauthor = {Zubair, Niha and Graff, Mariaelisa and Luis Ambite, Jose and Bush, William S. and Kichaev, Gleb and Lu, Yingchang and Manichaikul, Ani and Sheu, Wayne H.-H. and Absher, Devin and Assimes, Themistocles L. and Bielinski, Suzette J. and Bottinger, Erwin P. and Buzkova, Petra and Chuang, Lee-Ming and Chung, Ren-Hua and Cochran, Barbara and Dumitrescu, Logan and Gottesman, Omri and Haessler, Jeffrey W. and Haiman, Christopher and Heiss, Gerardo and Hsiung, Chao A. and Hung, Yi-Jen and Hwu, Chii-Min and Juang, Jyh-Ming J. and Le Marchand, Loic and Lee, I.-Te and Lee, Wen-Jane and Lin, Li-An and Lin, Danyu and Lin, Shih-Yi and Mackey, Rachel H. and Martin, Lisa W. and Pasaniuc, Bogdan and Peters, Ulrike and Predazzi, Irene and Quertermous, Thomas and Reiner, Alex P. and Robinson, Jennifer and Rotter, Jerome I. and Ryckman, Kelli K. and Schreiner, Pamela J. and Stahl, Eli and Tao, Ran and Tsai, Michael Y. and Waite, Lindsay L. and Wang, Tzung-Dau and Buyske, Steven and Ida Chen, Yii-Der and Cheng, Iona and Crawford, Dana C. and Loos, Ruth J. F. and Rich, Stephen S. and Fornage, Myriam and North, Kari E. and Kooperberg, Charles and Carty, Cara L.},\n\tchemicals = {ABCG8 protein, human, APOA5 protein, human, ATP Binding Cassette Transporter, Subfamily G, Member 8, Apolipoprotein A-V, Cholesterol, HDL, Cholesterol, LDL, Lipids, Triglycerides, LPL protein, human, Lipoprotein Lipase},\n\tcitation-subset = {IM},\n\tcompleted = {2017-06-09},\n\tcountry = {England},\n\tdoi = {10.1093/hmg/ddw358},\n\tissn = {1460-2083},\n\tissn-linking = {0964-6906},\n\tissue = {24},\n\tjournal = {Human molecular genetics},\n\tkeywords = {ATP Binding Cassette Transporter, Subfamily G, Member 8, genetics; African Americans, genetics; Apolipoprotein A-V, genetics; Asian Continental Ancestry Group, genetics; Cholesterol, HDL, genetics; Cholesterol, LDL, genetics; Female; Genome-Wide Association Study; Hispanic Americans, genetics; Humans; Indians, North American, genetics; Lipids, genetics; Lipoprotein Lipase, genetics; Male; Triglycerides, genetics},\n\tmonth = dec,\n\tnlm-id = {9208958},\n\towner = {NLM},\n\tpages = {5500--5512},\n\tpii = {2595394},\n\tpmc = {PMC5721937},\n\tpmid = {28426890},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/28426890/},\n\n\tpubmodel = {Print},\n\tpubstate = {ppublish},\n\trevised = {2019-10-01},\n\ttitle = {Fine-mapping of lipid regions in global populations discovers ethnic-specific signals and refines previously identified lipid loci.},\n\tvolume = {25},\n\tyear = {2016},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/28426890/},\n\tbdsk-url-2 = {https://doi.org/10.1093/hmg/ddw358}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Population Stratification in the Context of Diverse Epidemiologic Surveys Sans Genome-Wide Data.\n \n \n \n \n\n\n \n Oetjens, M. T.; Brown-Gentry, K.; Goodloe, R.; Dilks, H. H.; and Crawford, D. C.\n\n\n \n\n\n\n Frontiers in genetics, 7: 76. 2016.\n \n\n\n\n
\n\n\n\n \n \n $\"Population$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@article{OetjensBrownGentryGoodloeEtAl2016,\n\tabstract = {Population stratification or confounding by genetic ancestry is a potential cause of false associations in genetic association studies. Estimation of and adjustment for genetic ancestry has become common practice thanks in part to the availability of ancestry informative markers on genome-wide association study ({GWAS}) arrays. While array data is now widespread, these data are not ubiquitous as several large epidemiologic and clinic-based studies lack genome-wide data. One such large epidemiologic-based study lacking genome-wide data accessible to investigators is the National Health and Nutrition Examination Surveys (NHANES), population-based cross-sectional surveys of Americans linked to demographic, health, and lifestyle data conducted by the Centers for Disease Control and Prevention. DNA samples (n = 14,998) were extracted from biospecimens from consented NHANES participants between 1991-1994 (NHANES III, phase 2) and 1999-2002 and represent three major self-identified racial/ethnic groups: non-Hispanic whites (n = 6,634), non-Hispanic blacks (n = 3,458), and Mexican Americans (n = 3,950). We as the Epidemiologic Architecture for Genes Linked to Environment study genotyped candidate gene and {GWAS}-identified index variants in NHANES as part of the larger {Population Architecture using Genomics and Epidemiology} I study for collaborative genetic association studies. To enable basic quality control such as estimation of genetic ancestry to control for population stratification in NHANES san genome-wide data, we outline here strategies that use limited genetic data to identify the markers optimal for characterizing genetic ancestry. From among 411 and 295 autosomal SNPs available in NHANES III and NHANES 1999-2002, we demonstrate that markers with ancestry information can be identified to estimate global ancestry. Despite limited resolution, global genetic ancestry is highly correlated with self-identified race for the majority of participants, although less so for ethnicity. Overall, the strategies outlined here for a large epidemiologic study can be applied to other datasets accessible for genotype-phenotype studies but are sans genome-wide data.},\n\tauthor = {Oetjens, Matthew T. and Brown-Gentry, Kristin and Goodloe, Robert and Dilks, Holli H. and Crawford, Dana C.},\n\tcompleted = {2016-05-20},\n\tcountry = {Switzerland},\n\tdoi = {10.3389/fgene.2016.00076},\n\tissn = {1664-8021},\n\tissn-linking = {1664-8021},\n\tjournal = {Frontiers in genetics},\n\tkeywords = {EAGLE; NHANES; cross-sectional; epidemiology; genetic epidemiology; global genetic ancestry; population stratification},\n\tnlm-id = {101560621},\n\towner = {NLM},\n\tpages = {76},\n\tpmc = {PMC4858524},\n\tpmid = {27200085},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/27200085/},\n\n\tpubmodel = {Electronic-eCollection},\n\tpubstate = {epublish},\n\trevised = {2020-09-28},\n\ttitle = {Population Stratification in the Context of Diverse Epidemiologic Surveys Sans Genome-Wide Data.},\n\tvolume = {7},\n\tyear = {2016},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/27200085/},\n\tbdsk-url-2 = {https://doi.org/10.3389/fgene.2016.00076}}\n\n

\n\n\n

\n Population stratification or confounding by genetic ancestry is a potential cause of false associations in genetic association studies. Estimation of and adjustment for genetic ancestry has become common practice thanks in part to the availability of ancestry informative markers on genome-wide association study (GWAS) arrays. While array data is now widespread, these data are not ubiquitous as several large epidemiologic and clinic-based studies lack genome-wide data. One such large epidemiologic-based study lacking genome-wide data accessible to investigators is the National Health and Nutrition Examination Surveys (NHANES), population-based cross-sectional surveys of Americans linked to demographic, health, and lifestyle data conducted by the Centers for Disease Control and Prevention. DNA samples (n = 14,998) were extracted from biospecimens from consented NHANES participants between 1991-1994 (NHANES III, phase 2) and 1999-2002 and represent three major self-identified racial/ethnic groups: non-Hispanic whites (n = 6,634), non-Hispanic blacks (n = 3,458), and Mexican Americans (n = 3,950). We as the Epidemiologic Architecture for Genes Linked to Environment study genotyped candidate gene and GWAS-identified index variants in NHANES as part of the larger Population Architecture using Genomics and Epidemiology I study for collaborative genetic association studies. To enable basic quality control such as estimation of genetic ancestry to control for population stratification in NHANES san genome-wide data, we outline here strategies that use limited genetic data to identify the markers optimal for characterizing genetic ancestry. From among 411 and 295 autosomal SNPs available in NHANES III and NHANES 1999-2002, we demonstrate that markers with ancestry information can be identified to estimate global ancestry. Despite limited resolution, global genetic ancestry is highly correlated with self-identified race for the majority of participants, although less so for ethnicity. Overall, the strategies outlined here for a large epidemiologic study can be applied to other datasets accessible for genotype-phenotype studies but are sans genome-wide data.\n

\n\n\n

\n\n\n\n\n\n

\n\n

\n \n 2015\n \n \n (3)\n \n \n

\n \n \n

\n \n\n \n \n \n \n \n \n Pleiotropic and sex-specific effects of cancer GWAS SNPs on melanoma risk in the Population Architecture using Genomics and Epidemiology (PAGE) study.\n \n \n \n \n\n\n \n Kocarnik, J. M.; Park, S. L.; Han, J.; Dumitrescu, L.; Cheng, I.; Wilkens, L. R.; Schumacher, F. R.; Kolonel, L.; Carlson, C. S.; Crawford, D. C.; Goodloe, R. J.; Dilks, H. H.; Baker, P.; Richardson, D.; Matise, T. C.; Ambite, J. L.; Song, F.; Qureshi, A. A.; Zhang, M.; Duggan, D.; Hutter, C.; Hindorff, L.; Bush, W. S.; Kooperberg, C.; Le Marchand, L.; and Peters, U.\n\n\n \n\n\n\n PloS one, 10: e0120491. 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Pleiotropic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{KocarnikParkHanEtAl2015,\n\tabstract = {Several regions of the genome show pleiotropic associations with multiple cancers. We sought to evaluate whether 181 single-nucleotide polymorphisms previously associated with various cancers in genome-wide association studies were also associated with melanoma risk. We evaluated 2,131 melanoma cases and 20,353 controls from three studies in the {Population Architecture using Genomics and Epidemiology} (PAGE) study (EAGLE-BioVU, MEC, WHI) and two collaborating studies (HPFS, NHS). Overall and sex-stratified analyses were performed across studies. We observed statistically significant associations with melanoma for two lung cancer SNPs in the TERT-CLPTM1L locus (Bonferroni-corrected p<2.8x10-4), replicating known pleiotropic effects at this locus. In sex-stratified analyses, we also observed a potential male-specific association between prostate cancer risk variant rs12418451 and melanoma risk (OR=1.22, p=8.0x10-4). No other variants in our study were associated with melanoma after multiple comparisons adjustment (p>2.8e-4). We provide confirmatory evidence of pleiotropic associations with melanoma for two SNPs previously associated with lung cancer, and provide suggestive evidence for a male-specific association with melanoma for prostate cancer variant rs12418451. This {SNP} is located near TPCN2, an ion transport gene containing SNPs which have been previously associated with hair pigmentation but not melanoma risk. Previous evidence provides biological plausibility for this association, and suggests a complex interplay between ion transport, pigmentation, and melanoma risk that may vary by sex. If confirmed, these pleiotropic relationships may help elucidate shared molecular pathways between cancers and related phenotypes.},\n\tauthor = {Kocarnik, Jonathan M. and Park, S. Lani and Han, Jiali and Dumitrescu, Logan and Cheng, Iona and Wilkens, Lynne R. and Schumacher, Fredrick R. and Kolonel, Laurence and Carlson, Chris S. and Crawford, Dana C. and Goodloe, Robert J. and Dilks, Holli H. and Baker, Paxton and Richardson, Danielle and Matise, Tara C. and Ambite, Jos{\\'e} Luis and Song, Fengju and Qureshi, Abrar A. and Zhang, Mingfeng and Duggan, David and Hutter, Carolyn and Hindorff, Lucia and Bush, William S. and Kooperberg, Charles and Le Marchand, Loic and Peters, Ulrike},\n\tchemicals = {CLPTM1L protein, human, Membrane Proteins, Neoplasm Proteins, TERT protein, human, Telomerase},\n\tcitation-subset = {IM},\n\tcompleted = {2015-12-15},\n\tcountry = {United States},\n\tdoi = {10.1371/journal.pone.0120491},\n\tissn = {1932-6203},\n\tissn-linking = {1932-6203},\n\tissue = {3},\n\tjournal = {PloS one},\n\tkeywords = {Aged; Aged, 80 and over; Alleles; Case-Control Studies; Female; Genetic Pleiotropy; Genome-Wide Association Study; Genotype; Humans; Lung Neoplasms, genetics, pathology; Male; Melanoma, genetics, pathology; Membrane Proteins, genetics; Metagenomics; Middle Aged; Neoplasm Proteins, genetics; Polymorphism, Single Nucleotide; Prostatic Neoplasms, genetics, pathology; Risk; Sex Factors; Skin Neoplasms, genetics, metabolism; Telomerase, genetics},\n\tnlm-id = {101285081},\n\towner = {NLM},\n\tpages = {e0120491},\n\tpii = {PONE-D-14-45087},\n\tpmc = {PMC4366224},\n\tpmid = {25789475},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/25789475/},\n\n\tpubmodel = {Electronic-eCollection},\n\tpubstate = {epublish},\n\trevised = {2018-11-13},\n\ttitle = {Pleiotropic and sex-specific effects of cancer {GWAS} {SNP}s on melanoma risk in the {Population Architecture using Genomics and Epidemiology} ({PAGE}) study.},\n\tvolume = {10},\n\tyear = {2015},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/25789475/},\n\tbdsk-url-2 = {https://doi.org/10.1371/journal.pone.0120491}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Genetic studies of body mass index yield new insights for obesity biology.\n \n \n \n \n\n\n \n Locke, A. E.; Kahali, B.; Berndt, S. I.; Justice, A. E.; Pers, T. H.; Day, F. R.; Powell, C.; Vedantam, S.; Buchkovich, M. L.; Yang, J.; Croteau-Chonka, D. C.; Esko, T.; Fall, T.; Ferreira, T.; Gustafsson, S.; Kutalik, Z.; Luan, J.; Mägi, R.; Randall, J. C.; Winkler, T. W.; Wood, A. R.; Workalemahu, T.; Faul, J. D.; Smith, J. A.; Zhao, J. H.; Zhao, W.; Chen, J.; Fehrmann, R.; Hedman, Å. K.; Karjalainen, J.; Schmidt, E. M.; Absher, D.; Amin, N.; Anderson, D.; Beekman, M.; Bolton, J. L.; Bragg-Gresham, J. L.; Buyske, S.; Demirkan, A.; Deng, G.; Ehret, G. B.; Feenstra, B.; Feitosa, M. F.; Fischer, K.; Goel, A.; Gong, J.; Jackson, A. U.; Kanoni, S.; Kleber, M. E.; Kristiansson, K.; Lim, U.; Lotay, V.; Mangino, M.; Leach, I. M.; Medina-Gomez, C.; Medland, S. E.; Nalls, M. A.; Palmer, C. D.; Pasko, D.; Pechlivanis, S.; Peters, M. J.; Prokopenko, I.; Shungin, D.; Stan ̌cáková, A.; Strawbridge, R. J.; Sung, Y. J.; Tanaka, T.; Teumer, A.; Trompet, S.; van der Laan, S. W.; van Setten, J.; Van Vliet-Ostaptchouk, J. V.; Wang, Z.; Yengo, L.; Zhang, W.; Isaacs, A.; Albrecht, E.; Ärnlöv, J.; Arscott, G. M.; Attwood, A. P.; Bandinelli, S.; Barrett, A.; Bas, I. N.; Bellis, C.; Bennett, A. J.; Berne, C.; Blagieva, R.; Blüher, M.; Böhringer, S.; Bonnycastle, L. L.; Böttcher, Y.; Boyd, H. A.; Bruinenberg, M.; Caspersen, I. H.; Chen, Y. I.; Clarke, R.; Daw, E. W.; de Craen, A. J. M.; Delgado, G.; Dimitriou, M.; Doney, A. S. F.; Eklund, N.; Estrada, K.; Eury, E.; Folkersen, L.; Fraser, R. M.; Garcia, M. E.; Geller, F.; Giedraitis, V.; Gigante, B.; Go, A. S.; Golay, A.; Goodall, A. H.; Gordon, S. D.; Gorski, M.; Grabe, H.; Grallert, H.; Grammer, T. B.; Gräßler, J.; Grönberg, H.; Groves, C. J.; Gusto, G.; Haessler, J.; Hall, P.; Haller, T.; Hallmans, G.; Hartman, C. A.; Hassinen, M.; Hayward, C.; Heard-Costa, N. L.; Helmer, Q.; Hengstenberg, C.; Holmen, O.; Hottenga, J.; James, A. L.; Jeff, J. M.; Johansson, Å.; Jolley, J.; Juliusdottir, T.; Kinnunen, L.; Koenig, W.; Koskenvuo, M.; Kratzer, W.; Laitinen, J.; Lamina, C.; Leander, K.; Lee, N. R.; Lichtner, P.; Lind, L.; Lindström, J.; Lo, K. S.; Lobbens, S.; Lorbeer, R.; Lu, Y.; Mach, F.; Magnusson, P. K. E.; Mahajan, A.; McArdle, W. L.; McLachlan, S.; Menni, C.; Merger, S.; Mihailov, E.; Milani, L.; Moayyeri, A.; Monda, K. L.; Morken, M. A.; Mulas, A.; Müller, G.; Müller-Nurasyid, M.; Musk, A. W.; Nagaraja, R.; Nöthen, M. M.; Nolte, I. M.; Pilz, S.; Rayner, N. W.; Renstrom, F.; Rettig, R.; Ried, J. S.; Ripke, S.; Robertson, N. R.; Rose, L. M.; Sanna, S.; Scharnagl, H.; Scholtens, S.; Schumacher, F. R.; Scott, W. R.; Seufferlein, T.; Shi, J.; Smith, A. V.; Smolonska, J.; Stanton, A. V.; Steinthorsdottir, V.; Stirrups, K.; Stringham, H. M.; Sundström, J.; Swertz, M. A.; Swift, A. J.; Syvänen, A.; Tan, S.; Tayo, B. O.; Thorand, B.; Thorleifsson, G.; Tyrer, J. P.; Uh, H.; Vandenput, L.; Verhulst, F. C.; Vermeulen, S. H.; Verweij, N.; Vonk, J. M.; Waite, L. L.; Warren, H. R.; Waterworth, D.; Weedon, M. N.; Wilkens, L. R.; Willenborg, C.; Wilsgaard, T.; Wojczynski, M. K.; Wong, A.; Wright, A. F.; Zhang, Q.; Study, L. C.; Brennan, E. P.; Choi, M.; Dastani, Z.; Drong, A. W.; Eriksson, P.; Franco-Cereceda, A.; Gådin, J. R.; Gharavi, A. G.; Goddard, M. E.; Handsaker, R. E.; Huang, J.; Karpe, F.; Kathiresan, S.; Keildson, S.; Kiryluk, K.; Kubo, M.; Lee, J.; Liang, L.; Lifton, R. P.; Ma, B.; McCarroll, S. A.; McKnight, A. J.; Min, J. L.; Moffatt, M. F.; Montgomery, G. W.; Murabito, J. M.; Nicholson, G.; Nyholt, D. R.; Okada, Y.; Perry, J. R. B.; Dorajoo, R.; Reinmaa, E.; Salem, R. M.; Sandholm, N.; Scott, R. A.; Stolk, L.; Takahashi, A.; Tanaka, T.; van 't Hooft, F. M.; Vinkhuyzen, A. A. E.; Westra, H.; Zheng, W.; Zondervan, K. T.; ADIPOGen Consortium; AGEN-BMI Working Group; CARDIOGRAMplusC4D Consortium; CKDGen Consortium; GLGC; ICBP; MAGIC Investigators; MuTHER Consortium; MIGen Consortium; PAGE Consortium; ReproGen Consortium; GENIE Consortium; International Endogene Consortium; Heath, A. C.; Arveiler, D.; Bakker, S. J. L.; Beilby, J.; Bergman, R. N.; Blangero, J.; Bovet, P.; Campbell, H.; Caulfield, M. J.; Cesana, G.; Chakravarti, A.; Chasman, D. I.; Chines, P. S.; Collins, F. S.; Crawford, D. C.; Cupples, L. A.; Cusi, D.; Danesh, J.; de Faire, U.; den Ruijter, H. M.; Dominiczak, A. F.; Erbel, R.; Erdmann, J.; Eriksson, J. G.; Farrall, M.; Felix, S. B.; Ferrannini, E.; Ferrières, J.; Ford, I.; Forouhi, N. G.; Forrester, T.; Franco, O. H.; Gansevoort, R. T.; Gejman, P. V.; Gieger, C.; Gottesman, O.; Gudnason, V.; Gyllensten, U.; Hall, A. S.; Harris, T. B.; Hattersley, A. T.; Hicks, A. A.; Hindorff, L. A.; Hingorani, A. D.; Hofman, A.; Homuth, G.; Hovingh, G. K.; Humphries, S. E.; Hunt, S. C.; Hyppönen, E.; Illig, T.; Jacobs, K. B.; Jarvelin, M.; Jöckel, K.; Johansen, B.; Jousilahti, P.; Jukema, J. W.; Jula, A. M.; Kaprio, J.; Kastelein, J. J. P.; Keinanen-Kiukaanniemi, S. M.; Kiemeney, L. A.; Knekt, P.; Kooner, J. S.; Kooperberg, C.; Kovacs, P.; Kraja, A. T.; Kumari, M.; Kuusisto, J.; Lakka, T. A.; Langenberg, C.; Marchand, L. L.; Lehtimäki, T.; Lyssenko, V.; Männistö, S.; Marette, A.; Matise, T. C.; McKenzie, C. A.; McKnight, B.; Moll, F. L.; Morris, A. D.; Morris, A. P.; Murray, J. C.; Nelis, M.; Ohlsson, C.; Oldehinkel, A. J.; Ong, K. K.; Madden, P. A. F.; Pasterkamp, G.; Peden, J. F.; Peters, A.; Postma, D. S.; Pramstaller, P. P.; Price, J. F.; Qi, L.; Raitakari, O. T.; Rankinen, T.; Rao, D. C.; Rice, T. K.; Ridker, P. M.; Rioux, J. D.; Ritchie, M. D.; Rudan, I.; Salomaa, V.; Samani, N. J.; Saramies, J.; Sarzynski, M. A.; Schunkert, H.; Schwarz, P. E. H.; Sever, P.; Shuldiner, A. R.; Sinisalo, J.; Stolk, R. P.; Strauch, K.; Tönjes, A.; Trégouët, D.; Tremblay, A.; Tremoli, E.; Virtamo, J.; Vohl, M.; Völker, U.; Waeber, G.; Willemsen, G.; Witteman, J. C.; Zillikens, M. C.; Adair, L. S.; Amouyel, P.; Asselbergs, F. W.; Assimes, T. L.; Bochud, M.; Boehm, B. O.; Boerwinkle, E.; Bornstein, S. R.; Bottinger, E. P.; Bouchard, C.; Cauchi, S.; Chambers, J. C.; Chanock, S. J.; Cooper, R. S.; de Bakker, P. I. W.; Dedoussis, G.; Ferrucci, L.; Franks, P. W.; Froguel, P.; Groop, L. C.; Haiman, C. A.; Hamsten, A.; Hui, J.; Hunter, D. J.; Hveem, K.; Kaplan, R. C.; Kivimaki, M.; Kuh, D.; Laakso, M.; Liu, Y.; Martin, N. G.; März, W.; Melbye, M.; Metspalu, A.; Moebus, S.; Munroe, P. B.; Njølstad, I.; Oostra, B. A.; Palmer, C. N. A.; Pedersen, N. L.; Perola, M.; Pérusse, L.; Peters, U.; Power, C.; Quertermous, T.; Rauramaa, R.; Rivadeneira, F.; Saaristo, T. E.; Saleheen, D.; Sattar, N.; Schadt, E. E.; Schlessinger, D.; Slagboom, P. E.; Snieder, H.; Spector, T. D.; Thorsteinsdottir, U.; Stumvoll, M.; Tuomilehto, J.; Uitterlinden, A. G.; Uusitupa, M.; van der Harst, P.; Walker, M.; Wallaschofski, H.; Wareham, N. J.; Watkins, H.; Weir, D. R.; Wichmann, H.; Wilson, J. F.; Zanen, P.; Borecki, I. B.; Deloukas, P.; Fox, C. S.; Heid, I. M.; O'Connell, J. R.; Strachan, D. P.; Stefansson, K.; van Duijn, C. M.; Abecasis, G. R.; Franke, L.; Frayling, T. M.; McCarthy, M. I.; Visscher, P. M.; Scherag, A.; Willer, C. J.; Boehnke, M.; Mohlke, K. L.; Lindgren, C. M.; Beckmann, J. S.; Barroso, I.; North, K. E.; Ingelsson, E.; Hirschhorn, J. N.; Loos, R. J. F.; and Speliotes, E. K.\n\n\n \n\n\n\n Nature, 518: 197–206. February 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"Genetic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{LockeKahaliBerndtEtAl2015,\n\tabstract = {Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10(-8)), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.},\n\tauthor = {Locke, Adam E. and Kahali, Bratati and Berndt, Sonja I. and Justice, Anne E. and Pers, Tune H. and Day, Felix R. and Powell, Corey and Vedantam, Sailaja and Buchkovich, Martin L. and Yang, Jian and Croteau-Chonka, Damien C. and Esko, Tonu and Fall, Tove and Ferreira, Teresa and Gustafsson, Stefan and Kutalik, Zolt{\\'a}n and Luan, Jian'an and M{\\"a}gi, Reedik and Randall, Joshua C. and Winkler, Thomas W. and Wood, Andrew R. and Workalemahu, Tsegaselassie and Faul, Jessica D. and Smith, Jennifer A. and Zhao, Jing Hua and Zhao, Wei and Chen, Jin and Fehrmann, Rudolf and Hedman, {\\AA}sa K. and Karjalainen, Juha and Schmidt, Ellen M. and Absher, Devin and Amin, Najaf and Anderson, Denise and Beekman, Marian and Bolton, Jennifer L. and Bragg-Gresham, Jennifer L. and Buyske, Steven and Demirkan, Ayse and Deng, Guohong and Ehret, Georg B. and Feenstra, Bjarke and Feitosa, Mary F. and Fischer, Krista and Goel, Anuj and Gong, Jian and Jackson, Anne U. and Kanoni, Stavroula and Kleber, Marcus E. and Kristiansson, Kati and Lim, Unhee and Lotay, Vaneet and Mangino, Massimo and Leach, Irene Mateo and Medina-Gomez, Carolina and Medland, Sarah E. and Nalls, Michael A. and Palmer, Cameron D. and Pasko, Dorota and Pechlivanis, Sonali and Peters, Marjolein J. and Prokopenko, Inga and Shungin, Dmitry and Stan{\\v c}{\\'a}kov{\\'a}, Alena and Strawbridge, Rona J. and Sung, Yun Ju and Tanaka, Toshiko and Teumer, Alexander and Trompet, Stella and van der Laan, Sander W. and van Setten, Jessica and Van Vliet-Ostaptchouk, Jana V. and Wang, Zhaoming and Yengo, Lo{\\"\\i}c and Zhang, Weihua and Isaacs, Aaron and Albrecht, Eva and {\\"A}rnl{\\"o}v, Johan and Arscott, Gillian M. and Attwood, Antony P. and Bandinelli, Stefania and Barrett, Amy and Bas, Isabelita N. and Bellis, Claire and Bennett, Amanda J. and Berne, Christian and Blagieva, Roza and Bl{\\"u}her, Matthias and B{\\"o}hringer, Stefan and Bonnycastle, Lori L. and B{\\"o}ttcher, Yvonne and Boyd, Heather A. and Bruinenberg, Marcel and Caspersen, Ida H. and Chen, Yii-Der Ida and Clarke, Robert and Daw, E. Warwick and de Craen, Anton J. M. and Delgado, Graciela and Dimitriou, Maria and Doney, Alex S. F. and Eklund, Niina and Estrada, Karol and Eury, Elodie and Folkersen, Lasse and Fraser, Ross M. and Garcia, Melissa E. and Geller, Frank and Giedraitis, Vilmantas and Gigante, Bruna and Go, Alan S. and Golay, Alain and Goodall, Alison H. and Gordon, Scott D. and Gorski, Mathias and Grabe, Hans-J{\\"o}rgen and Grallert, Harald and Grammer, Tanja B. and Gr{\\"a}{\\ss}ler, J{\\"u}rgen and Gr{\\"o}nberg, Henrik and Groves, Christopher J. and Gusto, Ga{\\"e}lle and Haessler, Jeffrey and Hall, Per and Haller, Toomas and Hallmans, Goran and Hartman, Catharina A. and Hassinen, Maija and Hayward, Caroline and Heard-Costa, Nancy L. and Helmer, Quinta and Hengstenberg, Christian and Holmen, Oddgeir and Hottenga, Jouke-Jan and James, Alan L. and Jeff, Janina M. and Johansson, {\\AA}sa and Jolley, Jennifer and Juliusdottir, Thorhildur and Kinnunen, Leena and Koenig, Wolfgang and Koskenvuo, Markku and Kratzer, Wolfgang and Laitinen, Jaana and Lamina, Claudia and Leander, Karin and Lee, Nanette R. and Lichtner, Peter and Lind, Lars and Lindstr{\\"o}m, Jaana and Lo, Ken Sin and Lobbens, St{\\'e}phane and Lorbeer, Roberto and Lu, Yingchang and Mach, Fran{\\c c}ois and Magnusson, Patrik K. E. and Mahajan, Anubha and McArdle, Wendy L. and McLachlan, Stela and Menni, Cristina and Merger, Sigrun and Mihailov, Evelin and Milani, Lili and Moayyeri, Alireza and Monda, Keri L. and Morken, Mario A. and Mulas, Antonella and M{\\"u}ller, Gabriele and M{\\"u}ller-Nurasyid, Martina and Musk, Arthur W. and Nagaraja, Ramaiah and N{\\"o}then, Markus M. and Nolte, Ilja M. and Pilz, Stefan and Rayner, Nigel W. and Renstrom, Frida and Rettig, Rainer and Ried, Janina S. and Ripke, Stephan and Robertson, Neil R. and Rose, Lynda M. and Sanna, Serena and Scharnagl, Hubert and Scholtens, Salome and Schumacher, Fredrick R. and Scott, William R. and Seufferlein, Thomas and Shi, Jianxin and Smith, Albert Vernon and Smolonska, Joanna and Stanton, Alice V. and Steinthorsdottir, Valgerdur and Stirrups, Kathleen and Stringham, Heather M. and Sundstr{\\"o}m, Johan and Swertz, Morris A. and Swift, Amy J. and Syv{\\"a}nen, Ann-Christine and Tan, Sian-Tsung and Tayo, Bamidele O. and Thorand, Barbara and Thorleifsson, Gudmar and Tyrer, Jonathan P. and Uh, Hae-Won and Vandenput, Liesbeth and Verhulst, Frank C. and Vermeulen, Sita H. and Verweij, Niek and Vonk, Judith M. and Waite, Lindsay L. and Warren, Helen R. and Waterworth, Dawn and Weedon, Michael N. and Wilkens, Lynne R. and Willenborg, Christina and Wilsgaard, Tom and Wojczynski, Mary K. and Wong, Andrew and Wright, Alan F. and Zhang, Qunyuan and Study, LifeLines Cohort and Brennan, Eoin P. and Choi, Murim and Dastani, Zari and Drong, Alexander W. and Eriksson, Per and Franco-Cereceda, Anders and G{\\aa}din, Jesper R. and Gharavi, Ali G. and Goddard, Michael E. and Handsaker, Robert E. and Huang, Jinyan and Karpe, Fredrik and Kathiresan, Sekar and Keildson, Sarah and Kiryluk, Krzysztof and Kubo, Michiaki and Lee, Jong-Young and Liang, Liming and Lifton, Richard P. and Ma, Baoshan and McCarroll, Steven A. and McKnight, Amy J. and Min, Josine L. and Moffatt, Miriam F. and Montgomery, Grant W. and Murabito, Joanne M. and Nicholson, George and Nyholt, Dale R. and Okada, Yukinori and Perry, John R. B. and Dorajoo, Rajkumar and Reinmaa, Eva and Salem, Rany M. and Sandholm, Niina and Scott, Robert A. and Stolk, Lisette and Takahashi, Atsushi and Tanaka, Toshihiro and van 't Hooft, Ferdinand M. and Vinkhuyzen, Anna A. E. and Westra, Harm-Jan and Zheng, Wei and Zondervan, Krina T. and {ADIPOGen Consortium} and {AGEN-BMI Working Group} and {CARDIOGRAMplusC4D Consortium} and {CKDGen Consortium} and {GLGC} and {ICBP} and {MAGIC Investigators} and {MuTHER Consortium} and {MIGen Consortium} and {PAGE Consortium} and {ReproGen Consortium} and {GENIE Consortium} and {International Endogene Consortium} and Heath, Andrew C. and Arveiler, Dominique and Bakker, Stephan J. L. and Beilby, John and Bergman, Richard N. and Blangero, John and Bovet, Pascal and Campbell, Harry and Caulfield, Mark J. and Cesana, Giancarlo and Chakravarti, Aravinda and Chasman, Daniel I. and Chines, Peter S. and Collins, Francis S. and Crawford, Dana C. and Cupples, L. Adrienne and Cusi, Daniele and Danesh, John and de Faire, Ulf and den Ruijter, Hester M. and Dominiczak, Anna F. and Erbel, Raimund and Erdmann, Jeanette and Eriksson, Johan G. and Farrall, Martin and Felix, Stephan B. and Ferrannini, Ele and Ferri{\\`e}res, Jean and Ford, Ian and Forouhi, Nita G. and Forrester, Terrence and Franco, Oscar H. and Gansevoort, Ron T. and Gejman, Pablo V. and Gieger, Christian and Gottesman, Omri and Gudnason, Vilmundur and Gyllensten, Ulf and Hall, Alistair S. and Harris, Tamara B. and Hattersley, Andrew T. and Hicks, Andrew A. and Hindorff, Lucia A. and Hingorani, Aroon D. and Hofman, Albert and Homuth, Georg and Hovingh, G. Kees and Humphries, Steve E. and Hunt, Steven C. and Hypp{\\"o}nen, Elina and Illig, Thomas and Jacobs, Kevin B. and Jarvelin, Marjo-Riitta and J{\\"o}ckel, Karl-Heinz and Johansen, Berit and Jousilahti, Pekka and Jukema, J. Wouter and Jula, Antti M. and Kaprio, Jaakko and Kastelein, John J. P. and Keinanen-Kiukaanniemi, Sirkka M. and Kiemeney, Lambertus A. and Knekt, Paul and Kooner, Jaspal S. and Kooperberg, Charles and Kovacs, Peter and Kraja, Aldi T. and Kumari, Meena and Kuusisto, Johanna and Lakka, Timo A. and Langenberg, Claudia and Marchand, Loic Le and Lehtim{\\"a}ki, Terho and Lyssenko, Valeriya and M{\\"a}nnist{\\"o}, Satu and Marette, Andr{\\'e} and Matise, Tara C. and McKenzie, Colin A. and McKnight, Barbara and Moll, Frans L. and Morris, Andrew D. and Morris, Andrew P. and Murray, Jeffrey C. and Nelis, Mari and Ohlsson, Claes and Oldehinkel, Albertine J. and Ong, Ken K. and Madden, Pamela A. F. and Pasterkamp, Gerard and Peden, John F. and Peters, Annette and Postma, Dirkje S. and Pramstaller, Peter P. and Price, Jackie F. and Qi, Lu and Raitakari, Olli T. and Rankinen, Tuomo and Rao, D. C. and Rice, Treva K. and Ridker, Paul M. and Rioux, John D. and Ritchie, Marylyn D. and Rudan, Igor and Salomaa, Veikko and Samani, Nilesh J. and Saramies, Jouko and Sarzynski, Mark A. and Schunkert, Heribert and Schwarz, Peter E. H. and Sever, Peter and Shuldiner, Alan R. and Sinisalo, Juha and Stolk, Ronald P. and Strauch, Konstantin and T{\\"o}njes, Anke and Tr{\\'e}gou{\\"e}t, David-Alexandre and Tremblay, Angelo and Tremoli, Elena and Virtamo, Jarmo and Vohl, Marie-Claude and V{\\"o}lker, Uwe and Waeber, G{\\'e}rard and Willemsen, Gonneke and Witteman, Jacqueline C. and Zillikens, M. Carola and Adair, Linda S. and Amouyel, Philippe and Asselbergs, Folkert W. and Assimes, Themistocles L. and Bochud, Murielle and Boehm, Bernhard O. and Boerwinkle, Eric and Bornstein, Stefan R. and Bottinger, Erwin P. and Bouchard, Claude and Cauchi, St{\\'e}phane and Chambers, John C. and Chanock, Stephen J. and Cooper, Richard S. and de Bakker, Paul I. W. and Dedoussis, George and Ferrucci, Luigi and Franks, Paul W. and Froguel, Philippe and Groop, Leif C. and Haiman, Christopher A. and Hamsten, Anders and Hui, Jennie and Hunter, David J. and Hveem, Kristian and Kaplan, Robert C. and Kivimaki, Mika and Kuh, Diana and Laakso, Markku and Liu, Yongmei and Martin, Nicholas G. and M{\\"a}rz, Winfried and Melbye, Mads and Metspalu, Andres and Moebus, Susanne and Munroe, Patricia B. and Nj{\\o}lstad, Inger and Oostra, Ben A. and Palmer, Colin N. A. and Pedersen, Nancy L. and Perola, Markus and P{\\'e}russe, Louis and Peters, Ulrike and Power, Chris and Quertermous, Thomas and Rauramaa, Rainer and Rivadeneira, Fernando and Saaristo, Timo E. and Saleheen, Danish and Sattar, Naveed and Schadt, Eric E. and Schlessinger, David and Slagboom, P. Eline and Snieder, Harold and Spector, Tim D. and Thorsteinsdottir, Unnur and Stumvoll, Michael and Tuomilehto, Jaakko and Uitterlinden, Andr{\\'e} G. and Uusitupa, Matti and van der Harst, Pim and Walker, Mark and Wallaschofski, Henri and Wareham, Nicholas J. and Watkins, Hugh and Weir, David R. and Wichmann, H.-Erich and Wilson, James F. and Zanen, Pieter and Borecki, Ingrid B. and Deloukas, Panos and Fox, Caroline S. and Heid, Iris M. and O'Connell, Jeffrey R. and Strachan, David P. and Stefansson, Kari and van Duijn, Cornelia M. and Abecasis, Gon{\\c c}alo R. and Franke, Lude and Frayling, Timothy M. and McCarthy, Mark I. and Visscher, Peter M. and Scherag, Andr{\\'e} and Willer, Cristen J. and Boehnke, Michael and Mohlke, Karen L. and Lindgren, Cecilia M. and Beckmann, Jacques S. and Barroso, In{\\^e}s and North, Kari E. and Ingelsson, Erik and Hirschhorn, Joel N. and Loos, Ruth J. F. and Speliotes, Elizabeth K.},\n\tchemicals = {Insulin, Glutamic Acid},\n\tcitation-subset = {IM},\n\tcompleted = {2015-02-27},\n\tcountry = {England},\n\tdoi = {10.1038/nature14177},\n\tinvestigator = {Deloukas, Panos and Kanoni, Stavroula and Willenborg, Christina and Farrall, Martin and Assimes, Themistocles L and Thompson, John R and Ingelsson, Erik and Saleheen, Danish and Erdmann, Jeanette and Goldstein, Benjamin A and Stirrups, Kathleen and K{\\"o}nig, Inke R and Cazier, Jean-Baptiste and Johansson, {\\AA}sa and Hall, Alistair S and Lee, Jong-Young and Willer, Cristen J and Chambers, John C and Esko, T{\\~o}nu and Folkersen, Lasse and Goel, Anuj and Grundberg, Elin and Havulinna, Aki S and Ho, Weang K and Hopewell, Jemma C and Eriksson, Niclas and Kleber, Marcus E and Kristiansson, Kati and Lundmark, Per and Lyytik{\\"a}inen, Leo-Pekka and Rafelt, Suzanne and Shungin, Dmitry and Strawbridge, Rona J and Thorleifsson, Gudmar and Tikkanen, Emmi and Van Zuydam, Natalie and Voight, Benjamin F and Waite, Lindsay L and Zhang, Weihua and Ziegler, Andreas and Absher, Devin and Altshuler, David and Balmforth, Anthony J and Barroso, In{\\^e}s and Braund, Peter S and Burgdorf, Christof and Claudi-Boehm, Simone and Cox, David and Dimitriou, Maria and Do, Ron and Doney, Alex S F and El Mokhtari, NourEddine and Eriksson, Per and Fischer, Krista and Fontanillas, Pierre and Franco-Cereceda, Anders and Gigante, Bruna and Groop, Leif and Gustafsson, Stefan and Hager, J{\\"o}rg and Hallmans, G{\\"o}ran and Han, Bok-Ghee and Hunt, Sarah E and Kang, Hyun M and Illig, Thomas and Kessler, Thorsten and Knowles, Joshua W and Kolovou, Genovefa and Kuusisto, Johanna and Langenberg, Claudia and Langford, Cordelia and Leander, Karin and Lokki, Marja-Liisa and Lundmark, Anders and McCarthy, Mark I and Meisinger, Christa and Melander, Olle and Mihailov, Evelin and Maouche, Seraya and Morris, Andrew D and M{\\"u}ller-Nurasyid, Martina and Nikus, Kjell and Peden, John F and Rayner, N William and Rasheed, Asif and Rosinger, Silke and Rubin, Diana and Rumpf, Moritz P and Sch{\\"a}fer, Arne and Sivananthan, Mohan and Song, Ci and Stewart, Alexandre F R and Tan, Sian-Tsung and Thorgeirsson, Gudmundur and van der Schoot, C Ellen and Wagner, Peter J and Wells, George A and Wild, Philipp S and Tsun-Po, Yang and Amouyel, Philippe and Arveiler, Dominique and Basart, Hanneke and Boehnke, Michael and Boerwinkle, Eric and Brambilla, Paolo and Cambien, Francois and Cupples, Adrienne L and de Faire, Ulf and Dehghan, Abbas and Diemert, Patrick and Epstein, Stephen E and Evans, Alun and Ferrario, Marco M and Ferri{\\`e}res, Jean and Gauguier, Dominique and Go, Alan S and Goodall, Alison H and Gudnason, Villi and Hazen, Stanley L and Holm, Hilma and Iribarren, Carlos and Jang, Yangsoo and K{\\"a}h{\\"o}nen, Mika and Kee, Frank and Kim, Hyo-Soo and Klopp, Norman and Koenig, Wolfgang and Kratzer, Wolfgang and Kuulasmaa, Kari and Laakso, Markku and Laaksonen, Reijo and Lee, Ji-Young and Lind, Lars and Ouwehand, Willem H and Parish, Sarah and Park, Jeong E and Pedersen, Nancy L and Peters, Annette and Quertermous, Thomas and Rader, Daniel J and Salomaa, Veikko and Schadt, Eric and Shah, Svati H and Sinisalo, Juha and Stark, Klaus and Stefansson, Kari and Tr{\\'e}gou{\\"e}t, David-Alexandre and Virtamo, Jarmo and Wallentin, Lars and Wareham, Nicholas and Zimmermann, Martina E and Nieminen, Markku S and Hengstenberg, Christian and Sandhu, Manjinder S and Pastinen, Tomi and Syv{\\"a}nen, Ann-Christine and Hovingh, G Kees and Dedoussis, George and Franks, Paul W and Lehtim{\\"a}ki, Terho and Metspalu, Andres and Zalloua, Pierre A and Siegbahn, Agneta and Schreiber, Stefan and Ripatti, Samuli and Blankenberg, Stefan S and Perola, Markus and Clarke, Robert and Boehm, Bernhard O and O'Donnell, Christopher and Reilly, Muredach P and M{\\"a}rz, Winfried and Collins, Rory and Kathiresan, Sekar and Hamsten, Anders and Kooner, Jaspal S and Thorsteinsdottir, Unnur and Danesh, John and Palmer, Colin N A and Roberts, Robert and Watkins, Hugh and Schunkert, Heribert and Samani, Nilesh J and Pattaro, Cristian and K{\\"o}ttgen, Anna and Teumer, Alexander and Garnaas, Maija and B{\\"o}ger, Carsten A and Fuchsberger, Christian and Olden, Matthias and Chen, Ming-Huei and Tin, Adrienne and Taliun, Daniel and Li, Man and Gao, Xiaoyi and Gorski, Mathias and Yang, Qiong and Hundertmark, Claudia and Foster, Meredith C and O'Seaghdha, Conall M and Glazer, Nicole and Isaacs, Aaron and Liu, Ching-Ti and Smith, Albert V and O'Connell, Jeffrey R and Struchalin, Maksim and Tanaka, Toshiko and Li, Guo and Johnson, Andrew D and Gierman, Hinco J and Feitosa, Mary and Hwang, Shih-Jen and Atkinson, Elizabeth J and Lohman, Kurt and Cornelis, Marilyn C and Johansson, {\\AA}sa and T{\\"o}njes, Anke and Dehghan, Abbas and Chouraki, Vincent and Holliday, Elizabeth G and Sorice, Rossella and Kutalik, Zoltan and Lehtim{\\"a}ki, Terho and Esko, T{\\~o}nu and Deshmukh, Harshal and Ulivi, Sheila and Chu, Audrey Y and Murgia, Federico and Trompet, Stella and Imboden, Medea and Kollerits, Barbara and Pistis, Giorgio and Harris, Tamara B and Launer, Lenore J and Aspelund, Thor and Eiriksdottir, Gudny and Mitchell, Braxton D and Boerwinkle, Eric and Schmidt, Helena and Cavalieri, Margherita and Rao, Madhumathi and Hu, Frank B and Demirkan, Ayse and Oostra, Ben A and de Andrade, Mariza and Turner, Stephen T and Ding, Jingzhong and Andrews, Jeanette S and Freedman, Barry I and Koenig, Wolfgang and Illig, Thomas and D{\\"o}ring, Angela and Wichmann, H-Erich and Kolcic, Ivana and Zemunik, Tatijana and Boban, Mladen and Minelli, Cosetta and Wheeler, Heather E and Igl, Wilmar and Zaboli, Ghazal and Wild, Sarah H and Wright, Alan F and Campbell, Harry and Ellinghaus, David and N{\\"o}thlings, Ute and Jacobs, Gunnar and Biffar, Reiner and Endlich, Karlhans and Ernst, Florian and Homuth, Georg and Kroemer, Heyo K and Nauck, Matthias and Stracke, Sylvia and V{\\"o}lker, Uwe and V{\\"o}lzke, Henry and Kovacs, Peter and Stumvoll, Michael and M{\\"a}gi, Reedik and Hofman, Albert and Uitterlinden, Andre G and Rivadeneira, Fernando and Aulchenko, Yurii S and Polasek, Ozren and Hastie, Nick and Vitart, Veronique and Helmer, Catherine and Wang, Jie Jin and Ruggiero, Daniela and Bergmann, Sven and K{\\"a}h{\\"o}nen, Mika and Viikari, Jorma and Nikopensius, Tiit and Province, Michael and Ketkar, Shamika and Colhoun, Helen and Doney, Alex and Robino, Antonietta and Giulianini, Franco and Kr{\\"a}mer, Bernhard K and Portas, Laura and Ford, Ian and Buckley, Brendan M and Adam, Martin and Thun, Gian-Andri and Paulweber, Bernhard and Haun, Margot and Sala, Cinzia and Metzger, Marie and Mitchell, Paul and Ciullo, Marina and Kim, Stuart K and Vollenweider, Peter and Raitakari, Olli and Metspalu, Andres and Palmer, Colin and Gasparini, Paolo and Pirastu, Mario and Jukema, J Wouter and Probst-Hensch, Nicole M and Kronenberg, Florian and Toniolo, Daniela and Gudnason, Vilmundur and Shuldiner, Alan R and Coresh, Josef and Schmidt, Reinhold and Ferrucci, Luigi and Siscovick, David S and van Duijn, Cornelia M and Borecki, Ingrid and Kardia, Sharon L R and Liu, Yongmei and Curhan, Gary C and Rudan, Igor and Gyllensten, Ulf and Wilson, James F and Franke, Andre and Pramstaller, Peter P and Rettig, Rainer and Prokopenko, Inga and Witteman, Jacqueline C M and Hayward, Caroline and Ridker, Paul and Parsa, Afshin and Bochud, Murielle and Heid, Iris M and Goessling, Wolfram and Chasman, Daniel I and Kao, W H Linda and Fox, Caroline S and B{\\"o}ger, Carsten A and Chen, Ming-Huei and Tin, Adrienne and Olden, Matthias and K{\\"o}ttgen, Anna and de Boer, Ian H and Fuchsberger, Christian and O'Seaghdha, Conall M and Pattaro, Cristian and Teumer, Alexander and Liu, Ching-Ti and Glazer, Nicole L and O'Connell, Jeffrey R and Tanaka, Toshiko and Peralta, Carmen A and Kutalik, Zolt{\\'a}n and Luan, Jian'an and Zhao, Jing Hua and Hwang, Shih-Jen and Akylbekova, Ermeg and Kramer, Holly and van der Harst, Pim and Smith, Albert V and Lohman, Kurt and de Andrade, Mariza and Hayward, Caroline and Kollerits, Barbara and T{\\"o}njes, Anke and Aspelund, Thor and Ingelsson, Erik and Eiriksdottir, Gudny and Launer, Lenore J and Harris, Tamara B and Shuldiner, Alan R and Mitchell, Braxton D and Arking, Dan E and Franceschini, Nora and Boerwinkle, Eric and Egan, Josephine and Hernandez, Dena and Reilly, Muredach and Townsend, Raymond R and Lumley, Thomas and Siscovick, David S and Psaty, Bruce M and Kestenbaum, Bryan and Haritunians, Talin and Bergmann, Sven and Vollenweider, Peter and Waeber, Gerard and Mooser, Vincent and Waterworth, Dawn and Johnson, Andrew D and Florez, Jose C and Meigs, James B and Lu, Xiaoning and Turner, Stephen T and Atkinson, Elizabeth J and Leak, Tennille S and Aasar{\\o}d, Knut and Skorpen, Frank and Syv{\\"a}nen, Ann-Christine and Illig, Thomas and Baumert, Jens and Koenig, Wolfgang and Kr{\\"a}mer, Bernhard K and Devuyst, Olivier and Mychaleckyj, Josyf C and Minelli, Cosetta and Bakker, Stephan J L and Kedenko, Lyudmyla and Paulweber, Bernhard and Coassin, Stefan and Endlich, Karlhans and Kroemer, Heyo K and Biffar, Reiner and Stracke, Sylvia and V{\\"o}lzke, Henry and Stumvoll, Michael and M{\\"a}gi, Reedik and Campbell, Harry and Vitart, Veronique and Hastie, Nicholas D and Gudnason, Vilmundur and Kardia, Sharon L R and Liu, Yongmei and Polasek, Ozren and Curhan, Gary and Kronenberg, Florian and Prokopenko, Inga and Rudan, Igor and {\\"A}rnl{\\"o}v, Johan and Hallan, Stein and Navis, Gerjan and Parsa, Afshin and Ferrucci, Luigi and Coresh, Josef and Shlipak, Michael G and Bull, Shelley B and Paterson, Andrew D and Wichmann, H-Erich and Wareham, Nicholas J and Loos, Ruth J F and Rotter, Jerome I and Pramstaller, Peter P and Cupples, L Adrienne and Beckmann, Jacques S and Yang, Qiong and Heid, Iris M and Rettig, Rainer and Dreisbach, Albert W and Bochud, Murielle and Fox, Caroline S and Kao, W H L and Anderson, Carl A and Gordon, Scott D and Guo, Qun and Henders, Anjali K and Lambert, Ann and Lee, Sang Hong and Kraft, Peter and Kennedy, Stephen H and Macgregor, Stuart and Martin, Nicholas G and Missmer, Stacey A and Montgomery, Grant W and Morris, Andrew P and Nyholt, Dale R and Painter, Jodie N and Roseman, Fenella and Treloar, Susan A and Visscher, Peter M and Wallace, Leanne and Zondervan, Krina T and Sandholm, Niina and Salem, Rany M and McKnight, Amy Jayne and Brennan, Eoin P and Forsblom, Carol and Isakova, Tamara and McKay, Gareth J and Williams, Winfred W and Sadlier, Denise M and M{\\"a}kinen, Ville-Petteri and Swan, Elizabeth J and Palmer, Cameron and Boright, Andrew P and Ahlqvist, Emma and Deshmukh, Harshal A and Keller, Benjamin J and Huang, Huateng and Ahola, Aila and Fagerholm, Emma and Gordin, Daniel and Harjutsalo, Valma and He, Bing and Heikkil{\\"a}, Outi and Hietala, Kustaa and Kyt{\\"o}, Janne and Lahermo, P{\\"a}ivi and Lehto, Markku and {\\"O}sterholm, Anne-May and Parkkonen, Maija and Pitk{\\"a}niemi, Janne and Roseng{\\aa}rd-B{\\"a}rlund, Milla and Saraheimo, Markku and Sarti, Cinzia and S{\\"o}derlund, Jenny and Soro-Paavonen, Aino and Syreeni, Anna and Thorn, Lena M and Tikkanen, Heikki and Tolonen, Nina and Tryggvason, Karl and Tuomilehto, Jaakko and Wad{\\'e}n, Johan and Gill, Geoffrey V and Prior, Sarah and Guiducci, Candace and Mirel, Daniel B and Taylor, Andrew and Hosseini, Mohsen and Parving, Hans-Henrik and Rossing, Peter and Tarnow, Lise and Ladenvall, Claes and Alhenc-Gelas, Fran{\\c c}ois and Lefebvre, Pierre and Rigalleau, Vincent and Roussel, Ronan and Tregouet, David-Alexandre and Maestroni, Anna and Maestroni, Silvia and Falhammar, Henrik and Gu, Tianwei and M{\\"o}llsten, Anna and Cimponeriu, Dan and Mihai, Ioana and Mota, Maria and Mota, Eugen and Serafinceanu, Cristian and Stavarachi, Monica and Hanson, Robert L and Nelson, Robert G and Kretzler, Matthias and Colhoun, Helen M and Panduru, Nicolae Mircea and Gu, Harvest F and Brismar, Kerstin and Zerbini, Gianpaolo and Hadjadj, Samy and Marre, Michel and Groop, Leif and Lajer, Maria and Bull, Shelley B and Waggott, Daryl and Paterson, Andrew D and Savage, David A and Bain, Stephen C and Martin, Finian and Hirschhorn, Joel N and Godson, Catherine and Florez, Jose C and Groop, Per-Henrik and Maxwell, Alexander P and Willer, Cristen J and Schmidt, Ellen M and Sengupta, Sebanti and Peloso, Gina M and Gustafsson, Stefan and Kanoni, Stavroula and Ganna, Andrea and Chen, Jin and Buchkovich, Martin L and Mora, Samia and Beckmann, Jacques S and Bragg-Gresham, Jennifer L and Chang, Hsing-Y and Demirkan, Ay{\\c s}e and Den Hertog, Heleen M and Do, Ron and Donnelly, Louise A and Ehret, Georg B and Esko, T{\\~o}nu and Feitosa, Mary F and Ferreira, Teresa and Fischer, Krista and Fontanillas, Pierre and Fraser, Ross M and Freitag, Daniel F and Gurdasani, Deepti and Heikkil{\\"a}, Kauko and Hypp{\\"o}nen, Elina and Isaacs, Aaron and Jackson, Anne U and Johansson, {\\AA}sa and Johnson, Toby and Kaakinen, Marika and Kettunen, Johannes and Kleber, Marcus E and Li, Xiaohui and Luan, Jian'an and Lyytik{\\"a}inen, Leo-Pekka and Magnusson, Patrik K E and Mangino, Massimo and Mihailov, Evelin and Montasser, May E and M{\\"u}ller-Nurasyid, Martina and Nolte, Ilja M and O'Connell, Jeffrey R and Palmer, Cameron D and Perola, Markus and Petersen, Ann-Kristin and Sanna, Serena and Saxena, Richa and Service, Susan K and Shah, Sonia and Shungin, Dmitry and Sidore, Carlo and Song, Ci and Strawbridge, Rona J and Surakka, Ida and Tanaka, Toshiko and Teslovich, Tanya M and Thorleifsson, Gudmar and Van den Herik, Evita G and Voight, Benjamin F and Volcik, Kelly A and Waite, Lindsay L and Wong, Andrew and Wu, Ying and Zhang, Weihua and Absher, Devin and Asiki, Gershim and Barroso, In{\\^e}s and Been, Latonya F and Bolton, Jennifer L and Bonnycastle, Lori L and Brambilla, Paolo and Burnett, Mary S and Cesana, Giancarlo and Dimitriou, Maria and Doney, Alex S F and Doring, Angela and Elliott, Paul and Epstein, Stephen E and Eyjolfsson, Gudmundur Ingi and Gigante, Bruna and Goodarzi, Mark O and Grallert, Harald and Gravito, Martha L and Groves, Christopher J and Hallmans, G{\\"o}ran and Hartikainen, Anna-Liisa and Hayward, Caroline and Hernandez, Dena and Hicks, Andrew A and Holm, Hilma and Hung, Yi-Jen and Illig, Thomas and Jones, Michelle R and Kaleebu, Pontiano and Kastelein, John J P and Khaw, Kay-Tee and Kim, Eric and Klopp, Norman and Komulainen, Pirjo and Kumari, Meena and Langenberg, Claudia and Lehtimaki, Terho and Lin, Shih-Yi and Lindstrom, Jaana and Loos, Ruth J F and Mach, Fran{\\c c}ois and McArdle, Wendy L and Meisinger, Christa and Mitchell, Braxton D and Muller, Gabrielle and Nagaraja, Ramaiah and Narisu, Narisu and Nieminen, Tuomo V M and Nsubuga, Rebecca N and Olafsson, Isleifur and Ong, Ken K and Palotie, Aarno and Papamarkou, Theodore and Pomilla, Cristina and Pouta, Anneli and Rader, Daniel J and Reilly, Muredach P and Ridker, Paul M and Rivadeneira, Fernando and Rudan, Igor and Ruokonen, Aimo and Samani, Nilesh and Scharnagl, Hubert and Seeley, Janet and Silander, Kaisa and Stan{\\v c}{\\'a}kov{\\'a}, Alena and Stirrups, Kathleen and Swift, Amy J and Tiret, Laurence and Uitterlinden, Andre G and van Pelt, L Joost and Vedantam, Sailaja and Wainwright, Nicholas and Wijmenga, Cisca and Wild, Sarah H and Willemsen, Gonneke and Wilsgaard, Tom and Wilson, James F and Young, Elizabeth H and Zhao, Jing Hua and Adair, Linda S and Arveiler, Dominique and Assimes, Themistocles L and Bandinelli, Stefania and Bennett, Franklyn and Bochud, Murielle and Boehm, Bernhard O and Boomsma, Dorret I and Borecki, Ingrid B and Bornstein, Stefan R and Bovet, Pascal and Burnier, Michel and Campbell, Harry and Chakravarti, Aravinda and Chambers, John C and Chen, Yii-Der Ida and Collins, Francis S and Cooper, Richard S and Danesh, John and Dedoussis, George and de Faire, Ulf and Feranil, Alan B and Ferrieres, Jean and Ferrucci, Luigi and Freimer, Nelson B and Gieger, Christian and Groop, Leif C and Gudnason, Vilmundur and Gyllensten, Ulf and Hamsten, Anders and Harris, Tamara B and Hingorani, Aroon and Hirschhorn, Joel N and Hofman, Albert and Hovingh, G Kees and Hsiung, Chao Agnes and Humphries, Steve E and Hunt, Steven C and Hveem, Kristian and Iribarren, Carlos and Jarvelin, Marjo-Riitta and Jula, Antti and K{\\"a}h{\\"o}nen, Mika and Kaprio, Jaakko and Kes{\\"a}niemi, Antero and Kivimaki, Mika and Kooner, Jaspal S and Koudstaal, Peter J and Krauss, Ronald M and Kuh, Diana and Kuusisto, Johanna and Kyvik, Kirsten O and Laakso, Markku and Lakka, Timo A and Lind, Lars and Lindgren, Cecilia M and Martin, Nicholas G and M{\\"a}rz, Winfried and McCarthy, Mark I and McKenzie, Colin A and Meneton, Pierre and Metspalu, Andres and Moilanen, Leena and Morris, Andrew D and Munroe, Patricia B and Nj{\\o}lstad, Inger and Pedersen, Nancy L and Power, Chris and Pramstaller, Peter P and Price, Jackie F and Psaty, Bruce M and Quertermous, Thomas and Rauramaa, Rainer and Saleheen, Danish and Salomaa, Veikko and Sanghera, Dharambir K and Saramies, Jouko and Schwarz, Peter E H and Sheu, Wayne H-H and Shuldiner, Alan R and Siegbahn, Agneta and Spector, Tim D and Stefansson, Kari and Strachan, David P and Tayo, Bamidele O and Tremoli, Elena and Tuomilehto, Jaakko and Uusitupa, Matti and van Duijn, Cornelia M and Vollenweider, Peter and Wallentin, Lars and Wareham, Nicholas J and Whitfield, John B and Wolffenbuttel, Bruce H R and Ordovas, Jose M and Boerwinkle, Eric and Palmer, Colin N A and Thorsteinsdottir, Unnur and Chasman, Daniel I and Rotter, Jerome I and Franks, Paul W and Ripatti, Samuli and Cupples, L Adrienne and Sandhu, Manjinder S and Rich, Stephen S and Boehnke, Michael and Deloukas, Panos and Kathiresan, Sekar and Mohlke, Karen L and Ingelsson, Erik and Abecasis, Gon{\\c c}alo R and Abecasis, Gon{\\c c}alo and Bochud, Murielle and Caulfield, Mark and Chakravarti, Aravinda and Chasman, Dan and Ehret, Georg and Elliott, Paul and Johnson, Andrew and Johnson, Louise and Larson, Martin and Levy, Daniel and Munroe, Patricia and Newton-Cheh, Christopher and O'Reilly, Paul and Palmas, Walter and Psaty, Bruce and Rice, Kenneth and Smith, Albert and Snider, Harold and Tobin, Martin and Van Duijn, Cornelia and Verwoert, Germaine and Ehret, Georg B and Munroe, Patricia B and Rice, Kenneth M and Bochud, Murielle and Johnson, Andrew D and Chasman, Daniel I and Smith, Albert V and Tobin, Martin D and Verwoert, Germaine C and Hwang, Shih-Jen and Pihur, Vasyl and Vollenweider, Peter and O'Reilly, Paul F and Amin, Najaf and Bragg-Gresham, Jennifer L and Teumer, Alexander and Glazer, Nicole L and Launer, Lenore and Zhao, Jing Hua and Aulchenko, Yurii and Heath, Simon and S{\\~o}ber, Siim and Parsa, Afshin and Luan, Jian'an and Arora, Pankaj and Dehghan, Abbas and Zhang, Feng and Lucas, Gavin and Hicks, Andrew A and Jackson, Anne U and Peden, John F and Tanaka, Toshiko and Wild, Sarah H and Rudan, Igor and Igl, Wilmar and Milaneschi, Yuri and Parker, Alex N and Fava, Cristiano and Chambers, John C and Fox, Ervin R and Kumari, Meena and Go, Min Jin and van der Harst, Pim and Kao, Wen Hong Linda and Sj{\\"o}gren, Marketa and Vinay, D G and Alexander, Myriam and Tabara, Yasuharu and Shaw-Hawkins, Sue and Whincup, Peter H and Liu, Yongmei and Shi, Gang and Kuusisto, Johanna and Tayo, Bamidele and Seielstad, Mark and Sim, Xueling and Nguyen, Khanh-Dung Hoang and Lehtim{\\"a}ki, Terho and Matullo, Giuseppe and Wu, Ying and Gaunt, Tom R and Onland-Moret, N Charlotte and Cooper, Matthew N and Platou, Carl G P and Org, Elin and Hardy, Rebecca and Dahgam, Santosh and Palmen, Jutta and Vitart, Veronique and Braund, Peter S and Kuznetsova, Tatiana and Uiterwaal, Cuno S P M and Adeyemo, Adebowale and Palmas, Walter and Campbell, Harry and Ludwig, Barbara and Tomaszewski, Maciej and Tzoulaki, Ioanna and Palmer, Nicholette D and Aspelund, Thor and Garcia, Melissa and Chang, Yen-Pei C and O'Connell, Jeffrey R and Steinle, Nanette I and Grobbee, Diederick E and Arking, Dan E and Kardia, Sharon L and Morrison, Alanna C and Hernandez, Dena and Najjar, Samer and McArdle, Wendy L and Hadley, David and Brown, Morris J and Connell, John M and Hingorani, Aroon D and Day, Ian N M and Lawlor, Debbie A and Beilby, John P and Lawrence, Robert W and Clarke, Robert and Collins, Rory and Hopewell, Jemma C and Ongen, Halit and Dreisbach, Albert W and Li, Yali and Young, J H and Bis, Joshua C and K{\\"a}h{\\"o}nen, Mika and Viikari, Jorma and Adair, Linda S and Lee, Nanette R and Chen, Ming-Huei and Olden, Matthias and Pattaro, Cristian and Bolton, Judith A Hoffman and K{\\"o}ttgen, Anna and Bergmann, Sven and Mooser, Vincent and Chaturvedi, Nish and Frayling, Timothy M and Islam, Muhammad and Jafar, Tazeen H and Erdmann, Jeanette and Kulkarni, Smita R and Bornstein, Stefan R and Gr{\\"a}ssler, J{\\"u}rgen and Groop, Leif and Voight, Benjamin F and Kettunen, Johannes and Howard, Philip and Taylor, Andrew and Guarrera, Simonetta and Ricceri, Fulvio and Emilsson, Valur and Plump, Andrew and Barroso, In{\\^e}s and Khaw, Kay-Tee and Weder, Alan B and Hunt, Steven C and Sun, Yan V and Bergman, Richard N and Collins, Francis S and Bonnycastle, Lori L and Scott, Laura J and Stringham, Heather M and Peltonen, Leena and Perola, Markus and Vartiainen, Erkki and Brand, Stefan-Martin and Staessen, Jan A and Wang, Thomas J and Burton, Paul R and Artigas, Maria Soler and Dong, Yanbin and Snieder, Harold and Wang, Xiaoling and Zhu, Haidong and Lohman, Kurt K and Rudock, Megan E and Heckbert, Susan R and Smith, Nicholas L and Wiggins, Kerri L and Doumatey, Ayo and Shriner, Daniel and Veldre, Gudrun and Viigimaa, Margus and Kinra, Sanjay and Prabhakaran, Dorairajan and Tripathy, Vikal and Langefeld, Carl D and Rosengren, Annika and Thelle, Dag S and Corsi, Anna Maria and Singleton, Andrew and Forrester, Terrence and Hilton, Gina and McKenzie, Colin A and Salako, Tunde and Iwai, Naoharu and Kita, Yoshikuni and Ogihara, Toshio and Ohkubo, Takayoshi and Okamura, Tomonori and Ueshima, Hirotsugu and Umemura, Satoshi and Eyheramendy, Susana and Meitinger, Thomas and Wichmann, H-Erich and Cho, Yoon Shin and Kim, Hyung-Lae and Lee, Jong-Young and Scott, James and Sehmi, Joban S and Zhang, Weihua and Hedblad, Bo and Nilsson, Peter and Smith, George Davey and Wong, Andrew and Narisu, Narisu and Stan{\\v c}{\\'a}kov{\\'a}, Alena and Raffel, Leslie J and Yao, Jie and Kathiresan, Sekar and O'Donnell, Chris and Schwartz, Stephen M and Ikram, M Arfan and Longstreth, W T and Mosley, Thomas H and Seshadri, Sudha and Shrine, Nick R G and Wain, Louise V and Morken, Mario A and Swift, Amy J and Laitinen, Jaana and Prokopenko, Inga and Zitting, Paavo and Cooper, Jackie A and Humphries, Steve E and Danesh, John and Rasheed, Asif and Goel, Anuj and Hamsten, Anders and Watkins, Hugh and Bakker, Stephan J L and van Gilst, Wiek H and Janipalli, Charles S and Mani, K Radha and Yajnik, Chittaranjan S and Hofman, Albert and Mattace-Raso, Francesco U S and Oostra, Ben A and Demirkan, Ayse and Isaacs, Aaron and Rivadeneira, Fernando and Lakatta, Edward G and Orru, Marco and Scuteri, Angelo and Ala-Korpela, Mika and Kangas, Antti J and Lyytik{\\"a}inen, Leo-Pekka and Soininen, Pasi and Tukiainen, Taru and W{\\"u}rtz, Peter and Ong, Rick Twee-Hee and D{\\"o}rr, Marcus and Kroemer, Heyo K and V{\\"o}lker, Uwe and V{\\"o}lzke, Henry and Galan, Pilar and Hercberg, Serge and Lathrop, Mark and Zelenika, Diana and Deloukas, Panos and Mangino, Massimo and Spector, Tim D and Zhai, Guangju and Meschia, James F and Nalls, Michael A and Sharma, Pankaj and Terzic, Janos and Kumar, M J Kranthi and Denniff, Matthew and Zukowska-Szczechowska, Ewa and Wagenknecht, Lynne E and Fowkes, F Gerald R and Charchar, Fadi J and Schwarz, Peter E H and Hayward, Caroline and Guo, Xiuqing and Rotimi, Charles and Bots, Michiel L and Brand, Eva and Samani, Nilesh J and Polasek, Ozren and Talmud, Philippa J and Nyberg, Fredrik and Kuh, Diana and Laan, Maris and Hveem, Kristian and Palmer, Lyle J and van der Schouw, Yvonne T and Casas, Juan P and Mohlke, Karen L and Vineis, Paolo and Raitakari, Olli and Ganesh, Santhi K and Wong, Tien Y and Tai, E Shyong and Cooper, Richard S and Laakso, Markku and Rao, Dabeeru C and Harris, Tamara B and Morris, Richard W and Dominiczak, Anna F and Kivimaki, Mika and Marmot, Michael G and Miki, Tetsuro and Saleheen, Danish and Chandak, Giriraj R and Coresh, Josef and Navis, Gerjan and Salomaa, Veikko and Han, Bok-Ghee and Zhu, Xiaofeng and Kooner, Jaspal S and Melander, Olle and Ridker, Paul M and Bandinelli, Stefania and Gyllensten, Ulf B and Wright, Alan F and Wilson, James F and Ferrucci, Luigi and Farrall, Martin and Tuomilehto, Jaakko and Pramstaller, Peter P and Elosua, Roberto and Soranzo, Nicole and Sijbrands, Eric J G and Altshuler, David and Loos, Ruth J F and Shuldiner, Alan R and Gieger, Christian and Meneton, Pierre and Uitterlinden, Andre G and Wareham, Nicholas J and Gudnason, Vilmundur and Rotter, Jerome I and Rettig, Rainer and Uda, Manuela and Strachan, David P and Witteman, Jacqueline C M and Hartikainen, Anna-Liisa and Beckmann, Jacques S and Boerwinkle, Eric and Vasan, Ramachandran S and Boehnke, Michael and Larson, Martin G and J{\\"a}rvelin, Marjo-Riitta and Psaty, Bruce M and Abecasis, Gon{\\c c}alo R and Chakravarti, Aravinda and Elliott, Paul and van Duijn, Cornelia M and Newton-Cheh, Christopher and Levy, Daniel and Caulfield, Mark J and Johnson, Toby and Alizadeh, Behrooz Z and de Boer, Rudolf A and Boezen, H Marike and Bruinenberg, Marcel and Franke, Lude and van der Harst, Pim and Hillege, Hans L and van der Klauw, Melanie M and Navis, Gerjan and Ormel, Johan and Postma, Dirkje S and Rosmalen, Judith G M and Slaets, Joris P and Snieder, Harold and Stolk, Ronald P and Wolffenbuttel, Bruce H R and Wijmenga, Cisca and Scott, Robert A and Lagou, Vasiliki and Welch, Ryan P and Wheeler, Eleanor and Montasser, May E and Luan, Jian'an and M{\\"a}gi, Reedik and Strawbridge, Rona J and Rehnberg, Emil and Gustafsson, Stefan and Kanoni, Stavroula and Rasmussen-Torvik, Laura J and Yengo, Lo{\\"\\i}c and Lecoeur, Cecile and Shungin, Dmitry and Sanna, Serena and Sidore, Carlo and Johnson, Paul C D and Jukema, J Wouter and Johnson, Toby and Mahajan, Anubha and Verweij, Niek and Thorleifsson, Gudmar and Hottenga, Jouke-Jan and Shah, Sonia and Smith, Albert V and Sennblad, Bengt and Gieger, Christian and Salo, Perttu and Perola, Markus and Timpson, Nicholas J and Evans, David M and St Pourcain, Beate and Wu, Ying and Andrews, Jeanette S and Hui, Jennie and Bielak, Lawrence F and Zhao, Wei and Horikoshi, Momoko and Navarro, Pau and Isaacs, Aaron and O'Connell, Jeffrey R and Stirrups, Kathleen and Vitart, Veronique and Hayward, Caroline and Esko, T{\\"o}nu and Mihailov, Evelin and Fraser, Ross M and Fall, Tove and Voight, Benjamin F and Raychaudhuri, Soumya and Chen, Han and Lindgren, Cecilia M and Morris, Andrew P and Rayner, Nigel W and Robertson, Neil and Rybin, Denis and Liu, Ching-Ti and Beckmann, Jacques S and Willems, Sara M and Chines, Peter S and Jackson, Anne U and Kang, Hyun Min and Stringham, Heather M and Song, Kijoung and Tanaka, Toshiko and Peden, John F and Goel, Anuj and Hicks, Andrew A and An, Ping and M{\\"u}ller-Nurasyid, Martina and Franco-Cereceda, Anders and Folkersen, Lasse and Marullo, Letizia and Jansen, Hanneke and Oldehinkel, Albertine J and Bruinenberg, Marcel and Pankow, James S and North, Kari E and Forouhi, Nita G and Loos, Ruth J F and Edkins, Sarah and Varga, Tibor V and Hallmans, G{\\"o}ran and Oksa, Heikki and Antonella, Mulas and Nagaraja, Ramaiah and Trompet, Stella and Ford, Ian and Bakker, Stephan J L and Kong, Augustine and Kumari, Meena and Gigante, Bruna and Herder, Christian and Munroe, Patricia B and Caulfield, Mark and Antti, Jula and Mangino, Massimo and Small, Kerrin and Miljkovic, Iva and Liu, Yongmei and Atalay, Mustafa and Kiess, Wieland and James, Alan L and Rivadeneira, Fernando and Uitterlinden, Andre G and Palmer, Colin N A and Doney, Alex S F and Willemsen, Gonneke and Smit, Johannes H and Campbell, Susan and Polasek, Ozren and Bonnycastle, Lori L and Hercberg, Serge and Dimitriou, Maria and Bolton, Jennifer L and Fowkes, Gerard R and Kovacs, Peter and Lindstr{\\"o}m, Jaana and Zemunik, Tatijana and Bandinelli, Stefania and Wild, Sarah H and Basart, Hanneke V and Rathmann, Wolfgang and Grallert, Harald and Maerz, Winfried and Kleber, Marcus E and Boehm, Bernhard O and Peters, Annette and Pramstaller, Peter P and Province, Michael A and Borecki, Ingrid B and Hastie, Nicholas D and Rudan, Igor and Campbell, Harry and Watkins, Hugh and Farrall, Martin and Stumvoll, Michael and Ferrucci, Luigi and Waterworth, Dawn M and Bergman, Richard N and Collins, Francis S and Tuomilehto, Jaakko and Watanabe, Richard M and de Geus, Eco J C and Penninx, Brenda W and Hofman, Albert and Oostra, Ben A and Psaty, Bruce M and Vollenweider, Peter and Wilson, James F and Wright, Alan F and Hovingh, G Kees and Metspalu, Andres and Uusitupa, Matti and Magnusson, Patrik K E and Kyvik, Kirsten O and Kaprio, Jaakko and Price, Jackie F and Dedoussis, George V and Deloukas, Panos and Meneton, Pierre and Lind, Lars and Boehnke, Michael and Shuldiner, Alan R and van Duijn, Cornelia M and Morris, Andrew D and Toenjes, Anke and Peyser, Patricia A and Beilby, John P and K{\\"o}rner, Antje and Kuusisto, Johanna and Laakso, Markku and Bornstein, Stefan R and Schwarz, Peter E H and Lakka, Timo A and Rauramaa, Rainer and Adair, Linda S and Smith, George Davey and Spector, Tim D and Illig, Thomas and de Faire, Ulf and Hamsten, Anders and Gudnason, Vilmundur and Kivimaki, Mika and Hingorani, Aroon and Keinanen-Kiukaanniemi, Sirkka M and Saaristo, Timo E and Boomsma, Dorret I and Stefansson, Kari and van der Harst, Pim and Dupuis, Jos{\\'e}e and Pedersen, Nancy L and Sattar, Naveed and Harris, Tamara B and Cucca, Francesco and Ripatti, Samuli and Salomaa, Veikko and Mohlke, Karen L and Balkau, Beverley and Froguel, Philippe and Pouta, Anneli and Jarvelin, Marjo-Riitta and Wareham, Nicholas J and Bouatia-Naji, Nabila and McCarthy, Mark I and Franks, Paul W and Meigs, James B and Teslovich, Tanya M and Florez, Jose C and Langenberg, Claudia and Ingelsson, Erik and Prokopenko, Inga and Barroso, In{\\^e}s and Kathiresan, Sekar and Voight, Benjamin F and Purcell, Shaun and Musunuru, Kiran and Ardissino, Diego and Mannucci, Pier M and Anand, Sonia and Engert, James C and Samani, Nilesh J and Schunkert, Heribert and Erdmann, Jeanette and Reilly, Muredach P and Rader, Daniel J and Morgan, Thomas and Spertus, John A and Stoll, Monika and Girelli, Domenico and McKeown, Pascal P and Patterson, Chris C and Siscovick, David S and O'Donnell, Christopher J and Elosua, Roberto and Peltonen, Leena and Salomaa, Veikko and Schwartz, Stephen M and Melander, Olle and Altshuler, David and Ardissino, Diego and Merlini, Pier Angelica and Berzuini, Carlo and Bernardinelli, Luisa and Peyvandi, Flora and Tubaro, Marco and Celli, Patrizia and Ferrario, Maurizio and Fetiveau, Raffaela and Marziliano, Nicola and Casari, Giorgio and Galli, Michele and Ribichini, Flavio and Rossi, Marco and Bernardi, Francesco and Zonzin, Pietro and Piazza, Alberto and Mannucci, Pier M and Schwartz, Stephen M and Siscovick, David S and Yee, Jean and Friedlander, Yechiel and Elosua, Roberto and Marrugat, Jaume and Lucas, Gavin and Subirana, Isaac and Sala, Joan and Ramos, Rafael and Kathiresan, Sekar and Meigs, James B and Williams, Gordon and Nathan, David M and MacRae, Calum A and O'Donnell, Christopher J and Salomaa, Veikko and Havulinna, Aki S and Peltonen, Leena and Melander, Olle and Berglund, Goran and Voight, Benjamin F and Kathiresan, Sekar and Hirschhorn, Joel N and Asselta, Rosanna and Duga, Stefano and Spreafico, Marta and Musunuru, Kiran and Daly, Mark J and Purcell, Shaun and Voight, Benjamin F and Purcell, Shaun and Nemesh, James and Korn, Joshua M and McCarroll, Steven A and Schwartz, Stephen M and Yee, Jean and Kathiresan, Sekar and Lucas, Gavin and Subirana, Isaac and Elosua, Roberto and Surti, Aarti and Guiducci, Candace and Gianniny, Lauren and Mirel, Daniel and Parkin, Melissa and Burtt, Noel and Gabriel, Stacey B and Samani, Nilesh J and Thompson, John R and Braund, Peter S and Wright, Benjamin J and Balmforth, Anthony J and Ball, Stephen G and Hall, Alistair S and Schunkert, I Heribert and Erdmann, Jeanette and Linsel-Nitschke, Patrick and Lieb, Wolfgang and Ziegler, Andreas and K{\\"o}nig, Inke R and Hengstenberg, Christian and Fischer, Marcus and Stark, Klaus and Grosshennig, Anika and Preuss, Michael and Wichmann, H-Erich and Schreiber, Stefan and Schunkert, Heribert and Samani, Nilesh J and Erdmann, Jeanette and Ouwehand, Willem and Hengstenberg, Christian and Deloukas, Panos and Scholz, Michael and Cambien, Francois and Goodall, Alison and Reilly, Muredach P and Li, Mingyao and Chen, Zhen and Wilensky, Robert and Matthai, William and Qasim, Atif and Hakonarson, Hakon H and Devaney, Joe and Burnett, Mary-Susan and Pichard, Augusto D and Kent, Kenneth M and Satler, Lowell and Lindsay, Joseph M and Waksman, Ron and Knouff, Christopher W and Waterworth, Dawn M and Walker, Max C and Mooser, Vincent and Epstein, Stephen E and Rader, Daniel J and Scheffold, Thomas and Berger, Klaus and Stoll, Monika and Huge, Andreas and Girelli, Domenico and Martinelli, Nicola and Olivieri, Oliviero and Corrocher, Roberto and Morgan, Thomas and Spertus, John A and McKeown, Pascal P and Patterson, Chris C and Schunkert, Heribert and Erdmann, Jeanette and Linsel-Nitschke, Patrick and Lieb, Wolfgang and Ziegler, Andreas and K{\\"o}nig, Inke R and Hengstenberg, Christian and Fischer, Marcus and Stark, Klaus and Grosshennig, Anika and Preuss, Michael and Wichmann, H-Erich and Schreiber, Stefan and H{\\'o}lm, Hilma and Thorleifsson, Gudmar and Thorsteinsdottir, Unnur and Stefansson, Kari and Engert, James C and Do, Ron and Xie, Changchun and Anand, Sonia and Kathiresan, Sekar and Ardissino, Diego and Mannucci, Pier M and Siscovick, David and O'Donnell, Christopher J and Samani, Nilesh J and Melander, Olle and Elosua, Roberto and Peltonen, Leena and Salomaa, Veikko and Schwartz, Stephen M and Altshuler, David and Ahmadi, Kourosh R and Ainali, Chrysanthi and Barrett, Amy and Bataille, Veronique and Bell, Jordana T and Buil, Alfonso and Deloukas, Panos and Dermitzakis, Emmanouil T and Dimas, Antigone S and Durbin, Richard and Glass, Daniel and Grundberg, Elin and Hassanali, Neelam and Hedman, {\\AA}sa K and Ingle, Catherine and Keildson, Sarah and Knowles, David and Krestyaninova, Maria and Lindgren, Cecilia M and Lowe, Christopher E and McCarthy, Mark I and Meduri, Eshwar and di Meglio, Paola and Min, Josine L and Montgomery, Stephen B and Nestle, Frank O and Nica, Alexandra C and Nisbet, James and O'Rahilly, Stephen and Parts, Leopold and Potter, Simon and Sekowska, Magdalena and Shin, So-Youn and Small, Kerrin S and Soranzo, Nicole and Spector, Tim D and Surdulescu, Gabriela and Travers, Mary E and Tsaprouni, Loukia and Tsoka, Sophia and Wilk, Alicja and Yang, Tsun-Po and Zondervan, Krina T and Matise, Tara and Buyske, Steve and Higashio, Julia and Williams, Rasheeda and Nato, Andrew and Ambite, Jose Luis and Deelman, Ewa and Manolio, Teri and Hindorff, Lucia and North, Kari E and Heiss, Gerardo and Taylor, Kira and Franceschini, Nora and Avery, Christy and Graff, Misa and Lin, Danyu and Quibrera, Miguel and Cochran, Barbara and Kao, Linda and Umans, Jason and Cole, Shelley and MacCluer, Jean and Person, Sharina and Pankow, James and Gross, Myron and Boerwinkle, Eric and Fornage, Myriam and Durda, Peter and Jenny, Nancy and Patsy, Bruce and Arnold, Alice and Buzkova, Petra and Crawford, Dana and Haines, Jonathan and Murdock, Deborah and Glenn, Kim and Brown-Gentry, Kristin and Thornton-Wells, Tricia and Dumitrescu, Logan and Jeff, Janina and Bush, William S and Mitchell, Sabrina L and Goodloe, Robert and Wilson, Sarah and Boston, Jonathan and Malinowski, Jennifer and Restrepo, Nicole and Oetjens, Matthew and Fowke, Jay and Zheng, Wei and Spencer, Kylee and Ritchie, Marylyn and Pendergrass, Sarah and Le Marchand, Lo{\\"\\i}c and Wilkens, Lynne and Park, Lani and Tiirikainen, Maarit and Kolonel, Laurence and Lim, Unhee and Cheng, Iona and Wang, Hansong and Shohet, Ralph and Haiman, Christopher and Stram, Daniel and Henderson, Brian and Monroe, Kristine and Schumacher, Fredrick and Kooperberg, Charles and Peters, Ulrike and Anderson, Garnet and Carlson, Chris and Prentice, Ross and LaCroix, Andrea and Wu, Chunyuan and Carty, Cara and Gong, Jian and Rosse, Stephanie and Young, Alicia and Haessler, Jeff and Kocarnik, Jonathan and Lin, Yi and Jackson, Rebecca and Duggan, David and Kuller, Lew and Stolk, Lisette and Perry, John R B and Chasman, Daniel I and He, Chunyan and Mangino, Massimo and Sulem, Patrick and Barbalic, Maja and Broer, Linda and Byrne, Enda M and Ernst, Florian and Esko, T{\\~o}nu and Franceschini, Nora and Gudbjartsson, Daniel F and Hottenga, Jouke-Jan and Kraft, Peter and McArdle, Patick F and Porcu, Eleonora and Shin, So-Youn and Smith, Albert V and van Wingerden, Sophie and Zhai, Guangju and Zhuang, Wei V and Albrecht, Eva and Alizadeh, Behrooz Z and Aspelund, Thor and Bandinelli, Stefania and Lauc, Lovorka Barac and Beckmann, Jacques S and Boban, Mladen and Boerwinkle, Eric and Broekmans, Frank J and Burri, Andrea and Campbell, Harry and Chanock, Stephen J and Chen, Constance and Cornelis, Marilyn C and Corre, Tanguy and Coviello, Andrea D and d'Adamo, Pio and Davies, Gail and de Faire, Ulf and de Geus, Eco J C and Deary, Ian J and Dedoussis, George V Z and Deloukas, Panagiotis and Ebrahim, Shah and Eiriksdottir, Gudny and Emilsson, Valur and Eriksson, Johan G and Fauser, Bart C J M and Ferreli, Liana and Ferrucci, Luigi and Fischer, Krista and Folsom, Aaron R and Garcia, Melissa E and Gasparini, Paolo and Gieger, Christian and Glazer, Nicole and Grobbee, Diederick E and Hall, Per and Haller, Toomas and Hankinson, Susan E and Hass, Merli and Hayward, Caroline and Heath, Andrew C and Hofman, Albert and Ingelsson, Erik and Janssens, A Cecile J W and Johnson, Andrew D and Karasik, David and Kardia, Sharon L R and Keyzer, Jules and Kiel, Douglas P and Kolcic, Ivana and Kutalik, Zolt{\\'a}n and Lahti, Jari and Lai, Sandra and Laisk, Triin and Laven, Joop S E and Lawlor, Debbie A and Liu, Jianjun and Lopez, Lorna M and Louwers, Yvonne V and Magnusson, Patrik K E and Marongiu, Mara and Martin, Nicholas G and Klaric, Irena Martinovic and Masciullo, Corrado and McKnight, Barbara and Medland, Sarah E and Melzer, David and Mooser, Vincent and Navarro, Pau and Newman, Anne B and Nyholt, Dale R and Onland-Moret, N Charlotte and Palotie, Aarno and Par{\\'e}, Guillaume and Parker, Alex N and Pedersen, Nancy L and Peeters, Petra H M and Pistis, Giorgio and Plump, Andrew S and Polasek, Ozren and Pop, Victor J M and Psaty, Bruce M and R{\\"a}ikk{\\"o}nen, Katri and Rehnberg, Emil and Rotter, Jerome I and Rudan, Igor and Sala, Cinzia and Salumets, Andres and Scuteri, Angelo and Singleton, Andrew and Smith, Jennifer A and Snieder, Harold and Soranzo, Nicole and Stacey, Simon N and Starr, John M and Stathopoulou, Maria G and Stirrups, Kathleen and Stolk, Ronald P and Styrkarsdottir, Unnur and Sun, Yan V and Tenesa, Albert and Thorand, Barbara and Toniolo, Daniela and Tryggvadottir, Laufey and Tsui, Kim and Ulivi, Sheila and van Dam, Rob M and van der Schouw, Yvonne T and van Gils, Carla H and van Nierop, Peter and Vink, Jacqueline M and Visscher, Peter M and Voorhuis, Marlies and Waeber, G{\\'e}rard and Wallaschofski, Henri and Wichmann, H Erich and Widen, Elisabeth and Wijnands-van Gent, Colette J M and Willemsen, Gonneke and Wilson, James F and Wolffenbuttel, Bruce H R and Wright, Alan F and Yerges-Armstrong, Laura M and Zemunik, Tatijana and Zgaga, Lina and Zillikens, M Carola and Zygmunt, Marek and Arnold, Alice M and Boomsma, Dorret I and Buring, Julie E and Crisponi, Laura and Demerath, Ellen W and Gudnason, Vilmundur and Harris, Tamara B and Hu, Frank B and Hunter, David J and Launer, Lenore J and Metspalu, Andres and Montgomery, Grant W and Oostra, Ben A and Ridker, Paul M and Sanna, Serena and Schlessinger, David and Spector, Tim D and Stefansson, Kari and Streeten, Elizabeth A and Thorsteinsdottir, Unnur and Uda, Manuela and Uitterlinden, Andr{\\'e} G and van Duijn, Cornelia M and V{\\"o}lzke, Henry and Murray, Anna and Murabito, Joanne M and Visser, Jenny A and Lunetta, Kathryn L and Elks, Cathy E and Perry, John R B and Sulem, Patrick and Chasman, Daniel I and Franceschini, Nora and He, Chunyan and Lunetta, Kathryn L and Visser, Jenny A and Byrne, Enda M and Cousminer, Diana L and Gudbjartsson, Daniel F and Esko, T{\\~o}nu and Feenstra, Bjarke and Hottenga, Jouke-Jan and Koller, Daniel L and Kutalik, Zolt{\\'a}n and Lin, Peng and Mangino, Massimo and Marongiu, Mara and McArdle, Patrick F and Smith, Albert V and Stolk, Lisette and van Wingerden, Sophie W and Zhao, Jing Hua and Albrecht, Eva and Corre, Tanguy and Ingelsson, Erik and Hayward, Caroline and Magnusson, Patrik K E and Smith, Erin N and Ulivi, Shelia and Warrington, Nicole M and Zgaga, Lina and Alavere, Helen and Amin, Najaf and Aspelund, Thor and Bandinelli, Stefania and Barroso, Ines and Berenson, Gerald S and Bergmann, Sven and Blackburn, Hannah and Boerwinkle, Eric and Buring, Julie E and Busonero, Fabio and Campbell, Harry and Chanock, Stephen J and Chen, Wei and Cornelis, Marilyn C and Couper, David and Coviello, Andrea D and d'Adamo, Pio and de Faire, Ulf and de Geus, Eco J C and Deloukas, Panos and D{\\"o}ring, Angela and Smith, George Davey and Easton, Douglas F and Eiriksdottir, Gudny and Emilsson, Valur and Eriksson, Johan and Ferrucci, Luigi and Folsom, Aaron R and Foroud, Tatiana and Garcia, Melissa and Gasparini, Paolo and Geller, Frank and Gieger, Christian and Gudnason, Vilmundur and Hall, Per and Hankinson, Susan E and Ferreli, Liana and Heath, Andrew C and Hernandez, Dena G and Hofman, Albert and Hu, Frank B and Illig, Thomas and J{\\"a}rvelin, Marjo-Riitta and Johnson, Andrew D and Karasik, David and Khaw, Kay-Tee and Kiel, Douglas P and Kilpel{\\"a}inen, Tuomas O and Kolcic, Ivana and Kraft, Peter and Launer, Lenore J and Laven, Joop S E and Li, Shengxu and Liu, Jianjun and Levy, Daniel and Martin, Nicholas G and McArdle, Wendy L and Melbye, Mads and Mooser, Vincent and Murray, Jeffrey C and Murray, Sarah S and Nalls, Michael A and Navarro, Pau and Nelis, Mari and Ness, Andrew R and Northstone, Kate and Oostra, Ben A and Peacock, Munro and Palmer, Lyle J and Palotie, Aarno and Par{\\'e}, Guillaume and Parker, Alex N and Pedersen, Nancy L and Peltonen, Leena and Pennell, Craig E and Pharoah, Paul and Polasek, Ozren and Plump, Andrew S and Pouta, Anneli and Porcu, Eleonora and Rafnar, Thorunn and Rice, John P and Ring, Susan M and Rivadeneira, Fernando and Rudan, Igor and Sala, Cinzia and Salomaa, Veikko and Sanna, Serena and Schlessinger, David and Schork, Nicholas J and Scuteri, Angelo and Segr{\\`e}, Ayellet V and Shuldiner, Alan R and Soranzo, Nicole and Sovio, Ulla and Srinivasan, Sathanur R and Strachan, David P and Tammesoo, Mar-Liis and Tikkanen, Emmi and Toniolo, Daniela and Tsui, Kim and Tryggvadottir, Laufey and Tyrer, Jonathon and Uda, Manuela and van Dam, Rob M and van Meurs, Joyve B J and Vollenweider, Peter and Waeber, Gerard and Wareham, Nicholas J and Waterworth, Dawn M and Weedon, Michael N and Wichmann, H Erich and Willemsen, Gonneke and Wilson, James F and Wright, Alan F and Young, Lauren and Zhai, Guangju and Zhuang, Wei Vivian and Bierut, Laura J and Boomsma, Dorret I and Boyd, Heather A and Crisponi, Laura and Demerath, Ellen W and van Duijn, Cornelia M and Econs, Michael J and Harris, Tamara B and Hunter, David J and Loos, Ruth J F and Metspalu, Andres and Montgomery, Grant W and Ridker, Paul M and Spector, Tim D and Streeten, Elizabeth A and Stefansson, Kari and Thorsteinsdottir, Unnur and Uitterlinden, Andr{\\'e} G and Widen, Elisabeth and Murabito, Joanne M and Ong, Ken K and Murray, Anna and Murray, Anna},\n\tissn = {1476-4687},\n\tissn-linking = {0028-0836},\n\tissue = {7538},\n\tjournal = {Nature},\n\tkeywords = {Adipogenesis, genetics; Adiposity, genetics; Age Factors; Body Mass Index; Continental Population Groups, genetics; Energy Metabolism, genetics; Europe, ethnology; Female; Genetic Predisposition to Disease, genetics; Genome-Wide Association Study; Glutamic Acid, metabolism; Humans; Insulin, metabolism; Insulin Secretion; Male; Obesity, genetics, metabolism; Polymorphism, Single Nucleotide, genetics; Quantitative Trait Loci, genetics; Synapses, metabolism},\n\tmid = {NIHMS668049},\n\tmonth = feb,\n\tnlm-id = {0410462},\n\towner = {NLM},\n\tpages = {197--206},\n\tpmc = {PMC4382211},\n\tpmid = {25673413},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/25673413/},\n\n\tpubmodel = {Print},\n\tpubstate = {ppublish},\n\trevised = {2020-05-14},\n\ttitle = {Genetic studies of body mass index yield new insights for obesity biology.},\n\tvolume = {518},\n\tyear = {2015},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/25673413/},\n\tbdsk-url-2 = {https://doi.org/10.1038/nature14177}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n New genetic loci link adipose and insulin biology to body fat distribution.\n \n \n \n \n\n\n \n Shungin, D.; Winkler, T. W.; Croteau-Chonka, D. C.; Ferreira, T.; Locke, A. E.; Mägi, R.; Strawbridge, R. J.; Pers, T. H.; Fischer, K.; Justice, A. E.; Workalemahu, T.; Wu, J. M. W.; Buchkovich, M. L.; Heard-Costa, N. L.; Roman, T. S.; Drong, A. W.; Song, C.; Gustafsson, S.; Day, F. R.; Esko, T.; Fall, T.; Kutalik, Z.; Luan, J.; Randall, J. C.; Scherag, A.; Vedantam, S.; Wood, A. R.; Chen, J.; Fehrmann, R.; Karjalainen, J.; Kahali, B.; Liu, C.; Schmidt, E. M.; Absher, D.; Amin, N.; Anderson, D.; Beekman, M.; Bragg-Gresham, J. L.; Buyske, S.; Demirkan, A.; Ehret, G. B.; Feitosa, M. F.; Goel, A.; Jackson, A. U.; Johnson, T.; Kleber, M. E.; Kristiansson, K.; Mangino, M.; Leach, I. M.; Medina-Gomez, C.; Palmer, C. D.; Pasko, D.; Pechlivanis, S.; Peters, M. J.; Prokopenko, I.; Stan ̌cáková, A.; Sung, Y. J.; Tanaka, T.; Teumer, A.; Van Vliet-Ostaptchouk, J. V.; Yengo, L.; Zhang, W.; Albrecht, E.; Ärnlöv, J.; Arscott, G. M.; Bandinelli, S.; Barrett, A.; Bellis, C.; Bennett, A. J.; Berne, C.; Blüher, M.; Böhringer, S.; Bonnet, F.; Böttcher, Y.; Bruinenberg, M.; Carba, D. B.; Caspersen, I. H.; Clarke, R.; Daw, E. W.; Deelen, J.; Deelman, E.; Delgado, G.; Doney, A. S.; Eklund, N.; Erdos, M. R.; Estrada, K.; Eury, E.; Friedrich, N.; Garcia, M. E.; Giedraitis, V.; Gigante, B.; Go, A. S.; Golay, A.; Grallert, H.; Grammer, T. B.; Gräßler, J.; Grewal, J.; Groves, C. J.; Haller, T.; Hallmans, G.; Hartman, C. A.; Hassinen, M.; Hayward, C.; Heikkilä, K.; Herzig, K.; Helmer, Q.; Hillege, H. L.; Holmen, O.; Hunt, S. C.; Isaacs, A.; Ittermann, T.; James, A. L.; Johansson, I.; Juliusdottir, T.; Kalafati, I.; Kinnunen, L.; Koenig, W.; Kooner, I. K.; Kratzer, W.; Lamina, C.; Leander, K.; Lee, N. R.; Lichtner, P.; Lind, L.; Lindström, J.; Lobbens, S.; Lorentzon, M.; Mach, F.; Magnusson, P. K.; Mahajan, A.; McArdle, W. L.; Menni, C.; Merger, S.; Mihailov, E.; Milani, L.; Mills, R.; Moayyeri, A.; Monda, K. L.; Mooijaart, S. P.; Mühleisen, T. W.; Mulas, A.; Müller, G.; Müller-Nurasyid, M.; Nagaraja, R.; Nalls, M. A.; Narisu, N.; Glorioso, N.; Nolte, I. M.; Olden, M.; Rayner, N. W.; Renstrom, F.; Ried, J. S.; Robertson, N. R.; Rose, L. M.; Sanna, S.; Scharnagl, H.; Scholtens, S.; Sennblad, B.; Seufferlein, T.; Sitlani, C. M.; Smith, A. V.; Stirrups, K.; Stringham, H. M.; Sundström, J.; Swertz, M. A.; Swift, A. J.; Syvänen, A.; Tayo, B. O.; Thorand, B.; Thorleifsson, G.; Tomaschitz, A.; Troffa, C.; van Oort, F. V.; Verweij, N.; Vonk, J. M.; Waite, L. L.; Wennauer, R.; Wilsgaard, T.; Wojczynski, M. K.; Wong, A.; Zhang, Q.; Zhao, J. H.; Brennan, E. P.; Choi, M.; Eriksson, P.; Folkersen, L.; Franco-Cereceda, A.; Gharavi, A. G.; Hedman, Å. K.; Hivert, M.; Huang, J.; Kanoni, S.; Karpe, F.; Keildson, S.; Kiryluk, K.; Liang, L.; Lifton, R. P.; Ma, B.; McKnight, A. J.; McPherson, R.; Metspalu, A.; Min, J. L.; Moffatt, M. F.; Montgomery, G. W.; Murabito, J. M.; Nicholson, G.; Nyholt, D. R.; Olsson, C.; Perry, J. R.; Reinmaa, E.; Salem, R. M.; Sandholm, N.; Schadt, E. E.; Scott, R. A.; Stolk, L.; Vallejo, E. E.; Westra, H.; Zondervan, K. T.; ADIPOGen Consortium; CARDIOGRAMplusC4D Consortium; CKDGen Consortium; GEFOS Consortium; GENIE Consortium; GLGC; ICBP; International Endogene Consortium; LifeLines Cohort Study; MAGIC Investigators; MuTHER Consortium; PAGE Consortium; ReproGen Consortium; Amouyel, P.; Arveiler, D.; Bakker, S. J.; Beilby, J.; Bergman, R. N.; Blangero, J.; Brown, M. J.; Burnier, M.; Campbell, H.; Chakravarti, A.; Chines, P. S.; Claudi-Boehm, S.; Collins, F. S.; Crawford, D. C.; Danesh, J.; de Faire, U.; de Geus, E. J.; Dörr, M.; Erbel, R.; Eriksson, J. G.; Farrall, M.; Ferrannini, E.; Ferrières, J.; Forouhi, N. G.; Forrester, T.; Franco, O. H.; Gansevoort, R. T.; Gieger, C.; Gudnason, V.; Haiman, C. A.; Harris, T. B.; Hattersley, A. T.; Heliövaara, M.; Hicks, A. A.; Hingorani, A. D.; Hoffmann, W.; Hofman, A.; Homuth, G.; Humphries, S. E.; Hyppönen, E.; Illig, T.; Jarvelin, M.; Johansen, B.; Jousilahti, P.; Jula, A. M.; Kaprio, J.; Kee, F.; Keinanen-Kiukaanniemi, S. M.; Kooner, J. S.; Kooperberg, C.; Kovacs, P.; Kraja, A. T.; Kumari, M.; Kuulasmaa, K.; Kuusisto, J.; Lakka, T. A.; Langenberg, C.; Le Marchand, L.; Lehtimäki, T.; Lyssenko, V.; Männistö, S.; Marette, A.; Matise, T. C.; McKenzie, C. A.; McKnight, B.; Musk, A. W.; Möhlenkamp, S.; Morris, A. D.; Nelis, M.; Ohlsson, C.; Oldehinkel, A. J.; Ong, K. K.; Palmer, L. J.; Penninx, B. W.; Peters, A.; Pramstaller, P. P.; Raitakari, O. T.; Rankinen, T.; Rao, D. C.; Rice, T. K.; Ridker, P. M.; Ritchie, M. D.; Rudan, I.; Salomaa, V.; Samani, N. J.; Saramies, J.; Sarzynski, M. A.; Schwarz, P. E.; Shuldiner, A. R.; Staessen, J. A.; Steinthorsdottir, V.; Stolk, R. P.; Strauch, K.; Tönjes, A.; Tremblay, A.; Tremoli, E.; Vohl, M.; Völker, U.; Vollenweider, P.; Wilson, J. F.; Witteman, J. C.; Adair, L. S.; Bochud, M.; Boehm, B. O.; Bornstein, S. R.; Bouchard, C.; Cauchi, S.; Caulfield, M. J.; Chambers, J. C.; Chasman, D. I.; Cooper, R. S.; Dedoussis, G.; Ferrucci, L.; Froguel, P.; Grabe, H.; Hamsten, A.; Hui, J.; Hveem, K.; Jöckel, K.; Kivimaki, M.; Kuh, D.; Laakso, M.; Liu, Y.; März, W.; Munroe, P. B.; Njølstad, I.; Oostra, B. A.; Palmer, C. N.; Pedersen, N. L.; Perola, M.; Pérusse, L.; Peters, U.; Power, C.; Quertermous, T.; Rauramaa, R.; Rivadeneira, F.; Saaristo, T. E.; Saleheen, D.; Sinisalo, J.; Slagboom, P. E.; Snieder, H.; Spector, T. D.; Stefansson, K.; Stumvoll, M.; Tuomilehto, J.; Uitterlinden, A. G.; Uusitupa, M.; van der Harst, P.; Veronesi, G.; Walker, M.; Wareham, N. J.; Watkins, H.; Wichmann, H.; Abecasis, G. R.; Assimes, T. L.; Berndt, S. I.; Boehnke, M.; Borecki, I. B.; Deloukas, P.; Franke, L.; Frayling, T. M.; Groop, L. C.; Hunter, D. J.; Kaplan, R. C.; O'Connell, J. R.; Qi, L.; Schlessinger, D.; Strachan, D. P.; Thorsteinsdottir, U.; van Duijn, C. M.; Willer, C. J.; Visscher, P. M.; Yang, J.; Hirschhorn, J. N.; Zillikens, M. C.; McCarthy, M. I.; Speliotes, E. K.; North, K. E.; Fox, C. S.; Barroso, I.; Franks, P. W.; Ingelsson, E.; Heid, I. M.; Loos, R. J.; Cupples, L. A.; Morris, A. P.; Lindgren, C. M.; and Mohlke, K. L.\n\n\n \n\n\n\n Nature, 518: 187–196. February 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"New$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{ShunginWinklerCroteauChonkaEtAl2015,\n\tabstract = {Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms.},\n\tauthor = {Shungin, Dmitry and Winkler, Thomas W. and Croteau-Chonka, Damien C. and Ferreira, Teresa and Locke, Adam E. and M{\\"a}gi, Reedik and Strawbridge, Rona J. and Pers, Tune H. and Fischer, Krista and Justice, Anne E. and Workalemahu, Tsegaselassie and Wu, Joseph M. W. and Buchkovich, Martin L. and Heard-Costa, Nancy L. and Roman, Tamara S. and Drong, Alexander W. and Song, Ci and Gustafsson, Stefan and Day, Felix R. and Esko, Tonu and Fall, Tove and Kutalik, Zolt{\\'a}n and Luan, Jian'an and Randall, Joshua C. and Scherag, Andr{\\'e} and Vedantam, Sailaja and Wood, Andrew R. and Chen, Jin and Fehrmann, Rudolf and Karjalainen, Juha and Kahali, Bratati and Liu, Ching-Ti and Schmidt, Ellen M. and Absher, Devin and Amin, Najaf and Anderson, Denise and Beekman, Marian and Bragg-Gresham, Jennifer L. and Buyske, Steven and Demirkan, Ayse and Ehret, Georg B. and Feitosa, Mary F. and Goel, Anuj and Jackson, Anne U. and Johnson, Toby and Kleber, Marcus E. and Kristiansson, Kati and Mangino, Massimo and Leach, Irene Mateo and Medina-Gomez, Carolina and Palmer, Cameron D. and Pasko, Dorota and Pechlivanis, Sonali and Peters, Marjolein J. and Prokopenko, Inga and Stan{\\v c}{\\'a}kov{\\'a}, Alena and Sung, Yun Ju and Tanaka, Toshiko and Teumer, Alexander and Van Vliet-Ostaptchouk, Jana V. and Yengo, Lo{\\"\\i}c and Zhang, Weihua and Albrecht, Eva and {\\"A}rnl{\\"o}v, Johan and Arscott, Gillian M. and Bandinelli, Stefania and Barrett, Amy and Bellis, Claire and Bennett, Amanda J. and Berne, Christian and Bl{\\"u}her, Matthias and B{\\"o}hringer, Stefan and Bonnet, Fabrice and B{\\"o}ttcher, Yvonne and Bruinenberg, Marcel and Carba, Delia B. and Caspersen, Ida H. and Clarke, Robert and Daw, E. Warwick and Deelen, Joris and Deelman, Ewa and Delgado, Graciela and Doney, Alex Sf and Eklund, Niina and Erdos, Michael R. and Estrada, Karol and Eury, Elodie and Friedrich, Nele and Garcia, Melissa E. and Giedraitis, Vilmantas and Gigante, Bruna and Go, Alan S. and Golay, Alain and Grallert, Harald and Grammer, Tanja B. and Gr{\\"a}{\\ss}ler, J{\\"u}rgen and Grewal, Jagvir and Groves, Christopher J. and Haller, Toomas and Hallmans, Goran and Hartman, Catharina A. and Hassinen, Maija and Hayward, Caroline and Heikkil{\\"a}, Kauko and Herzig, Karl-Heinz and Helmer, Quinta and Hillege, Hans L. and Holmen, Oddgeir and Hunt, Steven C. and Isaacs, Aaron and Ittermann, Till and James, Alan L. and Johansson, Ingegerd and Juliusdottir, Thorhildur and Kalafati, Ioanna-Panagiota and Kinnunen, Leena and Koenig, Wolfgang and Kooner, Ishminder K. and Kratzer, Wolfgang and Lamina, Claudia and Leander, Karin and Lee, Nanette R. and Lichtner, Peter and Lind, Lars and Lindstr{\\"o}m, Jaana and Lobbens, St{\\'e}phane and Lorentzon, Mattias and Mach, Fran{\\c c}ois and Magnusson, Patrik Ke and Mahajan, Anubha and McArdle, Wendy L. and Menni, Cristina and Merger, Sigrun and Mihailov, Evelin and Milani, Lili and Mills, Rebecca and Moayyeri, Alireza and Monda, Keri L. and Mooijaart, Simon P. and M{\\"u}hleisen, Thomas W. and Mulas, Antonella and M{\\"u}ller, Gabriele and M{\\"u}ller-Nurasyid, Martina and Nagaraja, Ramaiah and Nalls, Michael A. and Narisu, Narisu and Glorioso, Nicola and Nolte, Ilja M. and Olden, Matthias and Rayner, Nigel W. and Renstrom, Frida and Ried, Janina S. and Robertson, Neil R. and Rose, Lynda M. and Sanna, Serena and Scharnagl, Hubert and Scholtens, Salome and Sennblad, Bengt and Seufferlein, Thomas and Sitlani, Colleen M. and Smith, Albert Vernon and Stirrups, Kathleen and Stringham, Heather M. and Sundstr{\\"o}m, Johan and Swertz, Morris A. and Swift, Amy J. and Syv{\\"a}nen, Ann-Christine and Tayo, Bamidele O. and Thorand, Barbara and Thorleifsson, Gudmar and Tomaschitz, Andreas and Troffa, Chiara and van Oort, Floor Va and Verweij, Niek and Vonk, Judith M. and Waite, Lindsay L. and Wennauer, Roman and Wilsgaard, Tom and Wojczynski, Mary K. and Wong, Andrew and Zhang, Qunyuan and Zhao, Jing Hua and Brennan, Eoin P. and Choi, Murim and Eriksson, Per and Folkersen, Lasse and Franco-Cereceda, Anders and Gharavi, Ali G. and Hedman, {\\AA}sa K. and Hivert, Marie-France and Huang, Jinyan and Kanoni, Stavroula and Karpe, Fredrik and Keildson, Sarah and Kiryluk, Krzysztof and Liang, Liming and Lifton, Richard P. and Ma, Baoshan and McKnight, Amy J. and McPherson, Ruth and Metspalu, Andres and Min, Josine L. and Moffatt, Miriam F. and Montgomery, Grant W. and Murabito, Joanne M. and Nicholson, George and Nyholt, Dale R. and Olsson, Christian and Perry, John Rb and Reinmaa, Eva and Salem, Rany M. and Sandholm, Niina and Schadt, Eric E. and Scott, Robert A. and Stolk, Lisette and Vallejo, Edgar E. and Westra, Harm-Jan and Zondervan, Krina T. and {ADIPOGen Consortium} and {CARDIOGRAMplusC4D Consortium} and {CKDGen Consortium} and {GEFOS Consortium} and {GENIE Consortium} and {GLGC} and {ICBP} and {International Endogene Consortium }and {LifeLines Cohort Study} and {MAGIC Investigators} and {MuTHER Consortium} and {PAGE Consortium} and {ReproGen Consortium} and Amouyel, Philippe and Arveiler, Dominique and Bakker, Stephan Jl and Beilby, John and Bergman, Richard N. and Blangero, John and Brown, Morris J. and Burnier, Michel and Campbell, Harry and Chakravarti, Aravinda and Chines, Peter S. and Claudi-Boehm, Simone and Collins, Francis S. and Crawford, Dana C. and Danesh, John and de Faire, Ulf and de Geus, Eco Jc and D{\\"o}rr, Marcus and Erbel, Raimund and Eriksson, Johan G. and Farrall, Martin and Ferrannini, Ele and Ferri{\\`e}res, Jean and Forouhi, Nita G. and Forrester, Terrence and Franco, Oscar H. and Gansevoort, Ron T. and Gieger, Christian and Gudnason, Vilmundur and Haiman, Christopher A. and Harris, Tamara B. and Hattersley, Andrew T. and Heli{\\"o}vaara, Markku and Hicks, Andrew A. and Hingorani, Aroon D. and Hoffmann, Wolfgang and Hofman, Albert and Homuth, Georg and Humphries, Steve E. and Hypp{\\"o}nen, Elina and Illig, Thomas and Jarvelin, Marjo-Riitta and Johansen, Berit and Jousilahti, Pekka and Jula, Antti M. and Kaprio, Jaakko and Kee, Frank and Keinanen-Kiukaanniemi, Sirkka M. and Kooner, Jaspal S. and Kooperberg, Charles and Kovacs, Peter and Kraja, Aldi T. and Kumari, Meena and Kuulasmaa, Kari and Kuusisto, Johanna and Lakka, Timo A. and Langenberg, Claudia and Le Marchand, Loic and Lehtim{\\"a}ki, Terho and Lyssenko, Valeriya and M{\\"a}nnist{\\"o}, Satu and Marette, Andr{\\'e} and Matise, Tara C. and McKenzie, Colin A. and McKnight, Barbara and Musk, Arthur W. and M{\\"o}hlenkamp, Stefan and Morris, Andrew D. and Nelis, Mari and Ohlsson, Claes and Oldehinkel, Albertine J. and Ong, Ken K. and Palmer, Lyle J. and Penninx, Brenda W. and Peters, Annette and Pramstaller, Peter P. and Raitakari, Olli T. and Rankinen, Tuomo and Rao, D. C. and Rice, Treva K. and Ridker, Paul M. and Ritchie, Marylyn D. and Rudan, Igor and Salomaa, Veikko and Samani, Nilesh J. and Saramies, Jouko and Sarzynski, Mark A. and Schwarz, Peter Eh and Shuldiner, Alan R. and Staessen, Jan A. and Steinthorsdottir, Valgerdur and Stolk, Ronald P. and Strauch, Konstantin and T{\\"o}njes, Anke and Tremblay, Angelo and Tremoli, Elena and Vohl, Marie-Claude and V{\\"o}lker, Uwe and Vollenweider, Peter and Wilson, James F. and Witteman, Jacqueline C. and Adair, Linda S. and Bochud, Murielle and Boehm, Bernhard O. and Bornstein, Stefan R. and Bouchard, Claude and Cauchi, St{\\'e}phane and Caulfield, Mark J. and Chambers, John C. and Chasman, Daniel I. and Cooper, Richard S. and Dedoussis, George and Ferrucci, Luigi and Froguel, Philippe and Grabe, Hans-J{\\"o}rgen and Hamsten, Anders and Hui, Jennie and Hveem, Kristian and J{\\"o}ckel, Karl-Heinz and Kivimaki, Mika and Kuh, Diana and Laakso, Markku and Liu, Yongmei and M{\\"a}rz, Winfried and Munroe, Patricia B. and Nj{\\o}lstad, Inger and Oostra, Ben A. and Palmer, Colin Na and Pedersen, Nancy L. and Perola, Markus and P{\\'e}russe, Louis and Peters, Ulrike and Power, Chris and Quertermous, Thomas and Rauramaa, Rainer and Rivadeneira, Fernando and Saaristo, Timo E. and Saleheen, Danish and Sinisalo, Juha and Slagboom, P. Eline and Snieder, Harold and Spector, Tim D. and Stefansson, Kari and Stumvoll, Michael and Tuomilehto, Jaakko and Uitterlinden, Andr{\\'e} G. and Uusitupa, Matti and van der Harst, Pim and Veronesi, Giovanni and Walker, Mark and Wareham, Nicholas J. and Watkins, Hugh and Wichmann, H.-Erich and Abecasis, Goncalo R. and Assimes, Themistocles L. and Berndt, Sonja I. and Boehnke, Michael and Borecki, Ingrid B. and Deloukas, Panos and Franke, Lude and Frayling, Timothy M. and Groop, Leif C. and Hunter, David J. and Kaplan, Robert C. and O'Connell, Jeffrey R. and Qi, Lu and Schlessinger, David and Strachan, David P. and Thorsteinsdottir, Unnur and van Duijn, Cornelia M. and Willer, Cristen J. and Visscher, Peter M. and Yang, Jian and Hirschhorn, Joel N. and Zillikens, M. Carola and McCarthy, Mark I. and Speliotes, Elizabeth K. and North, Kari E. and Fox, Caroline S. and Barroso, In{\\^e}s and Franks, Paul W. and Ingelsson, Erik and Heid, Iris M. and Loos, Ruth Jf and Cupples, L. Adrienne and Morris, Andrew P. and Lindgren, Cecilia M. and Mohlke, Karen L.},\n\tchemicals = {Insulin},\n\tcitation-subset = {IM},\n\tcompleted = {2015-02-27},\n\tcountry = {England},\n\tdoi = {10.1038/nature14132},\n\tinvestigator = {Dastani, Zari and Hivert, Marie-France and Timpson, Nicholas and Perry, John R B and Yuan, Xin and Scott, Robert A and Henneman, Peter and Heid, Iris M and Kizer, Jorge R and Lyytikainen, Leo-Pekka and Fuchsberger, Christian and Tanaka, Toshiko and Morris, Andrew P and Small, Kerrin and Isaacs, Aaron and Beekman, Marian and Coassin, Stefan and Lohman, Kurt and Qi, Lu and Kanoni, Stavroula and Pankow, James S and Uh, Hae-Won and Wu, Ying and Bidulescu, Aurelian and Rasmussen-Torvik, Laura J and Greenwood, Celia M T and Ladouceur, Martin and Grimsby, Jonna and Manning, Alisa K and Liu, Ching-Ti and Kooner, Jaspal and Mooser, Vincent E and Vollenweider, Peter and Kapur, Karen A and Chambers, John and Wareham, Nicholas J and Langenberg, Claudia and Frants, Rune and Willemsvan-vanDijk, Ko and Oostra, Ben A and Willems, Sara M and Lamina, Claudia and Winkler, Thomas and Psaty, Bruce M and Tracy, Russell P and Brody, Jennifer and Chen, Ida and Viikari, Jorma and K{\\"a}h{\\"o}nen, Mika and Pramstaller, Peter P and Evans, David M and St Pourcain, Beate and Sattar, Naveed and Wood, Andy and Bandinelli, Stefania and Carlson, Olga D and Egan, Josephine M and B{\\"o}hringer, Stefan and van Heemst, Diana and Kedenko, Lyudmyla and Kristiansson, Kati and Nuotio, Marja-Liisa and Loo, Britt-Marie and Harris, Tamara and Garcia, Melissa and Kanaya, Alka and Haun, Margot and Klopp, Norman and Wichmann, H Erich and Deloukas, Panos and Katsareli, Efi and Couper, David J and Duncan, Bruce B and Kloppenburg, Margreet and Adair, Linda S and Borja, Judith B and Wilson, James G and Musani, Solomon and Guo, Xiuqing and Johnson, Toby and Semple, Robert and Teslovich, Tanya M and Allison, Matthew A and Redline, Susan and Buxbaum, Sarah G and Mohlke, Karen L and Meulenbelt, Ingrid and Ballantyne, Christie M and Dedoussis, George V and Hu, Frank B and Liu, Yongmei and Paulweber, Bernhard and Spector, Timothy D and Slagboom, P Eline and Ferrucci, Luigi and Jula, Antti and Perola, Markus and Raitakari, Olli and Florez, Jose C and Salomaa, Veikko and Eriksson, Johan G and Frayling, Timothy M and Hicks, Andrew A and Lehtim{\\"a}ki, Terho and Smith, George Davey and Siscovick, David S and Kronenberg, Florian and van Duijn, Cornelia and Loos, Ruth J F and Waterworth, Dawn M and Meigs, James B and Dupuis, Josee and Richards, John Brent and Deloukas, Panos and Kanoni, Stavroula and Willenborg, Christina and Farrall, Martin and Assimes, Themistocles L and Thompson, John R and Ingelsson, Erik and Saleheen, Danish and Erdmann, Jeanette and Goldstein, Benjamin A and Stirrups, Kathleen and K{\\"o}nig, Inke R and Cazier, Jean-Baptiste and Johansson, {\\AA}sa and Hall, Alistair S and Lee, Jong-Young and Willer, Cristen J and Chambers, John C and Esko, T{\\~o}nu and Folkersen, Lasse and Goel, Anuj and Grundberg, Elin and Havulinna, Aki S and Ho, Weang K and Hopewell, Jemma C and Eriksson, Niclas and Kleber, Marcus E and Kristiansson, Kati and Lundmark, Per and Lyytik{\\"a}inen, Leo-Pekka and Rafelt, Suzanne and Shungin, Dmitry and Strawbridge, Rona J and Thorleifsson, Gudmar and Tikkanen, Emmi and Van Zuydam, Natalie and Voight, Benjamin F and Waite, Lindsay L and Zhang, Weihua and Ziegler, Andreas and Absher, Devin and Altshuler, David and Balmforth, Anthony J and Barroso, In{\\^e}s and Braund, Peter S and Burgdorf, Christof and Claudi-Boehm, Simone and Cox, David and Dimitriou, Maria and Do, Ron and Doney, Alex S F and El Mokhtari, NourEddine and Eriksson, Per and Fischer, Krista and Fontanillas, Pierre and Franco-Cereceda, Anders and Gigante, Bruna and Groop, Leif and Gustafsson, Stefan and Hager, J{\\"o}rg and Hallmans, G{\\"o}ran and Han, Bok-Ghee and Hunt, Sarah E and Kang, Hyun M and Illig, Thomas and Kessler, Thorsten and Knowles, Joshua W and Kolovou, Genovefa and Kuusisto, Johanna and Langenberg, Claudia and Langford, Cordelia and Leander, Karin and Lokki, Marja-Liisa and Lundmark, Anders and McCarthy, Mark I and Meisinger, Christa and Melander, Olle and Mihailov, Evelin and Maouche, Seraya and Morris, Andrew D and M{\\"u}ller-Nurasyid, Martina and Nikus, Kjell and Peden, John F and Rayner, N William and Rasheed, Asif and Rosinger, Silke and Rubin, Diana and Rumpf, Moritz P and Sch{\\"a}fer, Arne and Sivananthan, Mohan and Song, Ci and Stewart, Alexandre F R and Tan, Sian-Tsung and Thorgeirsson, Gudmundur and van der Schoot, C Ellen and Wagner, Peter J and Wells, George A and Wild, Philipp S and Yang, Tsun-Po and Amouyel, Philippe and Arveiler, Dominique and Basart, Hanneke and Boehnke, Michael and Boerwinkle, Eric and Brambilla, Paolo and Cambien, Francois and Cupples, Adrienne L and de Faire, Ulf and Dehghan, Abbas and Diemert, Patrick and Epstein, Stephen E and Evans, Alun and Ferrario, Marco M and Ferri{\\`e}res, Jean and Gauguier, Dominique and Go, Alan S and Goodall, Alison H and Gudnason, Villi and Hazen, Stanley L and Holm, Hilma and Iribarren, Carlos and Jang, Yangsoo and K{\\"a}h{\\"o}nen, Mika and Kee, Frank and Kim, Hyo-Soo and Klopp, Norman and Koenig, Wolfgang and Kratzer, Wolfgang and Kuulasmaa, Kari and Laakso, Markku and Laaksonen, Reijo and Lee, Ji-Young and Lind, Lars and Ouwehand, Willem H and Parish, Sarah and Park, Jeong E and Pedersen, Nancy L and Peters, Annette and Quertermous, Thomas and Rader, Daniel J and Salomaa, Veikko and Schadt, Eric and Shah, Svati H and Sinisalo, Juha and Stark, Klaus and Stefansson, Kari and Tr{\\'e}gou{\\"e}t, David-Alexandre and Virtamo, Jarmo and Wallentin, Lars and Wareham, Nicholas and Zimmermann, Martina E and Nieminen, Markku S and Hengstenberg, Christian and Sandhu, Manjinder S and Pastinen, Tomi and Syv{\\"a}nen, Ann-Christine and Hovingh, G Kees and Dedoussis, George and Franks, Paul W and Lehtim{\\"a}ki, Terho and Metspalu, Andres and Zalloua, Pierre A and Siegbahn, Agneta and Schreiber, Stefan and Ripatti, Samuli and Blankenberg, Stefan S and Perola, Markus and Clarke, Robert and Boehm, Bernhard O and O'Donnell, Christopher and Reilly, Muredach P and M{\\"a}rz, Winfried and Collins, Rory and Kathiresan, Sekar and Hamsten, Anders and Kooner, Jaspal S and Thorsteinsdottir, Unnur and Danesh, John and Palmer, Colin N A and Roberts, Robert and Watkins, Hugh and Schunkert, Heribert and Samani, Nilesh J and Pattaro, Cristian and K{\\"o}ttgen, Anna and Teumer, Alexander and Garnaas, Maija and B{\\"o}ger, Carsten A and Fuchsberger, Christian and Olden, Matthias and Chen, Ming-Huei and Tin, Adrienne and Taliun, Daniel and Li, Man and Gao, Xiaoyi and Gorski, Mathias and Yang, Qiong and Hundertmark, Claudia and Foster, Meredith C and O'Seaghdha, Conall M and Glazer, Nicole and Isaacs, Aaron and Liu, Ching-Ti and Smith, Albert V and O'Connell, Jeffrey R and Struchalin, Maksim and Tanaka, Toshiko and Li, Guo and Johnson, Andrew D and Gierman, Hinco J and Feitosa, Mary and Hwang, Shih-Jen and Atkinson, Elizabeth J and Lohman, Kurt and Cornelis, Marilyn C and Johansson, {\\AA}sa and T{\\"o}njes, Anke and Dehghan, Abbas and Chouraki, Vincent and Holliday, Elizabeth G and Sorice, Rossella and Kutalik, Zoltan and Lehtim{\\"a}ki, Terho and Esko, T{\\~o}nu and Deshmukh, Harshal and Ulivi, Sheila and Chu, Audrey Y and Murgia, Federico and Trompet, Stella and Imboden, Medea and Kollerits, Barbara and Pistis, Giorgio and Harris, Tamara B and Launer, Lenore J and Aspelund, Thor and Eiriksdottir, Gudny and Mitchell, Braxton D and Boerwinkle, Eric and Schmidt, Helena and Cavalieri, Margherita and Rao, Madhumathi and Hu, Frank B and Demirkan, Ayse and Oostra, Ben A and de Andrade, Mariza and Turner, Stephen T and Ding, Jingzhong and Andrews, Jeanette S and Freedman, Barry I and Koenig, Wolfgang and Illig, Thomas and D{\\"o}ring, Angela and Wichmann, H -Erich and Kolcic, Ivana and Zemunik, Tatijana and Boban, Mladen and Minelli, Cosetta and Wheeler, Heather E and Igl, Wilmar and Zaboli, Ghazal and Wild, Sarah H and Wright, Alan F and Campbell, Harry and Ellinghaus, David and N{\\"o}thlings, Ute and Jacobs, Gunnar and Biffar, Reiner and Endlich, Karlhans and Ernst, Florian and Homuth, Georg and Kroemer, Heyo K and Nauck, Matthias and Stracke, Sylvia and V{\\"o}lker, Uwe and V{\\"o}lzke, Henry and Kovacs, Peter and Stumvoll, Michael and M{\\"a}gi, Reedik and Hofman, Albert and Uitterlinden, Andre G and Rivadeneira, Fernando and Aulchenko, Yurii S and Polasek, Ozren and Hastie, Nick and Vitart, Veronique and Helmer, Catherine and Wang, Jie Jin and Ruggiero, Daniela and Bergmann, Sven and K{\\"a}h{\\"o}nen, Mika and Viikari, Jorma and Nikopensius, Tiit and Province, Michael and Ketkar, Shamika and Colhoun, Helen and Doney, Alex and Robino, Antonietta and Giulianini, Franco and Kr{\\"a}mer, Bernhard K and Portas, Laura and Ford, Ian and Buckley, Brendan M and Adam, Martin and Thun, Gian-Andri and Paulweber, Bernhard and Haun, Margot and Sala, Cinzia and Metzger, Marie and Mitchell, Paul and Ciullo, Marina and Kim, Stuart K and Vollenweider, Peter and Raitakari, Olli and Metspalu, Andres and Palmer, Colin and Gasparini, Paolo and Pirastu, Mario and Jukema, J Wouter and Probst-Hensch, Nicole M and Kronenberg, Florian and Toniolo, Daniela and Gudnason, Vilmundur and Shuldiner, Alan R and Coresh, Josef and Schmidt, Reinhold and Ferrucci, Luigi and Siscovick, David S and van Duijn, Cornelia M and Borecki, Ingrid and Kardia, Sharon L R and Liu, Yongmei and Curhan, Gary C and Rudan, Igor and Gyllensten, Ulf and Wilson, James F and Franke, Andre and Pramstaller, Peter P and Rettig, Rainer and Prokopenko, Inga and Witteman, Jacqueline C M and Hayward, Caroline and Ridker, Paul and Parsa, Afshin and Bochud, Murielle and Heid, Iris M and Goessling, Wolfram and Chasman, Daniel I and Kao, W H Linda and Fox, Caroline S and B{\\"o}ger, Carsten A and Chen, Ming-Huei and Tin, Adrienne and Olden, Matthias and K{\\"o}ttgen, Anna and de Boer, Ian H and Fuchsberger, Christian and O'Seaghdha, Conall M and Pattaro, Cristian and Teumer, Alexander and Liu, Ching-Ti and Glazer, Nicole L and Li, Man and O'Connell, Jeffrey R and Tanaka, Toshiko and Peralta, Carmen A and Kutalik, Zolt{\\'a}n and Luan, Jian'an and Zhao, Jing Hua and Hwang, Shih-Jen and Akylbekova, Ermeg and Kramer, Holly and van der Harst, Pim and Smith, Albert V and Lohman, Kurt and de Andrade, Mariza and Hayward, Caroline and Kollerits, Barbara and T{\\"o}njes, Anke and Aspelund, Thor and Ingelsson, Erik and Eiriksdottir, Gudny and Launer, Lenore J and Harris, Tamara B and Shuldiner, Alan R and Mitchell, Braxton D and Arking, Dan E and Franceschini, Nora and Boerwinkle, Eric and Egan, Josephine and Hernandez, Dena and Reilly, Muredach and Townsend, Raymond R and Lumley, Thomas and Siscovick, David S and Psaty, Bruce M and Kestenbaum, Bryan and Haritunians, Talin and Bergmann, Sven and Vollenweider, Peter and Waeber, Gerard and Mooser, Vincent and Waterworth, Dawn and Johnson, Andrew D and Florez, Jose C and Meigs, James B and Lu, Xiaoning and Turner, Stephen T and Atkinson, Elizabeth J and Leak, Tennille S and Aasar{\\o}d, Knut and Skorpen, Frank and Syv{\\"a}nen, Ann-Christine and Illig, Thomas and Baumert, Jens and Koenig, Wolfgang and Kr{\\"a}mer, Bernhard K and Devuyst, Olivier and Mychaleckyj, Josyf C and Minelli, Cosetta and Bakker, Stephan J L and Kedenko, Lyudmyla and Paulweber, Bernhard and Coassin, Stefan and Endlich, Karlhans and Kroemer, Heyo K and Biffar, Reiner and Stracke, Sylvia and V{\\"o}lzke, Henry and Stumvoll, Michael and M{\\"a}gi, Reedik and Campbell, Harry and Vitart, Veronique and Hastie, Nicholas D and Gudnason, Vilmundur and Kardia, Sharon L R and Liu, Yongmei and Polasek, Ozren and Curhan, Gary and Kronenberg, Florian and Prokopenko, Inga and Rudan, Igor and {\\"A}rnl{\\"o}v, Johan and Hallan, Stein and Navis, Gerjan and Parsa, Afshin and Ferrucci, Luigi and Coresh, Josef and Shlipak, Michael G and Bull, Shelley B and Paterson, Andrew D and Wichmann, H -Erich and Wareham, Nicholas J and Loos, Ruth J F and Rotter, Jerome I and Pramstaller, Peter P and Cupples, L Adrienne and Beckmann, Jacques S and Yang, Qiong and Heid, Iris M and Rettig, Rainer and Dreisbach, Albert W and Bochud, Murielle and Fox, Caroline S and Kao, W H L and Estrada, Karol and Styrkarsdottir, Unnur and Evangelou, Evangelos and Hsu, Yi-Hsiang and Duncan, Emma L and Ntzani, Evangelia E and Oei, Ling and Albagha, Omar M E and Amin, Najaf and Kemp, John P and Koller, Daniel L and Li, Guo and Liu, Ching-Ti and Minster, Ryan L and Moayyeri, Alireza and Vandenput, Liesbeth and Willner, Dana and Xiao, Su-Mei and Yerges-Armstrong, Laura M and Zheng, Hou-Feng and Alonso, Nerea and Eriksson, Joel and Kammerer, Candace M and Kaptoge, Stephen K and Leo, Paul J and Thorleifsson, Gudmar and Wilson, Scott G and Wilson, James F and Aalto, Ville and Alen, Markku and Aragaki, Aaron K and Aspelund, Thor and Center, Jacqueline R and Dailiana, Zoe and Duggan, David J and Garcia, Melissa and Garcia-Giralt, Nat{\\`a}lia and Giroux, Sylvie and Hallmans, G{\\"o}ran and Hocking, Lynne J and Husted, Lise Bjerre and Jameson, Karen A and Khusainova, Rita and Kim, Ghi Su and Kooperberg, Charles and Koromila, Theodora and Kruk, Marcin and Laaksonen, Marika and Lacroix, Andrea Z and Lee, Seung Hun and Leung, Ping C and Lewis, Joshua R and Masi, Laura and Mencej-Bedrac, Simona and Nguyen, Tuan V and Nogues, Xavier and Patel, Millan S and Prezelj, Janez and Rose, Lynda M and Scollen, Serena and Siggeirsdottir, Kristin and Smith, Albert V and Svensson, Olle and Trompet, Stella and Trummer, Olivia and van Schoor, Natasja M and Woo, Jean and Zhu, Kun and Balcells, Susana and Brandi, Maria Luisa and Buckley, Brendan M and Cheng, Sulin and Christiansen, Claus and Cooper, Cyrus and Dedoussis, George and Ford, Ian and Frost, Morten and Goltzman, David and Gonz{\\'a}lez-Mac{\\'\\i}as, Jes{\\'u}s and K{\\"a}h{\\"o}nen, Mika and Karlsson, Magnus and Khusnutdinova, Elza and Koh, Jung-Min and Kollia, Panagoula and Langdahl, Bente Lomholt and Leslie, William D and Lips, Paul and Ljunggren, {\\"O}sten and Lorenc, Roman S and Marc, Janja and Mellstr{\\"o}m, Dan and Obermayer-Pietsch, Barbara and Olmos, Jos{\\'e} M and Pettersson-Kymmer, Ulrika and Reid, David M and Riancho, Jos{\\'e} A and Ridker, Paul M and Rousseau, Fran{\\c c}ois and Slagboom, P Eline and Tang, Nelson L S and Urreizti, Roser and Van Hul, Wim and Viikari, Jorma and Zarrabeitia, Mar{\\'\\i}a T and Aulchenko, Yurii S and Castano-Betancourt, Martha and Grundberg, Elin and Herrera, Lizbeth and Ingvarsson, Thorvaldur and Johannsdottir, Hrefna and Kwan, Tony and Li, Rui and Luben, Robert and Medina-G{\\'o}mez, Carolina and Palsson, Stefan Th and Reppe, Sjur and Rotter, Jerome I and Sigurdsson, Gunnar and van Meurs, Joyce B J and Verlaan, Dominique and Williams, Frances M K and Wood, Andrew R and Zhou, Yanhua and Gautvik, Kaare M and Pastinen, Tomi and Raychaudhuri, Soumya and Cauley, Jane A and Chasman, Daniel I and Clark, Graeme R and Cummings, Steven R and Danoy, Patrick and Dennison, Elaine M and Eastell, Richard and Eisman, John A and Gudnason, Vilmundur and Hofman, Albert and Jackson, Rebecca D and Jones, Graeme and Jukema, J Wouter and Khaw, Kay-Tee and Lehtim{\\"a}ki, Terho and Liu, Yongmei and Lorentzon, Mattias and McCloskey, Eugene and Mitchell, Braxton D and Nandakumar, Kannabiran and Nicholson, Geoffrey C and Oostra, Ben A and Peacock, Munro and Pols, Huibert A P and Prince, Richard L and Raitakari, Olli and Reid, Ian R and Robbins, John and Sambrook, Philip N and Sham, Pak Chung and Shuldiner, Alan R and Tylavsky, Frances A and van Duijn, Cornelia M and Wareham, Nick J and Cupples, L Adrienne and Econs, Michael J and Evans, David M and Harris, Tamara B and Kung, Annie Wai Chee and Psaty, Bruce M and Reeve, Jonathan and Spector, Timothy D and Streeten, Elizabeth A and Zillikens, M Carola and Thorsteinsdottir, Unnur and Ohlsson, Claes and Karasik, David and Richards, J Brent and Brown, Matthew A and Stefansson, Kari and Uitterlinden, Andr{\\'e} G and Ralston, Stuart H and Ioannidis, John P A and Kiel, Douglas P and Rivadeneira, Fernando and Sandholm, Niina and Salem, Rany M and McKnight, Amy Jayne and Brennan, Eoin P and Forsblom, Carol and Isakova, Tamara and McKay, Gareth J and Williams, Winfred W and Sadlier, Denise M and M{\\"a}kinen, Ville-Petteri and Swan, Elizabeth J and Palmer, Cameron and Boright, Andrew P and Ahlqvist, Emma and Deshmukh, Harshal A and Keller, Benjamin J and Huang, Huateng and Ahola, Aila and Fagerholm, Emma and Gordin, Daniel and Harjutsalo, Valma and He, Bing and Heikkil{\\"a}, Outi and Hietala, Kustaa and Kyt{\\"o}, Janne and Lahermo, P{\\"a}ivi and Lehto, Markku and {\\"O}sterholm, Anne-May and Parkkonen, Maija and Pitk{\\"a}niemi, Janne and Roseng{\\aa}rd-B{\\"a}rlund, Milla and Saraheimo, Markku and Sarti, Cinzia and S{\\"o}derlund, Jenny and Soro-Paavonen, Aino and Syreeni, Anna and Thorn, Lena M and Tikkanen, Heikki and Tolonen, Nina and Tryggvason, Karl and Tuomilehto, Jaakko and Wad{\\'e}n, Johan and Gill, Geoffrey V and Prior, Sarah and Guiducci, Candace and Mirel, Daniel B and Taylor, Andrew and Hosseini, Mohsen and Parving, Hans-Henrik and Rossing, Peter and Tarnow, Lise and Ladenvall, Claes and Alhenc-Gelas, Fran{\\c c}ois and Lefebvre, Pierre and Rigalleau, Vincent and Roussel, Ronan and Tregouet, David-Alexandre and Maestroni, Anna and Maestroni, Silvia and Falhammar, Henrik and Gu, Tianwei and M{\\"o}llsten, Anna and Cimponeriu, Dan and Mihai, Ioana and Mota, Maria and Mota, Eugen and Serafinceanu, Cristian and Stavarachi, Monica and Hanson, Robert L and Nelson, Robert G and Kretzler, Matthias and Colhoun, Helen M and Panduru, Nicolae Mircea and Gu, Harvest F and Brismar, Kerstin and Zerbini, Gianpaolo and Hadjadj, Samy and Marre, Michel and Groop, Leif and Lajer, Maria and Bull, Shelley B and Waggott, Daryl and Paterson, Andrew D and Savage, David A and Bain, Stephen C and Martin, Finian and Hirschhorn, Joel N and Godson, Catherine and Florez, Jose C and Groop, Per-Henrik and Maxwell, Alexander P and Willer, Cristen J and Schmidt, Ellen M and Sengupta, Sebanti and Peloso, Gina M and Gustafsson, Stefan and Kanoni, Stavroula and Ganna, Andrea and Chen, Jin and Buchkovich, Martin L and Mora, Samia and Beckmann, Jacques S and Bragg-Gresham, Jennifer L and Chang, Hsing-Yi and Demirkan, Ay{\\c s}e and Den Hertog, Heleen M and Do, Ron and Donnelly, Louise A and Ehret, Georg B and Esko, T{\\~o}nu and Feitosa, Mary F and Ferreira, Teresa and Fischer, Krista and Fontanillas, Pierre and Fraser, Ross M and Freitag, Daniel F and Gurdasani, Deepti and Heikkil{\\"a}, Kauko and Hypp{\\"o}nen, Elina and Isaacs, Aaron and Jackson, Anne U and Johansson, {\\AA}sa and Johnson, Toby and Kaakinen, Marika and Kettunen, Johannes and Kleber, Marcus E and Li, Xiaohui and Luan, Jian'an and Lyytik{\\"a}inen, Leo-Pekka and Magnusson, Patrik K E and Mangino, Massimo and Mihailov, Evelin and Montasser, May E and M{\\"u}ller-Nurasyid, Martina and Nolte, Ilja M and O'Connell, Jeffrey R and Palmer, Cameron D and Perola, Markus and Petersen, Ann-Kristin and Sanna, Serena and Saxena, Richa and Service, Susan K and Shah, Sonia and Shungin, Dmitry and Sidore, Carlo and Song, Ci and Strawbridge, Rona J and Surakka, Ida and Tanaka, Toshiko and Teslovich, Tanya M and Thorleifsson, Gudmar and Van den Herik, Evita G and Voight, Benjamin F and Volcik, Kelly A and Waite, Lindsay L and Wong, Andrew and Wu, Ying and Zhang, Weihua and Absher, Devin and Asiki, Gershim and Barroso, In{\\^e}s and Been, Latonya F and Bolton, Jennifer L and Bonnycastle, Lori L and Brambilla, Paolo and Burnett, Mary S and Cesana, Giancarlo and Dimitriou, Maria and Doney, Alex S F and D{\\"o}ring, Angela and Elliott, Paul and Epstein, Stephen E and Eyjolfsson, Gudmundur Ingi and Gigante, Bruna and Goodarzi, Mark O and Grallert, Harald and Gravito, Martha L and Groves, Christopher J and Hallmans, G{\\"o}ran and Hartikainen, Anna-Liisa and Hayward, Caroline and Hernandez, Dena and Hicks, Andrew A and Holm, Hilma and Hung, Yi-Jen and Illig, Thomas and Jones, Michelle R and Kaleebu, Pontiano and Kastelein, John J P and Khaw, Kay-Tee and Kim, Eric and Klopp, Norman and Komulainen, Pirjo and Kumari, Meena and Langenberg, Claudia and Lehtim{\\"a}ki, Terho and Lin, Shih-Yi and Lindstr{\\"o}m, Jaana and Loos, Ruth J F and Mach, Fran{\\c c}ois and McArdle, Wendy L and Meisinger, Christa and Mitchell, Braxton D and M{\\"u}ller, Gabrielle and Nagaraja, Ramaiah and Narisu, Narisu and Nieminen, Tuomo V M and Nsubuga, Rebecca N and Olafsson, Isleifur and Ong, Ken K and Palotie, Aarno and Papamarkou, Theodore and Pomilla, Cristina and Pouta, Anneli and Rader, Daniel J and Reilly, Muredach P and Ridker, Paul M and Rivadeneira, Fernando and Rudan, Igor and Ruokonen, Aimo and Samani, Nilesh and Scharnagl, Hubert and Seeley, Janet and Silander, Kaisa and Stan{\\v c}{\\'a}kov{\\'a}, Alena and Stirrups, Kathleen and Swift, Amy J and Tiret, Laurence and Uitterlinden, Andre G and van Pelt, L Joost and Vedantam, Sailaja and Wainwright, Nicholas and Wijmenga, Cisca and Wild, Sarah H and Willemsen, Gonneke and Wilsgaard, Tom and Wilson, James F and Young, Elizabeth H and Zhao, Jing Hua and Adair, Linda S and Arveiler, Dominique and Assimes, Themistocles L and Bandinelli, Stefania and Bennett, Franklyn and Bochud, Murielle and Boehm, Bernhard O and Boomsma, Dorret I and Borecki, Ingrid B and Bornstein, Stefan R and Bovet, Pascal and Burnier, Michel and Campbell, Harry and Chakravarti, Aravinda and Chambers, John C and Chen, Yii-Der Ida and Collins, Francis S and Cooper, Richard S and Danesh, John and Dedoussis, George and de Faire, Ulf and Feranil, Alan B and Ferri{\\`e}res, Jean and Ferrucci, Luigi and Freimer, Nelson B and Gieger, Christian and Groop, Leif C and Gudnason, Vilmundur and Gyllensten, Ulf and Hamsten, Anders and Harris, Tamara B and Hingorani, Aroon and Hirschhorn, Joel N and Hofman, Albert and Hovingh, G Kees and Hsiung, Chao Agnes and Humphries, Steve E and Hunt, Steven C and Hveem, Kristian and Iribarren, Carlos and J{\\"a}rvelin, Marjo-Riitta and Jula, Antti and K{\\"a}h{\\"o}nen, Mika and Kaprio, Jaakko and Kes{\\"a}niemi, Antero and Kivimaki, Mika and Kooner, Jaspal S and Koudstaal, Peter J and Krauss, Ronald M and Kuh, Diana and Kuusisto, Johanna and Kyvik, Kirsten O and Laakso, Markku and Lakka, Timo A and Lind, Lars and Lindgren, Cecilia M and Martin, Nicholas G and M{\\"a}rz, Winfried and McCarthy, Mark I and McKenzie, Colin A and Meneton, Pierre and Metspalu, Andres and Moilanen, Leena and Morris, Andrew D and Munroe, Patricia B and Nj{\\o}lstad, Inger and Pedersen, Nancy L and Power, Chris and Pramstaller, Peter P and Price, Jackie F and Psaty, Bruce M and Quertermous, Thomas and Rauramaa, Rainer and Saleheen, Danish and Salomaa, Veikko and Sanghera, Dharambir K and Saramies, Jouko and Schwarz, Peter E H and Sheu, Wayne H-H and Shuldiner, Alan R and Siegbahn, Agneta and Spector, Tim D and Stefansson, Kari and Strachan, David P and Tayo, Bamidele O and Tremoli, Elena and Tuomilehto, Jaakko and Uusitupa, Matti and van Duijn, Cornelia M and Vollenweider, Peter and Wallentin, Lars and Wareham, Nicholas J and Whitfield, John B and Wolffenbuttel, Bruce H R and Ordovas, Jose M and Boerwinkle, Eric and Palmer, Colin N A and Thorsteinsdottir, Unnur and Chasman, Daniel I and Rotter, Jerome I and Franks, Paul W and Ripatti, Samuli and Cupples, L Adrienne and Sandhu, Manjinder S and Rich, Stephen S and Boehnke, Michael and Deloukas, Panos and Kathiresan, Sekar and Mohlke, Karen L and Ingelsson, Erik and Abecasis, Gon{\\c c}alo R and Abecasis, Gon{\\c c}alo and Bochud, Murielle and Caulfield, Mark and Chakravarti, Aravinda and Chasman, Dan and Ehret, Georg and Elliott, Paul and Johnson, Andrew and Johnson, Louise and Larson, Martin and Levy, Daniel and Munroe, Patricia and Newton-Cheh, Christopher and O'Reilly, Paul and Palmas, Walter and Psaty, Bruce and Rice, Kenneth and Smith, Albert and Snider, Harold and Tobin, Martin and Van Duijn, Cornelia and Verwoert, Germaine and Ehret, Georg B and Munroe, Patricia B and Rice, Kenneth M and Bochud, Murielle and Johnson, Andrew D and Chasman, Daniel I and Smith, Albert V and Tobin, Martin D and Verwoert, Germaine C and Hwang, Shih-Jen and Pihur, Vasyl and Vollenweider, Peter and O'Reilly, Paul F and Amin, Najaf and Bragg-Gresham, Jennifer L and Teumer, Alexander and Glazer, Nicole L and Launer, Lenore and Zhao, Jing Hua and Aulchenko, Yurii and Heath, Simon and S{\\~o}ber, Siim and Parsa, Afshin and Luan, Jian'an and Arora, Pankaj and Dehghan, Abbas and Zhang, Feng and Lucas, Gavin and Hicks, Andrew A and Jackson, Anne U and Peden, John F and Tanaka, Toshiko and Wild, Sarah H and Rudan, Igor and Igl, Wilmar and Milaneschi, Yuri and Parker, Alex N and Fava, Cristiano and Chambers, John C and Fox, Ervin R and Kumari, Meena and Go, Min Jin and van der Harst, Pim and Kao, Wen Hong Linda and Sj{\\"o}gren, Marketa and Vinay, D G and Alexander, Myriam and Tabara, Yasuharu and Shaw-Hawkins, Sue and Whincup, Peter H and Liu, Yongmei and Shi, Gang and Kuusisto, Johanna and Tayo, Bamidele and Seielstad, Mark and Sim, Xueling and Nguyen, Khanh-Dung Hoang and Lehtim{\\"a}ki, Terho and Matullo, Giuseppe and Wu, Ying and Gaunt, Tom R and Onland-Moret, N Charlotte and Cooper, Matthew N and Platou, Carl G P and Org, Elin and Hardy, Rebecca and Dahgam, Santosh and Palmen, Jutta and Vitart, Veronique and Braund, Peter S and Kuznetsova, Tatiana and Uiterwaal, Cuno S P M and Adeyemo, Adebowale and Palmas, Walter and Campbell, Harry and Ludwig, Barbara and Tomaszewski, Maciej and Tzoulaki, Ioanna and Palmer, Nicholette D and Aspelund, Thor and Garcia, Melissa and Chang, Yen-Pei C and O'Connell, Jeffrey R and Steinle, Nanette I and Grobbee, Diederick E and Arking, Dan E and Kardia, Sharon L and Morrison, Alanna C and Hernandez, Dena and Najjar, Samer and McArdle, Wendy L and Hadley, David and Brown, Morris J and Connell, John M and Hingorani, Aroon D and Day, Ian N M and Lawlor, Debbie A and Beilby, John P and Lawrence, Robert W and Clarke, Robert and Collins, Rory and Hopewell, Jemma C and Ongen, Halit and Dreisbach, Albert W and Li, Yali and Young, J H and Bis, Joshua C and K{\\"a}h{\\"o}nen, Mika and Viikari, Jorma and Adair, Linda S and Lee, Nanette R and Chen, Ming-Huei and Olden, Matthias and Pattaro, Cristian and Bolton, Judith A Hoffman and K{\\"o}ttgen, Anna and Bergmann, Sven and Mooser, Vincent and Chaturvedi, Nish and Frayling, Timothy M and Islam, Muhammad and Jafar, Tazeen H and Erdmann, Jeanette and Kulkarni, Smita R and Bornstein, Stefan R and Gr{\\"a}ssler, J{\\"u}rgen and Groop, Leif and Voight, Benjamin F and Kettunen, Johannes and Howard, Philip and Taylor, Andrew and Guarrera, Simonetta and Ricceri, Fulvio and Emilsson, Valur and Plump, Andrew and Barroso, In{\\^e}s and Khaw, Kay-Tee and Weder, Alan B and Hunt, Steven C and Sun, Yan V and Bergman, Richard N and Collins, Francis S and Bonnycastle, Lori L and Scott, Laura J and Stringham, Heather M and Peltonen, Leena and Perola, Markus and Vartiainen, Erkki and Brand, Stefan-Martin and Staessen, Jan A and Wang, Thomas J and Burton, Paul R and Artigas, Maria Soler and Dong, Yanbin and Snieder, Harold and Wang, Xiaoling and Zhu, Haidong and Lohman, Kurt K and Rudock, Megan E and Heckbert, Susan R and Smith, Nicholas L and Wiggins, Kerri L and Doumatey, Ayo and Shriner, Daniel and Veldre, Gudrun and Viigimaa, Margus and Kinra, Sanjay and Prabhakaran, Dorairajan and Tripathy, Vikal and Langefeld, Carl D and Rosengren, Annika and Thelle, Dag S and Corsi, Anna Maria and Singleton, Andrew and Forrester, Terrence and Hilton, Gina and McKenzie, Colin A and Salako, Tunde and Iwai, Naoharu and Kita, Yoshikuni and Ogihara, Toshio and Ohkubo, Takayoshi and Okamura, Tomonori and Ueshima, Hirotsugu and Umemura, Satoshi and Eyheramendy, Susana and Meitinger, Thomas and Wichmann, H-Erich and Cho, Yoon Shin and Kim, Hyung-Lae and Lee, Jong-Young and Scott, James and Sehmi, Joban S and Zhang, Weihua and Hedblad, Bo and Nilsson, Peter and Smith, George Davey and Wong, Andrew and Narisu, Narisu and Stan{\\`e}{\\'a}kov{\\'a}, Alena and Raffel, Leslie J and Yao, Jie and Kathiresan, Sekar and O'Donnell, Chris and Schwartz, Stephen M and Ikram, M Arfan and Longstreth, W T and Mosley, Thomas H and Seshadri, Sudha and Shrine, Nick R G and Wain, Louise V and Morken, Mario A and Swift, Amy J and Laitinen, Jaana and Prokopenko, Inga and Zitting, Paavo and Cooper, Jackie A and Humphries, Steve E and Danesh, John and Rasheed, Asif and Goel, Anuj and Hamsten, Anders and Watkins, Hugh and Bakker, Stephan J L and van Gilst, Wiek H and Janipalli, Charles S and Mani, K Radha and Yajnik, Chittaranjan S and Hofman, Albert and Mattace-Raso, Francesco U S and Oostra, Ben A and Demirkan, Ayse and Isaacs, Aaron and Rivadeneira, Fernando and Lakatta, Edward G and Orru, Marco and Scuteri, Angelo and Ala-Korpela, Mika and Kangas, Antti J and Lyytik{\\"a}inen, Leo-Pekka and Soininen, Pasi and Tukiainen, Taru and W{\\"u}rtz, Peter and Ong, Rick Twee-Hee and D{\\"o}rr, Marcus and Kroemer, Heyo K and V{\\"o}lker, Uwe and V{\\"o}lzke, Henry and Galan, Pilar and Hercberg, Serge and Lathrop, Mark and Zelenika, Diana and Deloukas, Panos and Mangino, Massimo and Spector, Tim D and Zhai, Guangju and Meschia, James F and Nalls, Michael A and Sharma, Pankaj and Terzic, Janos and Kumar, M J Kranthi and Denniff, Matthew and Zukowska-Szczechowska, Ewa and Wagenknecht, Lynne E and Fowkes, F Gerald R and Charchar, Fadi J and Schwarz, Peter E H and Hayward, Caroline and Guo, Xiuqing and Rotimi, Charles and Bots, Michiel L and Brand, Eva and Samani, Nilesh J and Polasek, Ozren and Talmud, Philippa J and Nyberg, Fredrik and Kuh, Diana and Laan, Maris and Hveem, Kristian and Palmer, Lyle J and van der Schouw, Yvonne T and Casas, Juan P and Mohlke, Karen L and Vineis, Paolo and Raitakari, Olli and Ganesh, Santhi K and Wong, Tien Y and Tai, E Shyong and Cooper, Richard S and Laakso, Markku and Rao, Dabeeru C and Harris, Tamara B and Morris, Richard W and Dominiczak, Anna F and Kivimaki, Mika and Marmot, Michael G and Miki, Tetsuro and Saleheen, Danish and Chandak, Giriraj R and Coresh, Josef and Navis, Gerjan and Salomaa, Veikko and Han, Bok-Ghee and Zhu, Xiaofeng and Kooner, Jaspal S and Melander, Olle and Ridker, Paul M and Bandinelli, Stefania and Gyllensten, Ulf B and Wright, Alan F and Wilson, James F and Ferrucci, Luigi and Farrall, Martin and Tuomilehto, Jaakko and Pramstaller, Peter P and Elosua, Roberto and Soranzo, Nicole and Sijbrands, Eric J G and Altshuler, David and Loos, Ruth J F and Shuldiner, Alan R and Gieger, Christian and Meneton, Pierre and Uitterlinden, Andre G and Wareham, Nicholas J and Gudnason, Vilmundur and Rotter, Jerome I and Rettig, Rainer and Uda, Manuela and Strachan, David P and Witteman, Jacqueline C M and Hartikainen, Anna-Liisa and Beckmann, Jacques S and Boerwinkle, Eric and Vasan, Ramachandran S and Boehnke, Michael and Larson, Martin G and J{\\"a}rvelin, Marjo-Riitta and Psaty, Bruce M and Abecasis, Gon{\\c c}alo R and Chakravarti, Aravinda and Elliott, Paul and van Duijn, Cornelia M and Newton-Cheh, Christopher and Levy, Daniel and Caulfield, Mark J and Johnson, Toby and Anderson, Carl A and Gordon, Scott D and Guo, Qun and Henders, Anjali K and Lambert, Ann and Lee, Sang Hong and Kraft, Peter and Kennedy, Stephen H and Macgregor, Stuart and Martin, Nicholas G and Missmer, Stacey A and Montgomery, Grant W and Morris, Andrew P and Nyholt, Dale R and Painter, Jodie N and Roseman, Fenella and Treloar, Susan A and Visscher, Peter M and Wallace, Leanne and Zondervan, Krina T and Alizadeh, Behrooz Z and de Boer, Rudolf A and Boezen, H Marike and Bruinenberg, Marcel and Franke, Lude and van der Harst, Pim and Hillege, Hans L and van der Klauw, Melanie M and Navis, Gerjan and Ormel, Johan and Postma, Dirkje S and Rosmalen, Judith G M and Slaets, Joris P and Snieder, Harold and Stolk, Ronald P and Wolffenbuttel, Bruce H R and Wijmenga, Cisca and Scott, Robert A and Lagou, Vasiliki and Welch, Ryan P and Wheeler, Eleanor and Montasser, May E and Luan, Jian'an and M{\\"a}gi, Reedik and Strawbridge, Rona J and Rehnberg, Emil and Gustafsson, Stefan and Kanoni, Stavroula and Rasmussen-Torvik, Laura J and Yengo, Lo{\\"\\i}c and Lecoeur, Cecile and Shungin, Dmitry and Sanna, Serena and Sidore, Carlo and Johnson, Paul C D and Jukema, J Wouter and Johnson, Toby and Mahajan, Anubha and Verweij, Niek and Thorleifsson, Gudmar and Hottenga, Jouke-Jan and Shah, Sonia and Smith, Albert V and Sennblad, Bengt and Gieger, Christian and Salo, Perttu and Perola, Markus and Timpson, Nicholas J and Evans, David M and St Pourcain, Beate and Wu, Ying and Andrews, Jeanette S and Hui, Jennie and Bielak, Lawrence F and Zhao, Wei and Horikoshi, Momoko and Navarro, Pau and Isaacs, Aaron and O'Connell, Jeffrey R and Stirrups, Kathleen and Vitart, Veronique and Hayward, Caroline and Esko, T{\\"o}nu and Mihailov, Evelin and Fraser, Ross M and Fall, Tove and Voight, Benjamin F and Raychaudhuri, Soumya and Chen, Han and Lindgren, Cecilia M and Morris, Andrew P and Rayner, Nigel W and Robertson, Neil and Rybin, Denis and Liu, Ching-Ti and Beckmann, Jacques S and Willems, Sara M and Chines, Peter S and Jackson, Anne U and Kang, Hyun Min and Stringham, Heather M and Song, Kijoung and Tanaka, Toshiko and Peden, John F and Goel, Anuj and Hicks, Andrew A and An, Ping and M{\\"u}ller-Nurasyid, Martina and Franco-Cereceda, Anders and Folkersen, Lasse and Marullo, Letizia and Jansen, Hanneke and Oldehinkel, Albertine J and Bruinenberg, Marcel and Pankow, James S and North, Kari E and Forouhi, Nita G and Loos, Ruth J F and Edkins, Sarah and Varga, Tibor V and Hallmans, G{\\"o}ran and Oksa, Heikki and Antonella, Mulas and Nagaraja, Ramaiah and Trompet, Stella and Ford, Ian and Bakker, Stephan J L and Kong, Augustine and Kumari, Meena and Gigante, Bruna and Herder, Christian and Munroe, Patricia B and Caulfield, Mark and Antti, Jula and Mangino, Massimo and Small, Kerrin and Miljkovic, Iva and Liu, Yongmei and Atalay, Mustafa and Kiess, Wieland and James, Alan L and Rivadeneira, Fernando and Uitterlinden, Andre G and Palmer, Colin N A and Doney, Alex S F and Willemsen, Gonneke and Smit, Johannes H and Campbell, Susan and Polasek, Ozren and Bonnycastle, Lori L and Hercberg, Serge and Dimitriou, Maria and Bolton, Jennifer L and Fowkes, Gerard R and Kovacs, Peter and Lindstr{\\"o}m, Jaana and Zemunik, Tatijana and Bandinelli, Stefania and Wild, Sarah H and Basart, Hanneke V and Rathmann, Wolfgang and Grallert, Harald and Maerz, Winfried and Kleber, Marcus E and Boehm, Bernhard O and Peters, Annette and Pramstaller, Peter P and Province, Michael A and Borecki, Ingrid B and Hastie, Nicholas D and Rudan, Igor and Campbell, Harry and Watkins, Hugh and Farrall, Martin and Stumvoll, Michael and Ferrucci, Luigi and Waterworth, Dawn M and Bergman, Richard N and Collins, Francis S and Tuomilehto, Jaakko and Watanabe, Richard M and de Geus, Eco J C and Penninx, Brenda W and Hofman, Albert and Oostra, Ben A and Psaty, Bruce M and Vollenweider, Peter and Wilson, James F and Wright, Alan F and Hovingh, G Kees and Metspalu, Andres and Uusitupa, Matti and Magnusson, Patrik K E and Kyvik, Kirsten O and Kaprio, Jaakko and Price, Jackie F and Dedoussis, George V and Deloukas, Panos and Meneton, Pierre and Lind, Lars and Boehnke, Michael and Shuldiner, Alan R and van Duijn, Cornelia M and Morris, Andrew D and Toenjes, Anke and Peyser, Patricia A and Beilby, John P and K{\\"o}rner, Antje and Kuusisto, Johanna and Laakso, Markku and Bornstein, Stefan R and Schwarz, Peter E H and Lakka, Timo A and Rauramaa, Rainer and Adair, Linda S and Smith, George Davey and Spector, Tim D and Illig, Thomas and de Faire, Ulf and Hamsten, Anders and Gudnason, Vilmundur and Kivimaki, Mika and Hingorani, Aroon and Keinanen-Kiukaanniemi, Sirkka M and Saaristo, Timo E and Boomsma, Dorret I and Stefansson, Kari and van der Harst, Pim and Dupuis, Jos{\\'e}e and Pedersen, Nancy L and Sattar, Naveed and Harris, Tamara B and Cucca, Francesco and Ripatti, Samuli and Salomaa, Veikko and Mohlke, Karen L and Balkau, Beverley and Froguel, Philippe and Pouta, Anneli and Jarvelin, Marjo-Riitta and Wareham, Nicholas J and Bouatia-Naji, Nabila and McCarthy, Mark I and Franks, Paul W and Meigs, James B and Teslovich, Tanya M and Florez, Jose C and Langenberg, Claudia and Ingelsson, Erik and Prokopenko, Inga and Barroso, In{\\^e}s and Ahmadi, Kourosh R and Ainali, Chrysanthi and Barrett, Amy and Bataille, Veronique and Bell, Jordana T and Buil, Alfonso and Deloukas, Panos and Dermitzakis, Emmanouil T and Dimas, Antigone S and Durbin, Richard and Glass, Daniel and Grundberg, Elin and Hassanali, Neelam and Hedman, {\\AA}sa K and Ingle, Catherine and Keildson, Sarah and Knowles, David and Krestyaninova, Maria and Lindgren, Cecilia M and Lowe, Christopher E and McCarthy, Mark I and Meduri, Eshwar and di Meglio, Paola and Min, Josine L and Montgomery, Stephen B and Nestle, Frank O and Nica, Alexandra C and Nisbet, James and O'Rahilly, Stephen and Parts, Leopold and Potter, Simon and Sekowska, Magdalena and Shin, So-Youn and Small, Kerrin S and Soranzo, Nicole and Spector, Tim D and Surdulescu, Gabriela and Travers, Mary E and Tsaprouni, Loukia and Tsoka, Sophia and Wilk, Alicja and Yang, Tsun-Po and Zondervan, Krina T and Matise, Tara and Buyske, Steve and Higashio, Julia and Williams, Rasheeda and Nato, Andrew and Ambite, Jose Luis and Deelman, Ewa and Manolio, Teri and Hindorff, Lucia and North, Kari E and Heiss, Gerardo and Taylor, Kira and Franceschini, Nora and Avery, Christy and Graff, Misa and Lin, Danyu and Quibrera, Miguel and Cochran, Barbara and Kao, Linda and Umans, Jason and Cole, Shelley and MacCluer, Jean and Person, Sharina and Pankow, James and Gross, Myron and Boerwinkle, Eric and Fornage, Myriam and Durda, Peter and Jenny, Nancy and Patsy, Bruce and Arnold, Alice and Buzkova, Petra and Crawford, Dana and Haines, Jonathan and Murdock, Deborah and Glenn, Kim and Brown-Gentry, Kristin and Thornton-Wells, Tricia and Dumitrescu, Logan and Jeff, Janina and Bush, William S and Mitchell, Sabrina L and Goodloe, Robert and Wilson, Sarah and Boston, Jonathan and Malinowski, Jennifer and Restrepo, Nicole and Oetjens, Matthew and Fowke, Jay and Zheng, Wei and Spencer, Kylee and Ritchie, Marylyn and Pendergrass, Sarah and Le Marchand, Lo{\\"\\i}c and Wilkens, Lynne and Park, Lani and Tiirikainen, Maarit and Kolonel, Laurence and Lim, Unhee and Cheng, Iona and Wang, Hansong and Shohet, Ralph and Haiman, Christopher and Stram, Daniel and Henderson, Brian and Monroe, Kristine and Schumacher, Fredrick and Kooperberg, Charles and Peters, Ulrike and Anderson, Garnet and Carlson, Chris and Prentice, Ross and LaCroix, Andrea and Wu, Chunyuan and Carty, Cara and Gong, Jian and Rosse, Stephanie and Young, Alicia and Haessler, Jeff and Kocarnik, Jonathan and Lin, Yi and Jackson, Rebecca and Duggan, David and Kuller, Lew and Stolk, Lisette and Perry, John R B and Chasman, Daniel I and He, Chunyan and Mangino, Massimo and Sulem, Patrick and Barbalic, Maja and Broer, Linda and Byrne, Enda M and Ernst, Florian and Esko, T{\\~o}nu and Franceschini, Nora and Gudbjartsson, Daniel F and Hottenga, Jouke-Jan and Kraft, Peter and McArdle, Patick F and Porcu, Eleonora and Shin, So-Youn and Smith, Albert V and van Wingerden, Sophie and Zhai, Guangju and Zhuang, Wei V and Albrecht, Eva and Alizadeh, Behrooz Z and Aspelund, Thor and Bandinelli, Stefania and Lauc, Lovorka Barac and Beckmann, Jacques S and Boban, Mladen and Boerwinkle, Eric and Broekmans, Frank J and Burri, Andrea and Campbell, Harry and Chanock, Stephen J and Chen, Constance and Cornelis, Marilyn C and Corre, Tanguy and Coviello, Andrea D and d'Adamo, Pio and Davies, Gail and de Faire, Ulf and de Geus, Eco J C and Deary, Ian J and Dedoussis, George V Z and Deloukas, Panagiotis and Ebrahim, Shah and Eiriksdottir, Gudny and Emilsson, Valur and Eriksson, Johan G and Fauser, Bart C J M and Ferreli, Liana and Ferrucci, Luigi and Fischer, Krista and Folsom, Aaron R and Garcia, Melissa E and Gasparini, Paolo and Gieger, Christian and Glazer, Nicole and Grobbee, Diederick E and Hall, Per and Haller, Toomas and Hankinson, Susan E and Hass, Merli and Hayward, Caroline and Heath, Andrew C and Hofman, Albert and Ingelsson, Erik and Janssens, A Cecile J W and Johnson, Andrew D and Karasik, David and Kardia, Sharon L R and Keyzer, Jules and Kiel, Douglas P and Kolcic, Ivana and Kutalik, Zolt{\\'a}n and Lahti, Jari and Lai, Sandra and Laisk, Triin and Laven, Joop S E and Lawlor, Debbie A and Liu, Jianjun and Lopez, Lorna M and Louwers, Yvonne V and Magnusson, Patrik K E and Marongiu, Mara and Martin, Nicholas G and Klaric, Irena Martinovic and Masciullo, Corrado and McKnight, Barbara and Medland, Sarah E and Melzer, David and Mooser, Vincent and Navarro, Pau and Newman, Anne B and Nyholt, Dale R and Onland-Moret, N Charlotte and Palotie, Aarno and Par{\\'e}, Guillaume and Parker, Alex N and Pedersen, Nancy L and Peeters, Petra H M and Pistis, Giorgio and Plump, Andrew S and Polasek, Ozren and Pop, Victor J M and Psaty, Bruce M and R{\\"a}ikk{\\"o}nen, Katri and Rehnberg, Emil and Rotter, Jerome I and Rudan, Igor and Sala, Cinzia and Salumets, Andres and Scuteri, Angelo and Singleton, Andrew and Smith, Jennifer A and Snieder, Harold and Soranzo, Nicole and Stacey, Simon N and Starr, John M and Stathopoulou, Maria G and Stirrups, Kathleen and Stolk, Ronald P and Styrkarsdottir, Unnur and Sun, Yan V and Tenesa, Albert and Thorand, Barbara and Toniolo, Daniela and Tryggvadottir, Laufey and Tsui, Kim and Ulivi, Sheila and van Dam, Rob M and van der Schouw, Yvonne T and van Gils, Carla H and van Nierop, Peter and Vink, Jacqueline M and Visscher, Peter M and Voorhuis, Marlies and Waeber, G{\\'e}rard and Wallaschofski, Henri and Wichmann, H Erich and Widen, Elisabeth and Wijnands-van Gent, Colette J M and Willemsen, Gonneke and Wilson, James F and Wolffenbuttel, Bruce H R and Wright, Alan F and Yerges-Armstrong, Laura M and Zemunik, Tatijana and Zgaga, Lina and Zillikens, M Carola and Zygmunt, Marek and Arnold, Alice M and Boomsma, Dorret I and Buring, Julie E and Crisponi, Laura and Demerath, Ellen W and Gudnason, Vilmundur and Harris, Tamara B and Hu, Frank B and Hunter, David J and Launer, Lenore J and Metspalu, Andres and Montgomery, Grant W and Oostra, Ben A and Ridker, Paul M and Sanna, Serena and Schlessinger, David and Spector, Tim D and Stefansson, Kari and Streeten, Elizabeth A and Thorsteinsdottir, Unnur and Uda, Manuela and Uitterlinden, Andr{\\'e} G and van Duijn, Cornelia M and V{\\"o}lzke, Henry and Murray, Anna and Murabito, Joanne M and Visser, Jenny A and Lunetta, Kathryn L and Elks, Cathy E and Perry, John R B and Sulem, Patrick and Chasman, Daniel I and Franceschini, Nora and He, Chunyan and Lunetta, Kathryn L and Visser, Jenny A and Byrne, Enda M and Cousminer, Diana L and Gudbjartsson, Daniel F and Esko, T{\\~o}nu and Feenstra, Bjarke and Hottenga, Jouke-Jan and Koller, Daniel L and Kutalik, Zolt{\\'a}n and Lin, Peng and Mangino, Massimo and Marongiu, Mara and McArdle, Patrick F and Smith, Albert V and Stolk, Lisette and van Wingerden, Sophie W and Zhao, Jing Hua and Albrecht, Eva and Corre, Tanguy and Ingelsson, Erik and Hayward, Caroline and Magnusson, Patrik K E and Smith, Erin N and Ulivi, Shelia and Warrington, Nicole M and Zgaga, Lina and Alavere, Helen and Amin, Najaf and Aspelund, Thor and Bandinelli, Stefania and Barroso, Ines and Berenson, Gerald S and Bergmann, Sven and Blackburn, Hannah and Boerwinkle, Eric and Buring, Julie E and Busonero, Fabio and Campbell, Harry and Chanock, Stephen J and Chen, Wei and Cornelis, Marilyn C and Couper, David and Coviello, Andrea D and d'Adamo, Pio and de Faire, Ulf and de Geus, Eco J C and Deloukas, Panos and D{\\"o}ring, Angela and Smith, George Davey and Easton, Douglas F and Eiriksdottir, Gudny and Emilsson, Valur and Eriksson, Johan and Ferrucci, Luigi and Folsom, Aaron R and Foroud, Tatiana and Garcia, Melissa and Gasparini, Paolo and Geller, Frank and Gieger, Christian and Gudnason, Vilmundur and Hall, Per and Hankinson, Susan E and Ferreli, Liana and Heath, Andrew C and Hernandez, Dena G and Hofman, Albert and Hu, Frank B and Illig, Thomas and J{\\"a}rvelin, Marjo-Riitta and Johnson, Andrew D and Karasik, David and Khaw, Kay-Tee and Kiel, Douglas P and Kilpel{\\"a}inen, Tuomas O and Kolcic, Ivana and Kraft, Peter and Launer, Lenore J and Laven, Joop S E and Li, Shengxu and Liu, Jianjun and Levy, Daniel and Martin, Nicholas G and McArdle, Wendy L and Melbye, Mads and Mooser, Vincent and Murray, Jeffrey C and Murray, Sarah S and Nalls, Michael A and Navarro, Pau and Nelis, Mari and Ness, Andrew R and Northstone, Kate and Oostra, Ben A and Peacock, Munro and Palmer, Lyle J and Palotie, Aarno and Par{\\'e}, Guillaume and Parker, Alex N and Pedersen, Nancy L and Peltonen, Leena and Pennell, Craig E and Pharoah, Paul and Polasek, Ozren and Plump, Andrew S and Pouta, Anneli and Porcu, Eleonora and Rafnar, Thorunn and Rice, John P and Ring, Susan M and Rivadeneira, Fernando and Rudan, Igor and Sala, Cinzia and Salomaa, Veikko and Sanna, Serena and Schlessinger, David and Schork, Nicholas J and Scuteri, Angelo and Segr{\\`e}, Ayellet V and Shuldiner, Alan R and Soranzo, Nicole and Sovio, Ulla and Srinivasan, Sathanur R and Strachan, David P and Tammesoo, Mar-Liis and Tikkanen, Emmi and Toniolo, Daniela and Tsui, Kim and Tryggvadottir, Laufey and Tyrer, Jonathon and Uda, Manuela and van Dam, Rob M and van Meurs, Joyve B J and Vollenweider, Peter and Waeber, Gerard and Wareham, Nicholas J and Waterworth, Dawn M and Weedon, Michael N and Wichmann, H Erich and Willemsen, Gonneke and Wilson, James F and Wright, Alan F and Young, Lauren and Zhai, Guangju and Zhuang, Wei Vivian and Bierut, Laura J and Boomsma, Dorret I and Boyd, Heather A and Crisponi, Laura and Demerath, Ellen W and van Duijn, Cornelia M and Econs, Michael J and Harris, Tamara B and Hunter, David J and Loos, Ruth J F and Metspalu, Andres and Montgomery, Grant W and Ridker, Paul M and Spector, Tim D and Streeten, Elizabeth A and Stefansson, Kari and Thorsteinsdottir, Unnur and Uitterlinden, Andr{\\'e} G and Widen, Elisabeth and Murabito, Joanne M and Ong, Ken K and Murray, Anna},\n\tissn = {1476-4687},\n\tissn-linking = {0028-0836},\n\tissue = {7538},\n\tjournal = {Nature},\n\tkeywords = {Adipocytes, metabolism; Adipogenesis, genetics; Adipose Tissue, metabolism; Age Factors; Body Fat Distribution; Body Mass Index; Continental Population Groups, genetics; Epigenesis, Genetic; Europe, ethnology; Female; Genome, Human, genetics; Genome-Wide Association Study; Humans; Insulin, metabolism; Insulin Resistance, genetics; Male; Models, Biological; Neovascularization, Physiologic, genetics; Obesity, genetics; Polymorphism, Single Nucleotide, genetics; Quantitative Trait Loci, genetics; Sex Characteristics; Transcription, Genetic, genetics; Waist-Hip Ratio},\n\tmid = {EMS61357},\n\tmonth = feb,\n\tnlm = {EMS61357},\n\tnlm-id = {0410462},\n\towner = {NLM},\n\tpages = {187--196},\n\tpmc = {PMC4338562},\n\tpmid = {25673412},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/25673412/},\n\n\tpubmodel = {Print},\n\tpubstate = {ppublish},\n\trevised = {2020-03-27},\n\ttitle = {New genetic loci link adipose and insulin biology to body fat distribution.},\n\tvolume = {518},\n\tyear = {2015},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/25673412/},\n\tbdsk-url-2 = {https://doi.org/10.1038/nature14132}}\n\n

\n\n\n\n\n\n

\n\n

\n \n 2014\n \n \n (20)\n \n \n

\n \n \n

\n \n\n \n \n \n \n \n \n Prospective associations of coronary heart disease loci in African Americans using the Metabochip: the PAGE study.\n \n \n \n \n\n\n \n Franceschini, N.; Hu, Y.; Reiner, A. P.; Buyske, S.; Nalls, M.; Yanek, L. R.; Li, Y.; Hindorff, L. A.; Cole, S. A.; Howard, B. V.; Stafford, J. M.; Carty, C. L.; Sethupathy, P.; Martin, L. W.; Lin, D.; Johnson, K. C.; Becker, L. C.; North, K. E.; Dehghan, A.; Bis, J. C.; Liu, Y.; Greenland, P.; Manson, J. E.; Maeda, N.; Garcia, M.; Harris, T. B.; Becker, D. M.; O'Donnell, C.; Heiss, G.; Kooperberg, C.; and Boerwinkle, E.\n\n\n \n\n\n\n PloS one, 9: e113203. 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Prospective$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{FranceschiniHuReinerEtAl2014,\n\tabstract = {Coronary heart disease (CHD) is a leading cause of morbidity and mortality in African Americans. However, there is a paucity of studies assessing genetic determinants of CHD in African Americans. We examined the association of published variants in CHD loci with incident CHD, attempted to fine map these loci, and characterize novel variants influencing CHD risk in African Americans. Up to 8,201 African Americans (including 546 first CHD events) were genotyped using the MetaboChip array in the Atherosclerosis Risk in Communities (ARIC) study and Women's Health Initiative (WHI). We tested associations using Cox proportional hazard models in sex- and study-stratified analyses and combined results using meta-analysis. Among 44 validated CHD loci available in the array, we replicated and fine-mapped the SORT1 locus, and showed same direction of effects as reported in studies of individuals of European ancestry for SNPs in 22 additional published loci. We also identified a {SNP} achieving array wide significance (MYC: rs2070583, allele frequency 0.02, P = 8.1 × 10(-8)), but the association did not replicate in an additional 8,059 African Americans (577 events) from the WHI, HealthABC and GeneSTAR studies, and in a meta-analysis of 5 cohort studies of European ancestry (24,024 individuals including 1,570 cases of MI and 2,406 cases of CHD) from the CHARGE Consortium. Our findings suggest that some CHD loci previously identified in individuals of European ancestry may be relevant to incident CHD in African Americans.},\n\tauthor = {Franceschini, Nora and Hu, Yijuan and Reiner, Alex P. and Buyske, Steven and Nalls, Mike and Yanek, Lisa R. and Li, Yun and Hindorff, Lucia A. and Cole, Shelley A. and Howard, Barbara V. and Stafford, Jeanette M. and Carty, Cara L. and Sethupathy, Praveen and Martin, Lisa W. and Lin, Dan-Yu and Johnson, Karen C. and Becker, Lewis C. and North, Kari E. and Dehghan, Abbas and Bis, Joshua C. and Liu, Yongmei and Greenland, Philip and Manson, JoAnn E. and Maeda, Nobuyo and Garcia, Melissa and Harris, Tamara B. and Becker, Diane M. and O'Donnell, Christopher and Heiss, Gerardo and Kooperberg, Charles and Boerwinkle, Eric},\n\tchemicals = {Adaptor Proteins, Vesicular Transport, MYC protein, human, Proto-Oncogene Proteins c-myc, sortilin},\n\tcitation-subset = {IM},\n\tcompleted = {2015-08-27},\n\tcountry = {United States},\n\tdoi = {10.1371/journal.pone.0113203},\n\tissn = {1932-6203},\n\tissn-linking = {1932-6203},\n\tissue = {12},\n\tjournal = {PloS one},\n\tkeywords = {Adaptor Proteins, Vesicular Transport, genetics; African Americans, genetics; Coronary Disease, epidemiology, ethnology, genetics; Female; Genetic Association Studies, methods; Genetic Predisposition to Disease; Humans; Male; Oligonucleotide Array Sequence Analysis, methods; Polymorphism, Single Nucleotide; Prospective Studies; Proto-Oncogene Proteins c-myc, genetics},\n\tnlm-id = {101285081},\n\towner = {NLM},\n\tpages = {e113203},\n\tpii = {PONE-D-14-15994},\n\tpmc = {PMC4277270},\n\tpmid = {25542012},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/25542012/},\n\n\tpubmodel = {Electronic-eCollection},\n\tpubstate = {epublish},\n\trevised = {2018-12-02},\n\ttitle = {Prospective associations of coronary heart disease loci in {African Americans} using the {Metabochip}: the {PAGE} study.},\n\tvolume = {9},\n\tyear = {2014},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/25542012/},\n\tbdsk-url-2 = {https://doi.org/10.1371/journal.pone.0113203}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Genetic association analysis under complex survey sampling: the Hispanic Community Health Study/Study of Latinos.\n \n \n \n \n\n\n \n Lin, D.; Tao, R.; Kalsbeek, W. D.; Zeng, D.; Gonzalez, F.; Fernández-Rhodes, L.; Graff, M.; Koch, G. G.; North, K. E.; and Heiss, G.\n\n\n \n\n\n\n American journal of human genetics, 95: 675–688. December 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Genetic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{LinTaoKalsbeekEtAl2014,\n\tabstract = {The cohort design allows investigators to explore the genetic basis of a variety of diseases and traits in a single study while avoiding major weaknesses of the case-control design. Most cohort studies employ multistage cluster sampling with unequal probabilities to conveniently select participants with desired characteristics, and participants from different clusters might be genetically related. Analysis that ignores the complex sampling design can yield biased estimation of the genetic association and inflation of the type I error. Herein, we develop weighted estimators that reflect unequal selection probabilities and differential nonresponse rates, and we derive variance estimators that properly account for the sampling design and the potential relatedness of participants in different sampling units. We compare, both analytically and numerically, the performance of the proposed weighted estimators with unweighted estimators that disregard the sampling design. We demonstrate the usefulness of the proposed methods through analysis of MetaboChip data in the Hispanic Community Health Study/Study of Latinos, which is the largest health study of the Hispanic/Latino population in the United States aimed at identifying risk factors for various diseases and determining the role of genes and environment in the occurrence of diseases. We provide guidelines on the use of weighted and unweighted estimators, as well as the relevant software.},\n\tauthor = {Lin, Dan-Yu and Tao, Ran and Kalsbeek, William D. and Zeng, Donglin and Gonzalez, Franklyn and Fern{\\'a}ndez-Rhodes, Lindsay and Graff, Mariaelisa and Koch, Gary G. and North, Kari E. and Heiss, Gerardo},\n\tcitation-subset = {IM},\n\tcompleted = {2015-02-27},\n\tcountry = {United States},\n\tdoi = {10.1016/j.ajhg.2014.11.005},\n\tissn = {1537-6605},\n\tissn-linking = {0002-9297},\n\tissue = {6},\n\tjournal = {American journal of human genetics},\n\tkeywords = {Adolescent; Adult; Aged; Cohort Studies; Computer Simulation; Female; Genetic Association Studies, methods; Genotype; Health Surveys, methods; Hispanic Americans, genetics; Humans; Male; Middle Aged; Models, Statistical; Phenotype; Research Design; Sampling Studies; United States; Young Adult},\n\tmonth = dec,\n\tnlm-id = {0370475},\n\towner = {NLM},\n\tpages = {675--688},\n\tpii = {S0002-9297(14)00471-6},\n\tpmc = {PMC4259979},\n\tpmid = {25480034},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/25480034/},\n\n\tpubmodel = {Print},\n\tpubstate = {ppublish},\n\trevised = {2020-08-24},\n\ttitle = {Genetic association analysis under complex survey sampling: the {Hispanic Community Health Study/Study of Latinos}.},\n\tvolume = {95},\n\tyear = {2014},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/25480034/},\n\tbdsk-url-2 = {https://doi.org/10.1016/j.ajhg.2014.11.005}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Detection of pleiotropy through a Phenome-wide association study (PheWAS) of epidemiologic data as part of the Environmental Architecture for Genes Linked to Environment (EAGLE) study.\n \n \n \n \n\n\n \n Hall, M. A.; Verma, A.; Brown-Gentry, K. D.; Goodloe, R.; Boston, J.; Wilson, S.; McClellan, B.; Sutcliffe, C.; Dilks, H. H.; Gillani, N. B.; Jin, H.; Mayo, P.; Allen, M.; Schnetz-Boutaud, N.; Crawford, D. C.; Ritchie, M. D.; and Pendergrass, S. A.\n\n\n \n\n\n\n PLoS genetics, 10: e1004678. December 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Detection$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{HallVermaBrownGentryEtAl2014,\n\tabstract = {We performed a Phenome-wide association study (PheWAS) utilizing diverse genotypic and phenotypic data existing across multiple populations in the National Health and Nutrition Examination Surveys (NHANES), conducted by the Centers for Disease Control and Prevention (CDC), and accessed by the Epidemiological Architecture for Genes Linked to Environment (EAGLE) study. We calculated comprehensive tests of association in Genetic NHANES using 80 SNPs and 1,008 phenotypes (grouped into 184 phenotype classes), stratified by race-ethnicity. Genetic NHANES includes three surveys (NHANES III, 1999-2000, and 2001-2002) and three race-ethnicities: non-Hispanic whites (n = 6,634), non-Hispanic blacks (n = 3,458), and Mexican Americans (n = 3,950). We identified 69 PheWAS associations replicating across surveys for the same {SNP}, phenotype-class, direction of effect, and race-ethnicity at p<0.01, allele frequency >0.01, and sample size >200. Of these 69 PheWAS associations, 39 replicated previously reported {SNP}-phenotype associations, 9 were related to previously reported associations, and 21 were novel associations. Fourteen results had the same direction of effect across more than one race-ethnicity: one result was novel, 11 replicated previously reported associations, and two were related to previously reported results. Thirteen SNPs showed evidence of pleiotropy. We further explored results with gene-based biological networks, contrasting the direction of effect for pleiotropic associations across phenotypes. One PheWAS result was ABCG2 missense {SNP} rs2231142, associated with uric acid levels in both non-Hispanic whites and Mexican Americans, protoporphyrin levels in non-Hispanic whites and Mexican Americans, and blood pressure levels in Mexican Americans. Another example was {SNP} rs1800588 near LIPC, significantly associated with the novel phenotypes of folate levels (Mexican Americans), vitamin E levels (non-Hispanic whites) and triglyceride levels (non-Hispanic whites), and replication for cholesterol levels. The results of this PheWAS show the utility of this approach for exposing more of the complex genetic architecture underlying multiple traits, through generating novel hypotheses for future research.},\n\tauthor = {Hall, Molly A. and Verma, Anurag and Brown-Gentry, Kristin D. and Goodloe, Robert and Boston, Jonathan and Wilson, Sarah and McClellan, Bob and Sutcliffe, Cara and Dilks, Holly H. and Gillani, Nila B. and Jin, Hailing and Mayo, Ping and Allen, Melissa and Schnetz-Boutaud, Nathalie and Crawford, Dana C. and Ritchie, Marylyn D. and Pendergrass, Sarah A.},\n\tcitation-subset = {IM},\n\tcompleted = {2016-01-29},\n\tcountry = {United States},\n\tdoi = {10.1371/journal.pgen.1004678},\n\tissn = {1553-7404},\n\tissn-linking = {1553-7390},\n\tissue = {12},\n\tjournal = {PLoS genetics},\n\tkeywords = {Adult; Environment; Epidemiologic Research Design; Ethnic Groups, genetics, statistics & numerical data; Female; Gene Frequency; Gene-Environment Interaction; Genome-Wide Association Study, statistics & numerical data; Humans; Male; Middle Aged; Nutrition Surveys; Phenotype; Polymorphism, Single Nucleotide; Quantitative Trait, Heritable; United States, epidemiology},\n\tmonth = dec,\n\tnlm-id = {101239074},\n\towner = {NLM},\n\tpages = {e1004678},\n\tpii = {PGENETICS-D-14-00864},\n\tpmc = {PMC4256091},\n\tpmid = {25474351},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/25474351/},\n\n\tpubmodel = {Electronic-eCollection},\n\tpubstate = {epublish},\n\trevised = {2019-02-02},\n\ttitle = {Detection of pleiotropy through a Phenome-wide association study (PheWAS) of epidemiologic data as part of the {Environmental Architecture for Genes Linked to Environment (EAGLE)} study.},\n\tvolume = {10},\n\tyear = {2014},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/25474351/},\n\tbdsk-url-2 = {https://doi.org/10.1371/journal.pgen.1004678}}\n\n

\n\n\n

\n We performed a Phenome-wide association study (PheWAS) utilizing diverse genotypic and phenotypic data existing across multiple populations in the National Health and Nutrition Examination Surveys (NHANES), conducted by the Centers for Disease Control and Prevention (CDC), and accessed by the Epidemiological Architecture for Genes Linked to Environment (EAGLE) study. We calculated comprehensive tests of association in Genetic NHANES using 80 SNPs and 1,008 phenotypes (grouped into 184 phenotype classes), stratified by race-ethnicity. Genetic NHANES includes three surveys (NHANES III, 1999-2000, and 2001-2002) and three race-ethnicities: non-Hispanic whites (n = 6,634), non-Hispanic blacks (n = 3,458), and Mexican Americans (n = 3,950). We identified 69 PheWAS associations replicating across surveys for the same SNP, phenotype-class, direction of effect, and race-ethnicity at p<0.01, allele frequency >0.01, and sample size >200. Of these 69 PheWAS associations, 39 replicated previously reported SNP-phenotype associations, 9 were related to previously reported associations, and 21 were novel associations. Fourteen results had the same direction of effect across more than one race-ethnicity: one result was novel, 11 replicated previously reported associations, and two were related to previously reported results. Thirteen SNPs showed evidence of pleiotropy. We further explored results with gene-based biological networks, contrasting the direction of effect for pleiotropic associations across phenotypes. One PheWAS result was ABCG2 missense SNP rs2231142, associated with uric acid levels in both non-Hispanic whites and Mexican Americans, protoporphyrin levels in non-Hispanic whites and Mexican Americans, and blood pressure levels in Mexican Americans. Another example was SNP rs1800588 near LIPC, significantly associated with the novel phenotypes of folate levels (Mexican Americans), vitamin E levels (non-Hispanic whites) and triglyceride levels (non-Hispanic whites), and replication for cholesterol levels. The results of this PheWAS show the utility of this approach for exposing more of the complex genetic architecture underlying multiple traits, through generating novel hypotheses for future research.\n

\n\n\n

\n \n\n \n \n \n \n \n \n Rare variant APOC3 R19X is associated with cardio-protective profiles in a diverse population-based survey as part of the Epidemiologic Architecture for Genes Linked to Environment Study.\n \n \n \n \n\n\n \n Crawford, D. C.; Dumitrescu, L.; Goodloe, R.; Brown-Gentry, K.; Boston, J.; McClellan, B.; Sutcliffe, C.; Wiseman, R.; Baker, P.; Pericak-Vance, M. A.; Scott, W. K.; Allen, M.; Mayo, P.; Schnetz-Boutaud, N.; Dilks, H. H.; Haines, J. L.; and Pollin, T. I.\n\n\n \n\n\n\n Circulation. Cardiovascular genetics, 7: 848–853. December 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Rare$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{CrawfordDumitrescuGoodloeEtAl2014,\n\tabstract = {A founder mutation was recently discovered and described as conferring favorable lipid profiles and reduced subclinical atherosclerotic disease in a Pennsylvania Amish population. Preliminary data have suggested that this null mutation APOC3 R19X (rs76353203) is rare in the general population. To better describe the frequency and lipid profile in the general population, we as part of the {Population Architecture using Genomics and Epidemiology} I Study and the Epidemiological Architecture for Genes Linked to Environment Study genotyped rs76353203 in 1113 Amish participants from Ohio and Indiana and 19 613 participants from the National Health and Nutrition Examination Surveys (NHANES III, 1999 to 2002, and 2007 to 2008). We found no carriers among the Ohio and Indiana Amish. Of the 19 613 NHANES participants, we identified 31 participants carrying the 19X allele, for an overall allele frequency of 0.08%. Among fasting adults, the 19X allele was associated with lower triglycerides (n=7603; β=-71.20; P=0.007) and higher high-density lipoprotein cholesterol (n=8891; β=15.65; P=0.0002) and, although not significant, lower low-density lipoprotein cholesterol (n=6502; β= -4.85; P=0.68) after adjustment for age, sex, and race/ethnicity. On average, 19X allele participants had approximately half the triglyceride levels (geometric means, 51.3 to 69.7 versus 134.6 to 141.3 mg/dL), >20% higher high-density lipoprotein cholesterol levels (geometric means, 56.8 to 74.4 versus 50.38 to 53.36 mg/dL), and lower low-density lipoprotein cholesterol levels (geometric means, 104.5 to 128.6 versus 116.1 to 125.7 mg/dL) compared with noncarrier participants. These data demonstrate that APOC3 19X exists in the general US population in multiple racial/ethnic groups and is associated with cardio-protective lipid profiles.},\n\tauthor = {Crawford, Dana C. and Dumitrescu, Logan and Goodloe, Robert and Brown-Gentry, Kristin and Boston, Jonathan and McClellan, Bob and Sutcliffe, Cara and Wiseman, Rachel and Baker, Paxton and Pericak-Vance, Margaret A. and Scott, William K. and Allen, Melissa and Mayo, Ping and Schnetz-Boutaud, Nathalie and Dilks, Holli H. and Haines, Jonathan L. and Pollin, Toni I.},\n\tchemicals = {Apolipoprotein C-III, Cholesterol, HDL, Cholesterol, LDL, Triglycerides},\n\tcitation-subset = {IM},\n\tcompleted = {2015-07-23},\n\tcountry = {United States},\n\tdoi = {10.1161/CIRCGENETICS.113.000369},\n\tissn = {1942-3268},\n\tissn-linking = {1942-3268},\n\tissue = {6},\n\tjournal = {Circulation. Cardiovascular genetics},\n\tkeywords = {Adult; Aged; Alleles; Amish, genetics; Apolipoprotein C-III, genetics; Atherosclerosis, genetics, pathology; Cholesterol, HDL, blood; Cholesterol, LDL, blood; Female; Gene Frequency; Genotype; Haplotypes; Humans; Male; Middle Aged; Nutrition Surveys; Polymorphism, Single Nucleotide; Triglycerides, blood; genetic association studies; genetics; high-density lipoprotein cholesterol; molecular epidemiology; triglycerides},\n\tmid = {NIHMS640711},\n\tmonth = dec,\n\tnlm-id = {101489144},\n\towner = {NLM},\n\tpages = {848--853},\n\tpii = {CIRCGENETICS.113.000369},\n\tpmc = {PMC4305446},\n\tpmid = {25363704},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/25363704/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Rare variant {APOC3 R19X} is associated with cardio-protective profiles in a diverse population-based survey as part of the {Epidemiologic Architecture for Genes Linked to Environment Study}.},\n\tvolume = {7},\n\tyear = {2014},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/25363704/},\n\tbdsk-url-2 = {https://doi.org/10.1161/CIRCGENETICS.113.000369}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Defining the role of common variation in the genomic and biological architecture of adult human height.\n \n \n \n \n\n\n \n Wood, A. R.; Esko, T.; Yang, J.; Vedantam, S.; Pers, T. H.; Gustafsson, S.; Chu, A. Y.; Estrada, K.; Luan, J.; Kutalik, Z.; Amin, N.; Buchkovich, M. L.; Croteau-Chonka, D. C.; Day, F. R.; Duan, Y.; Fall, T.; Fehrmann, R.; Ferreira, T.; Jackson, A. U.; Karjalainen, J.; Lo, K. S.; Locke, A. E.; Mägi, R.; Mihailov, E.; Porcu, E.; Randall, J. C.; Scherag, A.; Vinkhuyzen, A. A. E.; Westra, H.; Winkler, T. W.; Workalemahu, T.; Zhao, J. H.; Absher, D.; Albrecht, E.; Anderson, D.; Baron, J.; Beekman, M.; Demirkan, A.; Ehret, G. B.; Feenstra, B.; Feitosa, M. F.; Fischer, K.; Fraser, R. M.; Goel, A.; Gong, J.; Justice, A. E.; Kanoni, S.; Kleber, M. E.; Kristiansson, K.; Lim, U.; Lotay, V.; Lui, J. C.; Mangino, M.; Mateo Leach, I.; Medina-Gomez, C.; Nalls, M. A.; Nyholt, D. R.; Palmer, C. D.; Pasko, D.; Pechlivanis, S.; Prokopenko, I.; Ried, J. S.; Ripke, S.; Shungin, D.; Stancáková, A.; Strawbridge, R. J.; Sung, Y. J.; Tanaka, T.; Teumer, A.; Trompet, S.; van der Laan, S. W.; van Setten, J.; Van Vliet-Ostaptchouk, J. V.; Wang, Z.; Yengo, L.; Zhang, W.; Afzal, U.; Arnlöv, J.; Arscott, G. M.; Bandinelli, S.; Barrett, A.; Bellis, C.; Bennett, A. J.; Berne, C.; Blüher, M.; Bolton, J. L.; Böttcher, Y.; Boyd, H. A.; Bruinenberg, M.; Buckley, B. M.; Buyske, S.; Caspersen, I. H.; Chines, P. S.; Clarke, R.; Claudi-Boehm, S.; Cooper, M.; Daw, E. W.; De Jong, P. A.; Deelen, J.; Delgado, G.; Denny, J. C.; Dhonukshe-Rutten, R.; Dimitriou, M.; Doney, A. S. F.; Dörr, M.; Eklund, N.; Eury, E.; Folkersen, L.; Garcia, M. E.; Geller, F.; Giedraitis, V.; Go, A. S.; Grallert, H.; Grammer, T. B.; Gräßler, J.; Grönberg, H.; de Groot, L. C. P. G. M.; Groves, C. J.; Haessler, J.; Hall, P.; Haller, T.; Hallmans, G.; Hannemann, A.; Hartman, C. A.; Hassinen, M.; Hayward, C.; Heard-Costa, N. L.; Helmer, Q.; Hemani, G.; Henders, A. K.; Hillege, H. L.; Hlatky, M. A.; Hoffmann, W.; Hoffmann, P.; Holmen, O.; Houwing-Duistermaat, J. J.; Illig, T.; Isaacs, A.; James, A. L.; Jeff, J.; Johansen, B.; Johansson, Å.; Jolley, J.; Juliusdottir, T.; Junttila, J.; Kho, A. N.; Kinnunen, L.; Klopp, N.; Kocher, T.; Kratzer, W.; Lichtner, P.; Lind, L.; Lindström, J.; Lobbens, S.; Lorentzon, M.; Lu, Y.; Lyssenko, V.; Magnusson, P. K. E.; Mahajan, A.; Maillard, M.; McArdle, W. L.; McKenzie, C. A.; McLachlan, S.; McLaren, P. J.; Menni, C.; Merger, S.; Milani, L.; Moayyeri, A.; Monda, K. L.; Morken, M. A.; Müller, G.; Müller-Nurasyid, M.; Musk, A. W.; Narisu, N.; Nauck, M.; Nolte, I. M.; Nöthen, M. M.; Oozageer, L.; Pilz, S.; Rayner, N. W.; Renstrom, F.; Robertson, N. R.; Rose, L. M.; Roussel, R.; Sanna, S.; Scharnagl, H.; Scholtens, S.; Schumacher, F. R.; Schunkert, H.; Scott, R. A.; Sehmi, J.; Seufferlein, T.; Shi, J.; Silventoinen, K.; Smit, J. H.; Smith, A. V.; Smolonska, J.; Stanton, A. V.; Stirrups, K.; Stott, D. J.; Stringham, H. M.; Sundström, J.; Swertz, M. A.; Syvänen, A.; Tayo, B. O.; Thorleifsson, G.; Tyrer, J. P.; van Dijk, S.; van Schoor, N. M.; van der Velde, N.; van Heemst, D.; van Oort, F. V. A.; Vermeulen, S. H.; Verweij, N.; Vonk, J. M.; Waite, L. L.; Waldenberger, M.; Wennauer, R.; Wilkens, L. R.; Willenborg, C.; Wilsgaard, T.; Wojczynski, M. K.; Wong, A.; Wright, A. F.; Zhang, Q.; Arveiler, D.; Bakker, S. J. L.; Beilby, J.; Bergman, R. N.; Bergmann, S.; Biffar, R.; Blangero, J.; Boomsma, D. I.; Bornstein, S. R.; Bovet, P.; Brambilla, P.; Brown, M. J.; Campbell, H.; Caulfield, M. J.; Chakravarti, A.; Collins, R.; Collins, F. S.; Crawford, D. C.; Cupples, L. A.; Danesh, J.; de Faire, U.; den Ruijter, H. M.; Erbel, R.; Erdmann, J.; Eriksson, J. G.; Farrall, M.; Ferrannini, E.; Ferrières, J.; Ford, I.; Forouhi, N. G.; Forrester, T.; Gansevoort, R. T.; Gejman, P. V.; Gieger, C.; Golay, A.; Gottesman, O.; Gudnason, V.; Gyllensten, U.; Haas, D. W.; Hall, A. S.; Harris, T. B.; Hattersley, A. T.; Heath, A. C.; Hengstenberg, C.; Hicks, A. A.; Hindorff, L. A.; Hingorani, A. D.; Hofman, A.; Hovingh, G. K.; Humphries, S. E.; Hunt, S. C.; Hypponen, E.; Jacobs, K. B.; Jarvelin, M.; Jousilahti, P.; Jula, A. M.; Kaprio, J.; Kastelein, J. J. P.; Kayser, M.; Kee, F.; Keinanen-Kiukaanniemi, S. M.; Kiemeney, L. A.; Kooner, J. S.; Kooperberg, C.; Koskinen, S.; Kovacs, P.; Kraja, A. T.; Kumari, M.; Kuusisto, J.; Lakka, T. A.; Langenberg, C.; Le Marchand, L.; Lehtimäki, T.; Lupoli, S.; Madden, P. A. F.; Männistö, S.; Manunta, P.; Marette, A.; Matise, T. C.; McKnight, B.; Meitinger, T.; Moll, F. L.; Montgomery, G. W.; Morris, A. D.; Morris, A. P.; Murray, J. C.; Nelis, M.; Ohlsson, C.; Oldehinkel, A. J.; Ong, K. K.; Ouwehand, W. H.; Pasterkamp, G.; Peters, A.; Pramstaller, P. P.; Price, J. F.; Qi, L.; Raitakari, O. T.; Rankinen, T.; Rao, D. C.; Rice, T. K.; Ritchie, M.; Rudan, I.; Salomaa, V.; Samani, N. J.; Saramies, J.; Sarzynski, M. A.; Schwarz, P. E. H.; Sebert, S.; Sever, P.; Shuldiner, A. R.; Sinisalo, J.; Steinthorsdottir, V.; Stolk, R. P.; Tardif, J.; Tönjes, A.; Tremblay, A.; Tremoli, E.; Virtamo, J.; Vohl, M.; The Electronic Medical Records; Consortium, G. (.; The MIGen Consortium; The PAGE Consortium; The LifeLines Cohort Study; Amouyel, P.; Asselbergs, F. W.; Assimes, T. L.; Bochud, M.; Boehm, B. O.; Boerwinkle, E.; Bottinger, E. P.; Bouchard, C.; Cauchi, S.; Chambers, J. C.; Chanock, S. J.; Cooper, R. S.; de Bakker, P. I. W.; Dedoussis, G.; Ferrucci, L.; Franks, P. W.; Froguel, P.; Groop, L. C.; Haiman, C. A.; Hamsten, A.; Hayes, M. G.; Hui, J.; Hunter, D. J.; Hveem, K.; Jukema, J. W.; Kaplan, R. C.; Kivimaki, M.; Kuh, D.; Laakso, M.; Liu, Y.; Martin, N. G.; März, W.; Melbye, M.; Moebus, S.; Munroe, P. B.; Njølstad, I.; Oostra, B. A.; Palmer, C. N. A.; Pedersen, N. L.; Perola, M.; Pérusse, L.; Peters, U.; Powell, J. E.; Power, C.; Quertermous, T.; Rauramaa, R.; Reinmaa, E.; Ridker, P. M.; Rivadeneira, F.; Rotter, J. I.; Saaristo, T. E.; Saleheen, D.; Schlessinger, D.; Slagboom, P. E.; Snieder, H.; Spector, T. D.; Strauch, K.; Stumvoll, M.; Tuomilehto, J.; Uusitupa, M.; van der Harst, P.; Völzke, H.; Walker, M.; Wareham, N. J.; Watkins, H.; Wichmann, H.; Wilson, J. F.; Zanen, P.; Deloukas, P.; Heid, I. M.; Lindgren, C. M.; Mohlke, K. L.; Speliotes, E. K.; Thorsteinsdottir, U.; Barroso, I.; Fox, C. S.; North, K. E.; Strachan, D. P.; Beckmann, J. S.; Berndt, S. I.; Boehnke, M.; Borecki, I. B.; McCarthy, M. I.; Metspalu, A.; Stefansson, K.; Uitterlinden, A. G.; van Duijn, C. M.; Franke, L.; Willer, C. J.; Price, A. L.; Lettre, G.; Loos, R. J. F.; Weedon, M. N.; Ingelsson, E.; O'Connell, J. R.; Abecasis, G. R.; Chasman, D. I.; Goddard, M. E.; Visscher, P. M.; Hirschhorn, J. N.; and Frayling, T. M.\n\n\n \n\n\n\n Nature genetics, 46: 1173–1186. November 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Defining$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{WoodEskoYangEtAl2014,\n\tabstract = {Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated ∼2,000, ∼3,700 and ∼9,500 SNPs explained ∼21%, ∼24% and ∼29% of phenotypic variance. Furthermore, all common variants together captured 60% of heritability. The 697 variants clustered in 423 loci were enriched for genes, pathways and tissue types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/β-catenin and chondroitin sulfate-related genes. We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid. Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants.},\n\tauthor = {Wood, Andrew R. and Esko, Tonu and Yang, Jian and Vedantam, Sailaja and Pers, Tune H. and Gustafsson, Stefan and Chu, Audrey Y. and Estrada, Karol and Luan, Jian'an and Kutalik, Zolt{\\'a}n and Amin, Najaf and Buchkovich, Martin L. and Croteau-Chonka, Damien C. and Day, Felix R. and Duan, Yanan and Fall, Tove and Fehrmann, Rudolf and Ferreira, Teresa and Jackson, Anne U. and Karjalainen, Juha and Lo, Ken Sin and Locke, Adam E. and M{\\"a}gi, Reedik and Mihailov, Evelin and Porcu, Eleonora and Randall, Joshua C. and Scherag, Andr{\\'e} and Vinkhuyzen, Anna A. E. and Westra, Harm-Jan and Winkler, Thomas W. and Workalemahu, Tsegaselassie and Zhao, Jing Hua and Absher, Devin and Albrecht, Eva and Anderson, Denise and Baron, Jeffrey and Beekman, Marian and Demirkan, Ayse and Ehret, Georg B. and Feenstra, Bjarke and Feitosa, Mary F. and Fischer, Krista and Fraser, Ross M. and Goel, Anuj and Gong, Jian and Justice, Anne E. and Kanoni, Stavroula and Kleber, Marcus E. and Kristiansson, Kati and Lim, Unhee and Lotay, Vaneet and Lui, Julian C. and Mangino, Massimo and Mateo Leach, Irene and Medina-Gomez, Carolina and Nalls, Michael A. and Nyholt, Dale R. and Palmer, Cameron D. and Pasko, Dorota and Pechlivanis, Sonali and Prokopenko, Inga and Ried, Janina S. and Ripke, Stephan and Shungin, Dmitry and Stanc{\\'a}kov{\\'a}, Alena and Strawbridge, Rona J. and Sung, Yun Ju and Tanaka, Toshiko and Teumer, Alexander and Trompet, Stella and van der Laan, Sander W. and van Setten, Jessica and Van Vliet-Ostaptchouk, Jana V. and Wang, Zhaoming and Yengo, Lo{\\"\\i}c and Zhang, Weihua and Afzal, Uzma and Arnl{\\"o}v, Johan and Arscott, Gillian M. and Bandinelli, Stefania and Barrett, Amy and Bellis, Claire and Bennett, Amanda J. and Berne, Christian and Bl{\\"u}her, Matthias and Bolton, Jennifer L. and B{\\"o}ttcher, Yvonne and Boyd, Heather A. and Bruinenberg, Marcel and Buckley, Brendan M. and Buyske, Steven and Caspersen, Ida H. and Chines, Peter S. and Clarke, Robert and Claudi-Boehm, Simone and Cooper, Matthew and Daw, E. Warwick and De Jong, Pim A. and Deelen, Joris and Delgado, Graciela and Denny, Josh C. and Dhonukshe-Rutten, Rosalie and Dimitriou, Maria and Doney, Alex S. F. and D{\\"o}rr, Marcus and Eklund, Niina and Eury, Elodie and Folkersen, Lasse and Garcia, Melissa E. and Geller, Frank and Giedraitis, Vilmantas and Go, Alan S. and Grallert, Harald and Grammer, Tanja B. and Gr{\\"a}{\\ss}ler, J{\\"u}rgen and Gr{\\"o}nberg, Henrik and de Groot, Lisette C. P. G. M. and Groves, Christopher J. and Haessler, Jeffrey and Hall, Per and Haller, Toomas and Hallmans, Goran and Hannemann, Anke and Hartman, Catharina A. and Hassinen, Maija and Hayward, Caroline and Heard-Costa, Nancy L. and Helmer, Quinta and Hemani, Gibran and Henders, Anjali K. and Hillege, Hans L. and Hlatky, Mark A. and Hoffmann, Wolfgang and Hoffmann, Per and Holmen, Oddgeir and Houwing-Duistermaat, Jeanine J. and Illig, Thomas and Isaacs, Aaron and James, Alan L. and Jeff, Janina and Johansen, Berit and Johansson, {\\AA}sa and Jolley, Jennifer and Juliusdottir, Thorhildur and Junttila, Juhani and Kho, Abel N. and Kinnunen, Leena and Klopp, Norman and Kocher, Thomas and Kratzer, Wolfgang and Lichtner, Peter and Lind, Lars and Lindstr{\\"o}m, Jaana and Lobbens, St{\\'e}phane and Lorentzon, Mattias and Lu, Yingchang and Lyssenko, Valeriya and Magnusson, Patrik K. E. and Mahajan, Anubha and Maillard, Marc and McArdle, Wendy L. and McKenzie, Colin A. and McLachlan, Stela and McLaren, Paul J. and Menni, Cristina and Merger, Sigrun and Milani, Lili and Moayyeri, Alireza and Monda, Keri L. and Morken, Mario A. and M{\\"u}ller, Gabriele and M{\\"u}ller-Nurasyid, Martina and Musk, Arthur W. and Narisu, Narisu and Nauck, Matthias and Nolte, Ilja M. and N{\\"o}then, Markus M. and Oozageer, Laticia and Pilz, Stefan and Rayner, Nigel W. and Renstrom, Frida and Robertson, Neil R. and Rose, Lynda M. and Roussel, Ronan and Sanna, Serena and Scharnagl, Hubert and Scholtens, Salome and Schumacher, Fredrick R. and Schunkert, Heribert and Scott, Robert A. and Sehmi, Joban and Seufferlein, Thomas and Shi, Jianxin and Silventoinen, Karri and Smit, Johannes H. and Smith, Albert Vernon and Smolonska, Joanna and Stanton, Alice V. and Stirrups, Kathleen and Stott, David J. and Stringham, Heather M. and Sundstr{\\"o}m, Johan and Swertz, Morris A. and Syv{\\"a}nen, Ann-Christine and Tayo, Bamidele O. and Thorleifsson, Gudmar and Tyrer, Jonathan P. and van Dijk, Suzanne and van Schoor, Natasja M. and van der Velde, Nathalie and van Heemst, Diana and van Oort, Floor V. A. and Vermeulen, Sita H. and Verweij, Niek and Vonk, Judith M. and Waite, Lindsay L. and Waldenberger, Melanie and Wennauer, Roman and Wilkens, Lynne R. and Willenborg, Christina and Wilsgaard, Tom and Wojczynski, Mary K. and Wong, Andrew and Wright, Alan F. and Zhang, Qunyuan and Arveiler, Dominique and Bakker, Stephan J. L. and Beilby, John and Bergman, Richard N. and Bergmann, Sven and Biffar, Reiner and Blangero, John and Boomsma, Dorret I. and Bornstein, Stefan R. and Bovet, Pascal and Brambilla, Paolo and Brown, Morris J. and Campbell, Harry and Caulfield, Mark J. and Chakravarti, Aravinda and Collins, Rory and Collins, Francis S. and Crawford, Dana C. and Cupples, L. Adrienne and Danesh, John and de Faire, Ulf and den Ruijter, Hester M. and Erbel, Raimund and Erdmann, Jeanette and Eriksson, Johan G. and Farrall, Martin and Ferrannini, Ele and Ferri{\\`e}res, Jean and Ford, Ian and Forouhi, Nita G. and Forrester, Terrence and Gansevoort, Ron T. and Gejman, Pablo V. and Gieger, Christian and Golay, Alain and Gottesman, Omri and Gudnason, Vilmundur and Gyllensten, Ulf and Haas, David W. and Hall, Alistair S. and Harris, Tamara B. and Hattersley, Andrew T. and Heath, Andrew C. and Hengstenberg, Christian and Hicks, Andrew A. and Hindorff, Lucia A. and Hingorani, Aroon D. and Hofman, Albert and Hovingh, G. Kees and Humphries, Steve E. and Hunt, Steven C. and Hypponen, Elina and Jacobs, Kevin B. and Jarvelin, Marjo-Riitta and Jousilahti, Pekka and Jula, Antti M. and Kaprio, Jaakko and Kastelein, John J. P. and Kayser, Manfred and Kee, Frank and Keinanen-Kiukaanniemi, Sirkka M. and Kiemeney, Lambertus A. and Kooner, Jaspal S. and Kooperberg, Charles and Koskinen, Seppo and Kovacs, Peter and Kraja, Aldi T. and Kumari, Meena and Kuusisto, Johanna and Lakka, Timo A. and Langenberg, Claudia and Le Marchand, Loic and Lehtim{\\"a}ki, Terho and Lupoli, Sara and Madden, Pamela A. F. and M{\\"a}nnist{\\"o}, Satu and Manunta, Paolo and Marette, Andr{\\'e} and Matise, Tara C. and McKnight, Barbara and Meitinger, Thomas and Moll, Frans L. and Montgomery, Grant W. and Morris, Andrew D. and Morris, Andrew P. and Murray, Jeffrey C. and Nelis, Mari and Ohlsson, Claes and Oldehinkel, Albertine J. and Ong, Ken K. and Ouwehand, Willem H. and Pasterkamp, Gerard and Peters, Annette and Pramstaller, Peter P. and Price, Jackie F. and Qi, Lu and Raitakari, Olli T. and Rankinen, Tuomo and Rao, D. C. and Rice, Treva K. and Ritchie, Marylyn and Rudan, Igor and Salomaa, Veikko and Samani, Nilesh J. and Saramies, Jouko and Sarzynski, Mark A. and Schwarz, Peter E. H. and Sebert, Sylvain and Sever, Peter and Shuldiner, Alan R. and Sinisalo, Juha and Steinthorsdottir, Valgerdur and Stolk, Ronald P. and Tardif, Jean-Claude and T{\\"o}njes, Anke and Tremblay, Angelo and Tremoli, Elena and Virtamo, Jarmo and Vohl, Marie-Claude and {{The Electronic Medical Records and Genomics} (eMERGE) Consortium} and {The MIGen Consortium} and {The PAGE Consortium} and {The LifeLines Cohort Study} and Amouyel, Philippe and Asselbergs, Folkert W. and Assimes, Themistocles L. and Bochud, Murielle and Boehm, Bernhard O. and Boerwinkle, Eric and Bottinger, Erwin P. and Bouchard, Claude and Cauchi, St{\\'e}phane and Chambers, John C. and Chanock, Stephen J. and Cooper, Richard S. and de Bakker, Paul I. W. and Dedoussis, George and Ferrucci, Luigi and Franks, Paul W. and Froguel, Philippe and Groop, Leif C. and Haiman, Christopher A. and Hamsten, Anders and Hayes, M. Geoffrey and Hui, Jennie and Hunter, David J. and Hveem, Kristian and Jukema, J. Wouter and Kaplan, Robert C. and Kivimaki, Mika and Kuh, Diana and Laakso, Markku and Liu, Yongmei and Martin, Nicholas G. and M{\\"a}rz, Winfried and Melbye, Mads and Moebus, Susanne and Munroe, Patricia B. and Nj{\\o}lstad, Inger and Oostra, Ben A. and Palmer, Colin N. A. and Pedersen, Nancy L. and Perola, Markus and P{\\'e}russe, Louis and Peters, Ulrike and Powell, Joseph E. and Power, Chris and Quertermous, Thomas and Rauramaa, Rainer and Reinmaa, Eva and Ridker, Paul M. and Rivadeneira, Fernando and Rotter, Jerome I. and Saaristo, Timo E. and Saleheen, Danish and Schlessinger, David and Slagboom, P. Eline and Snieder, Harold and Spector, Tim D. and Strauch, Konstantin and Stumvoll, Michael and Tuomilehto, Jaakko and Uusitupa, Matti and van der Harst, Pim and V{\\"o}lzke, Henry and Walker, Mark and Wareham, Nicholas J. and Watkins, Hugh and Wichmann, H.-Erich and Wilson, James F. and Zanen, Pieter and Deloukas, Panos and Heid, Iris M. and Lindgren, Cecilia M. and Mohlke, Karen L. and Speliotes, Elizabeth K. and Thorsteinsdottir, Unnur and Barroso, In{\\^e}s and Fox, Caroline S. and North, Kari E. and Strachan, David P. and Beckmann, Jacques S. and Berndt, Sonja I. and Boehnke, Michael and Borecki, Ingrid B. and McCarthy, Mark I. and Metspalu, Andres and Stefansson, Kari and Uitterlinden, Andr{\\'e} G. and van Duijn, Cornelia M. and Franke, Lude and Willer, Cristen J. and Price, Alkes L. and Lettre, Guillaume and Loos, Ruth J. F. and Weedon, Michael N. and Ingelsson, Erik and O'Connell, Jeffrey R. and Abecasis, Goncalo R. and Chasman, Daniel I. and Goddard, Michael E. and Visscher, Peter M. and Hirschhorn, Joel N. and Frayling, Timothy M.},\n\tcitation-subset = {IM},\n\tcompleted = {2015-01-20},\n\tcountry = {United States},\n\tdoi = {10.1038/ng.3097},\n\tinvestigator = {McCarty, Catherine A and Starren, Justin and Peissig, Peggy and Berg, Richard and Rasmussen, Luke and Linneman, James and Miller, Aaron and Choudary, Vidhu and Chen, Lin and Waudby, Carol and Kitchner, Terrie and Reeser, Jonathan and Fost, Norman and Ritchie, Marylyn and Wilke, Russell A and Chisholm, Rex L and Avila, Pedro C and Greenland, Philip and Hayes, M Geoff and Kho, Abel and Kibbe, Warren A and Lemke, Amy A and Lowe, William L and Smith, Maureen E and Wolf, Wendy A and Pacheco, Jennifer A and Thompson, William K and Humowiecki, Joel and Law, May and Chute, Christopher and Kullo, Iftikar and Koenig, Barbara and de Andrade, Mariza and Bielinski, Suzette and Pathak, Jyotishman and Savova, Guergana and Wu, Joel and Henriksen, Joan and Ding, Keyue and Hart, Lacey and Palbicki, Jeremy and Larson, Eric B and Newton, Katherine and Ludman, Evette and Spangler, Leslie and Hart, Gene and Carrell, David and Jarvik, Gail and Crane, Paul and Burke, Wylie and Fullerton, Stephanie Malia and Trinidad, Susan Brown and Carlson, Chris and Hutchinson, Fred and McDavid, Andrew and Roden, Dan M and Clayton, Ellen and Haines, Jonathan L and Masys, Daniel R and Churchill, Larry R and Cornfield, Daniel and Crawford, Dana and Darbar, Dawood and Denny, Joshua C and Malin, Bradley A and Ritchie, Marylyn D and Schildcrout, Jonathan S and Xu, Hua and Ramirez, Andrea Havens and Basford, Melissa and Pulley, Jill and Alizadeh, Behrooz Z and de Boer, Rudolf A and Boezen, H Marike and Bruinenberg, Marcel and Franke, Lude and van der Harst, Pim and Hillege, Hans L and van der Klauw, Melanie M and Navis, Gerjan and Ormel, Johan and Postma, Dirkje S and Rosmalen, Judith G M and Slaets, Joris P and Snieder, Harold and Stolk, Ronald P and Wolffenbuttel, Bruce H R and Wijmenga, Cisca and Kathiresan, Sekar and Voight, Benjamin F and Purcell, Shaun and Musunuru, Kiran and Ardissino, Diego and Mannucci, Pier M and Anand, Sonia and Engert, James C and Samani, Nilesh J and Schunkert, Heribert and Erdmann, Jeanette and Reilly, Muredach P and Rader, Daniel J and Morgan, Thomas and Spertus, John A and Stoll, Monika and Girelli, Domenico and McKeown, Pascal P and Patterson, Chris C and Siscovick, David S and O'Donnell, Christopher J and Elosua, Roberto and Peltonen, Leena and Salomaa, Veikko and Schwartz, Stephen M and Melander, Olle and Altshuler, David and Ardissino, Diego and Merlini, Pier Angelica and Berzuini, Carlo and Bernardinelli, Luisa and Peyvandi, Flora and Tubaro, Marco and Celli, Patrizia and Ferrario, Maurizio and Fetiveau, Raffaela and Marziliano, Nicola and Casari, Giorgio and Galli, Michele and Ribichini, Flavio and Rossi, Marco and Bernardi, Francesco and Zonzin, Pietro and Piazza, Alberto and Mannucci, Pier M and Schwartz, Stephen M and Siscovick, David S and Yee, Jean and Friedlander, Yechiel and Elosua, Roberto and Marrugat, Jaume and Lucas, Gavin and Subirana, Isaac and Sala, Joan and Ramos, Rafael and Kathiresan, Sekar and Meigs, James B and Williams, Gordon and Nathan, David M and MacRae, Calum A and O'Donnell, Christopher J and Salomaa, Veikko and Havulinna, Aki S and Peltonen, Leena and Melander, Olle and Berglund, Goran and Voight, Benjamin F and Kathiresan, Sekar and Hirschhorn, Joel N and Asselta, Rosanna and Duga, Stefano and Spreafico, Marta and Musunuru, Kiran and Daly, Mark J and Purcell, Shaun and Voight, Benjamin F and Purcell, Shaun and Nemesh, James and Korn, Joshua M and McCarroll, Steven A and Schwartz, Stephen M and Yee, Jean and Kathiresan, Sekar and Lucas, Gavin and Subirana, Isaac and Elosua, Roberto and Surti, Aarti and Guiducci, Candace and Gianniny, Lauren and Mirel, Daniel and Parkin, Melissa and Burtt, Noel and Gabriel, Stacey B and Samani, Nilesh J and Thompson, John R and Braund, Peter S and Wright, Benjamin J and Balmforth, Anthony J and Ball, Stephen G and Hall, Alistair S and Schunkert, I Heribert and Erdmann, Jeanette and Linsel-Nitschke, Patrick and Lieb, Wolfgang and Ziegler, Andreas and K{\\"o}nig, Inke R and Hengstenberg, Christian and Fischer, Marcus and Stark, Klaus and Grosshennig, Anika and Preuss, Michael and Wichmann, H-Erich and Schreiber, Stefan and Schunkert, Heribert and Samani, Nilesh J and Erdmann, Jeanette and Ouwehand, Willem and Hengstenberg, Christian and Deloukas, Panos and Scholz, Michael and Cambien, Francois and Goodall, Alison and Reilly, Muredach P and Li, Mingyao and Chen, Zhen and Wilensky, Robert and Matthai, William and Qasim, Atif and Hakonarson, Hakon H and Devaney, Joe and Burnett, Mary-Susan and Pichard, Augusto D and Kent, Kenneth M and Satler, Lowell and Lindsay, Joseph M and Waksman, Ron and Knouff, Christopher W and Waterworth, Dawn M and Walker, Max C and Mooser, Vincent and Epstein, Stephen E and Rader, Daniel J and Scheffold, Thomas and Berger, Klaus and Stoll, Monika and Huge, Andreas and Girelli, Domenico and Martinelli, Nicola and Olivieri, Oliviero and Corrocher, Roberto and Morgan, Thomas and Spertus, John A and McKeown, Pascal P and Patterson, Chris C and Schunkert, Heribert and Erdmann, Jeanette and Linsel-Nitschke, Patrick and Lieb, Wolfgang and Ziegler, Andreas and K{\\"o}nig, Inke R and Hengstenberg, Christian and Fischer, Marcus and Stark, Klaus and Grosshennig, Anika and Preuss, Michael and Wichmann, H-Erich and Schreiber, Stefan and H{\\'o}lm, Hilma and Thorleifsson, Gudmar and Thorsteinsdottir, Unnur and Stefansson, Kari and Engert, James C and Do, Ron and Xie, Changchun and Anand, Sonia and Kathiresan, Sekar and Ardissino, Diego and Mannucci, Pier M and Siscovick, David and O'Donnell, Christopher J and Samani, Nilesh J and Melander, Olle and Elosua, Roberto and Peltonen, Leena and Salomaa, Veikko and Schwartz, Stephen M and Altshuler, David and Matise, Tara and Buyske, Steve and Higashio, Julia and Williams, Rasheeda and Nato, Andrew and Ambite, Jose Luis and Deelman, Ewa and Manolio, Teri and Hindorff, Lucia and North, Kari E and Heiss, Gerardo and Taylor, Kira and Franceschini, Nora and Avery, Christy and Graff, Misa and Lin, Danyu and Quibrera, Miguel and Cochran, Barbara and Kao, Linda and Umans, Jason and Cole, Shelley and MacCluer, Jean and Person, Sharina and Pankow, James and Gross, Myron and Boerwinkle, Eric and Fornage, Myriam and Durda, Peter and Jenny, Nancy and Patsy, Bruce and Arnold, Alice and Buzkova, Petra and Crawford, Dana and Haines, Jonathan and Murdock, Deborah and Glenn, Kim and Brown-Gentry, Kristin and Thornton-Wells, Tricia and Dumitrescu, Logan and Jeff, Janina and Bush, William S and Mitchell, Sabrina L and Goodloe, Robert and Wilson, Sarah and Boston, Jonathan and Malinowski, Jennifer and Restrepo, Nicole and Oetjens, Matthew and Fowke, Jay and Zheng, Wei and Spencer, Kylee and Ritchie, Marylyn and Pendergrass, Sarah and Le Marchand, Lo{\\"\\i}c and Wilkens, Lynne and Park, Lani and Tiirikainen, Maarit and Kolonel, Laurence and Lim, Unhee and Cheng, Iona and Wang, Hansong and Shohet, Ralph and Haiman, Christopher and Stram, Daniel and Henderson, Brian and Monroe, Kristine and Schumacher, Fredrick and Kooperberg, Charles and Peters, Ulrike and Anderson, Garnet and Carlson, Chris and Prentice, Ross and LaCroix, Andrea and Wu, Chunyuan and Carty, Cara and Gong, Jian and Rosse, Stephanie and Young, Alicia and Haessler, Jeff and Kocarnik, Jonathan and Lin, Yi and Jackson, Rebecca and Duggan, David and Kuller, Lew},\n\tissn = {1546-1718},\n\tissn-linking = {1061-4036},\n\tissue = {11},\n\tjournal = {Nature genetics},\n\tkeywords = {Adult; Analysis of Variance; Body Height, genetics; European Continental Ancestry Group, genetics; Genetic Variation, genetics; Genetics, Population; Genome-Wide Association Study, methods; Humans; Oligonucleotide Array Sequence Analysis; Polymorphism, Single Nucleotide, genetics},\n\tmid = {EMS60217},\n\tmonth = nov,\n\tnlm = {EMS60217},\n\tnlm-id = {9216904},\n\towner = {NLM},\n\tpages = {1173--1186},\n\tpii = {ng.3097},\n\tpmc = {PMC4250049},\n\tpmid = {25282103},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/25282103/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2019-11-01},\n\ttitle = {Defining the role of common variation in the genomic and biological architecture of adult human height.},\n\tvolume = {46},\n\tyear = {2014},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/25282103/},\n\tbdsk-url-2 = {https://doi.org/10.1038/ng.3097}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Genetic determinants of age-related macular degeneration in diverse populations from the PAGE study.\n \n \n \n \n\n\n \n Restrepo, N. A.; Spencer, K. L.; Goodloe, R.; Garrett, T. A.; Heiss, G.; B ̊u ̌zková, P.; Jorgensen, N.; Jensen, R. A.; Matise, T. C.; Hindorff, L. A.; Klein, B. E. K.; Klein, R.; Wong, T. Y.; Cheng, C.; Cornes, B. K.; Tai, E.; Ritchie, M. D.; Haines, J. L.; and Crawford, D. C.\n\n\n \n\n\n\n Investigative ophthalmology & visual science, 55: 6839–6850. September 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Genetic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{RestrepoSpencerGoodloeEtAl2014,\n\tabstract = {Substantial progress has been made in identifying susceptibility variants for AMD in European populations; however, few studies have been conducted to understand the role these variants play in AMD risk in diverse populations. The present study aims to examine AMD risk across diverse populations in known and suspected AMD complement factor and lipid-related loci. Targeted genotyping was performed across study sites for AMD and lipid trait-associated single nucleotide polymorphism (SNPs). Genetic association tests were performed at individual sites and then meta-analyzed using logistic regression assuming an additive genetic model stratified by self-described race/ethnicity. Participants included cases with early or late AMD and controls with no signs of AMD as determined by fundus photography. Populations included in this study were European Americans, African Americans, Mexican Americans, and Singaporeans from the {Population Architecture using Genomics and Epidemiology} (PAGE) study. Index variants of AMD, rs1061170 (CFH) and rs10490924 (ARMS2), were associated with AMD at P=3.05×10(-8) and P=6.36×10(-6), respectively, in European Americans. In general, none of the major AMD index variants generalized to our non-European populations with the exception of rs10490924 in Mexican Americans at an uncorrected P value<0.05. Four lipid-associated SNPS (LPL rs328, TRIB1 rs6987702, CETP rs1800775, and KCTD10/MVK rs2338104) were associated with AMD in African Americans and Mexican Americans (P<0.05), but these associations did not survive strict corrections for multiple testing. While most associations did not generalize in the non-European populations, variants within lipid-related genes were found to be associated with AMD. This study highlights the need for larger well-powered studies in non-European populations.},\n\tauthor = {Restrepo, Nicole A. and Spencer, Kylee L. and Goodloe, Robert and Garrett, Tiana A. and Heiss, Gerardo and B{\\r u}{\\v z}kov{\\'a}, Petra and Jorgensen, Neal and Jensen, Richard A. and Matise, Tara C. and Hindorff, Lucia A. and Klein, Barbara E. K. and Klein, Ronald and Wong, Tien Y. and Cheng, Ching-Yu and Cornes, Belinda K. and Tai, E.-Shyong and Ritchie, Marylyn D. and Haines, Jonathan L. and Crawford, Dana C.},\n\tchemicals = {ARMS2 protein, human, Proteins, complement factor H, human, Complement Factor H, DNA},\n\tcitation-subset = {IM},\n\tcompleted = {2014-12-22},\n\tcountry = {United States},\n\tdoi = {10.1167/iovs.14-14246},\n\tissn = {1552-5783},\n\tissn-linking = {0146-0404},\n\tissue = {10},\n\tjournal = {Investigative ophthalmology \\& visual science},\n\tkeywords = {Adult; Aged; Aged, 80 and over; Complement Factor H, genetics, metabolism; DNA, genetics; Ethnic Groups, genetics; Female; Gene Frequency; Genetic Predisposition to Disease; Genotype; Humans; Macular Degeneration, ethnology, genetics, metabolism; Male; Middle Aged; Phenotype; Polymorphism, Single Nucleotide; Prevalence; Prospective Studies; Proteins, genetics, metabolism; Risk Factors; United States, epidemiology; ARMS2 A69S; CFH Y402H; PAGE Study; age-related macular degeneration; genetic epidemiology},\n\tmonth = sep,\n\tnlm-id = {7703701},\n\towner = {NLM},\n\tpages = {6839--6850},\n\tpii = {iovs.14-14246},\n\tpmc = {PMC4214207},\n\tpmid = {25205864},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/25205864/},\n\n\tpubmodel = {Electronic},\n\tpubstate = {epublish},\n\trevised = {2018-11-13},\n\ttitle = {Genetic determinants of age-related macular degeneration in diverse populations from the {PAGE} study.},\n\tvolume = {55},\n\tyear = {2014},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/25205864/},\n\tbdsk-url-2 = {https://doi.org/10.1167/iovs.14-14246}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Evidence of heterogeneity by race/ethnicity in genetic determinants of QT interval.\n \n \n \n \n\n\n \n Seyerle, A. A.; Young, A. M.; Jeff, J. M.; Melton, P. E.; Jorgensen, N. W.; Lin, Y.; Carty, C. L.; Deelman, E.; Heckbert, S. R.; Hindorff, L. A.; Jackson, R. D.; Martin, L. W.; Okin, P. M.; Perez, M. V.; Psaty, B. M.; Soliman, E. Z.; Whitsel, E. A.; North, K. E.; Laston, S.; Kooperberg, C.; and Avery, C. L.\n\n\n \n\n\n\n Epidemiology (Cambridge, Mass.), 25: 790–798. November 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Evidence$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{SeyerleYoungJeffEtAl2014,\n\tabstract = {{QT} interval ({QT}) prolongation is an established risk factor for ventricular tachyarrhythmia and sudden cardiac death. Previous genome-wide association studies in populations of the European descent have identified multiple genetic loci that influence {QT}, but few have examined these loci in ethnically diverse populations. Here, we examine the direction, magnitude, and precision of effect sizes for 21 previously reported SNPs from 12 {QT} loci, in populations of European (n = 16,398), African (n = 5,437), American Indian (n = 5,032), Hispanic (n = 1,143), and Asian (n = 932) descent as part of the {Population Architecture using Genomics and Epidemiology} (PAGE) study. Estimates obtained from linear regression models stratified by race/ethnicity were combined using inverse-variance weighted meta-analysis. Heterogeneity was evaluated using Cochran's Q test. Of 21 SNPs, 7 showed consistent direction of effect across all 5 populations, and an additional 9 had estimated effects that were consistent across 4 populations. Despite consistent direction of effect, 9 of 16 SNPs had evidence (P < 0.05) of heterogeneity by race/ethnicity. For these 9 SNPs, linkage disequilibrium plots often indicated substantial variation in linkage disequilibrium patterns among the various racial/ethnic groups, as well as possible allelic heterogeneity. These results emphasize the importance of analyzing racial/ethnic groups separately in genetic studies. Furthermore, they underscore the possible utility of trans-ethnic studies to pinpoint underlying casual variants influencing heritable traits such as {QT}.},\n\tauthor = {Seyerle, Amanda A. and Young, Alicia M. and Jeff, Janina M. and Melton, Phillip E. and Jorgensen, Neal W. and Lin, Yi and Carty, Cara L. and Deelman, Ewa and Heckbert, Susan R. and Hindorff, Lucia A. and Jackson, Rebecca D. and Martin, Lisa W. and Okin, Peter M. and Perez, Marco V. and Psaty, Bruce M. and Soliman, Elsayed Z. and Whitsel, Eric A. and North, Kari E. and Laston, Sandra and Kooperberg, Charles and Avery, Christy L.},\n\tcitation-subset = {IM},\n\tcompleted = {2015-06-02},\n\tcountry = {United States},\n\tdoi = {10.1097/EDE.0000000000000168},\n\tissn = {1531-5487},\n\tissn-linking = {1044-3983},\n\tissue = {6},\n\tjournal = {Epidemiology (Cambridge, Mass.)},\n\tkeywords = {Aged; Continental Population Groups, genetics; Electrocardiography; Female; Genetic Predisposition to Disease; Haplotypes; Humans; Long {QT} Syndrome, ethnology, genetics; Male; Middle Aged; Phenotype; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Quantitative Trait, Heritable; Risk Factors},\n\tmid = {NIHMS671623},\n\tmonth = nov,\n\tnlm-id = {9009644},\n\towner = {NLM},\n\tpages = {790--798},\n\tpmc = {PMC4380285},\n\tpmid = {25166880},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/25166880/},\n\n\tpubmodel = {Print},\n\tpubstate = {ppublish},\n\trevised = {2019-12-20},\n\ttitle = {Evidence of heterogeneity by race/ethnicity in genetic determinants of {QT} interval.},\n\tvolume = {25},\n\tyear = {2014},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/25166880/},\n\tbdsk-url-2 = {https://doi.org/10.1097/EDE.0000000000000168}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Association of cancer susceptibility variants with risk of multiple primary cancers: The Population Architecture using Genomics and Epidemiology study.\n \n \n \n \n\n\n \n Park, S. L.; Caberto, C. P.; Lin, Y.; Goodloe, R. J.; Dumitrescu, L.; Love, S.; Matise, T. C.; Hindorff, L. A.; Fowke, J. H.; Schumacher, F. R.; Beebe-Dimmer, J.; Chen, C.; Hou, L.; Thomas, F.; Deelman, E.; Han, Y.; Peters, U.; North, K. E.; Heiss, G.; Crawford, D. C.; Haiman, C. A.; Wilkens, L. R.; Bush, W. S.; Kooperberg, C.; Cheng, I.; and Le Marchand, L.\n\n\n \n\n\n\n Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 23: 2568–2578. November 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Association$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

@article{ParkCabertoLinEtAl2014,\n\tabstract = {Multiple primary cancers account for approximately 16% of all incident cancers in the United States. Although genome-wide association studies ({GWAS}) have identified many common genetic variants associated with various cancer sites, no study has examined the association of these genetic variants with risk of multiple primary cancers (MPC). As part of the National Human Genome Research Institute (NHGRI) {Population Architecture using Genomics and Epidemiology} (PAGE) study, we used data from the Multiethnic Cohort (MEC) and Women's Health Initiative (WHI). Incident MPC (IMPC) cases (n = 1,385) were defined as participants diagnosed with more than one incident cancer after cohort entry. Participants diagnosed with only one incident cancer after cohort entry with follow-up equal to or longer than IMPC cases served as controls (single-index cancer controls; n = 9,626). Fixed-effects meta-analyses of unconditional logistic regression analyses were used to evaluate the associations between 188 cancer risk variants and IMPC risk. To account for multiple comparisons, we used the false-positive report probability (FPRP) to determine statistical significance. A nicotine dependence-associated and lung cancer variant, CHRNA3 rs578776 [OR, 1.16; 95% confidence interval (CI), 1.05-1.26; P = 0.004], and two breast cancer variants, EMBP1 rs11249433 and TOX3 rs3803662 (OR, 1.16; 95% CI, 1.04-1.28; P = 0.005 and OR, 1.13; 95% CI, 1.03-1.23; P = 0.006), were significantly associated with risk of IMPC. The associations for rs578776 and rs11249433 remained (P < 0.05) after removing subjects who had lung or breast cancers, respectively (P ≤ 0.046). These associations did not show significant heterogeneity by smoking status (Pheterogeneity ≥ 0.53). Our study has identified rs578776 and rs11249433 as risk variants for IMPC. These findings may help to identify genetic regions associated with IMPC risk.},\n\tauthor = {Park, S. Lani and Caberto, Christian P. and Lin, Yi and Goodloe, Robert J. and Dumitrescu, Logan and Love, Shelly-Ann and Matise, Tara C. and Hindorff, Lucia A. and Fowke, Jay H. and Schumacher, Fredrick R. and Beebe-Dimmer, Jennifer and Chen, Chu and Hou, Lifang and Thomas, Fridtjof and Deelman, Ewa and Han, Ying and Peters, Ulrike and North, Kari E. and Heiss, Gerardo and Crawford, Dana C. and Haiman, Christopher A. and Wilkens, Lynne R. and Bush, William S. and Kooperberg, Charles and Cheng, Iona and Le Marchand, Lo{\\"\\i}c},\n\tcitation-subset = {IM},\n\tcompleted = {2015-07-08},\n\tcountry = {United States},\n\tdoi = {10.1158/1055-9965.EPI-14-0129},\n\tissn = {1538-7755},\n\tissn-linking = {1055-9965},\n\tissue = {11},\n\tjournal = {Cancer epidemiology, biomarkers \\& prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology},\n\tkeywords = {Aged; Disease Susceptibility; Epidemiologic Studies; Female; Genetic Predisposition to Disease; Genome-Wide Association Study; Genomics; Genotype; Humans; Male; Neoplasms, etiology; Polymorphism, Single Nucleotide},\n\tmid = {NIHMS622393},\n\tmonth = nov,\n\tnlm-id = {9200608},\n\towner = {NLM},\n\tpages = {2568--2578},\n\tpii = {1055-9965.EPI-14-0129},\n\tpmc = {PMC4221293},\n\tpmid = {25139936},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/25139936/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Association of cancer susceptibility variants with risk of multiple primary cancers: The {Population Architecture using Genomics and Epidemiology} study.},\n\tvolume = {23},\n\tyear = {2014},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/25139936/},\n\tbdsk-url-2 = {https://doi.org/10.1158/1055-9965.EPI-14-0129}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Analysis of metabolic syndrome components in $>$15 000 African Americans identifies pleiotropic variants: results from the Population Architecture using Genomics and Epidemiology study.\n \n \n \n \n\n\n \n Carty, C. L.; Bhattacharjee, S.; Haessler, J.; Cheng, I.; Hindorff, L. A.; Aroda, V.; Carlson, C. S.; Hsu, C.; Wilkens, L.; Liu, S.; Selvin, E.; Jackson, R.; North, K. E.; Peters, U.; Pankow, J. S.; Chatterjee, N.; and Kooperberg, C.\n\n\n \n\n\n\n Circulation. Cardiovascular genetics, 7: 505–513. August 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Analysis$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{CartyBhattacharjeeHaesslerEtAl2014,\n\tabstract = {Metabolic syndrome (MetS) refers to the clustering of cardiometabolic risk factors, including dyslipidemia, central adiposity, hypertension, and hyperglycemia, in individuals. Identification of pleiotropic genetic factors associated with MetS traits may shed light on key pathways or mediators underlying MetS. Using the Metabochip array in 15 148 African Americans from the {Population Architecture using Genomics and Epidemiology} (PAGE) study, we identify susceptibility loci and investigate pleiotropy among genetic variants using a subset-based meta-analysis method, ASsociation-analysis-based-on-subSETs (ASSET). Unlike conventional models that lack power when associations for MetS components are null or have opposite effects, Association-analysis-based-on-subsets uses 1-sided tests to detect positive and negative associations for components separately and combines tests accounting for correlations among components. With Association-analysis-based-on-subsets, we identify 27 single nucleotide polymorphisms in 1 glucose and 4 lipids loci (TCF7L2, LPL, APOA5, CETP, and APOC1/APOE/TOMM40) significantly associated with MetS components overall, all P<2.5e-7, the Bonferroni adjusted P value. Three loci replicate in a Hispanic population, n=5172. A novel African American-specific variant, rs12721054/APOC1, and rs10096633/LPL are associated with ≥3 MetS components. We find additional evidence of pleiotropy for APOE, TOMM40, TCF7L2, and CETP variants, many with opposing effects (eg, the same rs7901695/TCF7L2 allele is associated with increased odds of high glucose and decreased odds of central adiposity). We highlight a method to increase power in large-scale genomic association analyses and report a novel variant associated with all MetS components in African Americans. We also identify pleiotropic associations that may be clinically useful in patient risk profiling and for informing translational research of potential gene targets and medications.},\n\tauthor = {Carty, Cara L. and Bhattacharjee, Samsiddhi and Haessler, Jeff and Cheng, Iona and Hindorff, Lucia A. and Aroda, Vanita and Carlson, Christopher S. and Hsu, Chun-Nan and Wilkens, Lynne and Liu, Simin and Selvin, Elizabeth and Jackson, Rebecca and North, Kari E. and Peters, Ulrike and Pankow, James S. and Chatterjee, Nilanjan and Kooperberg, Charles},\n\tchemicals = {APOA5 protein, human, Apolipoprotein A-V, Apolipoprotein C-I, Apolipoproteins A, Blood Glucose, CETP protein, human, Cholesterol Ester Transfer Proteins, Cholesterol, HDL, TCF7L2 protein, human, Transcription Factor 7-Like 2 Protein, apolipoprotein C-I, human, LPL protein, human, Lipoprotein Lipase},\n\tcitation-subset = {IM},\n\tcompleted = {2015-09-02},\n\tcountry = {United States},\n\tdoi = {10.1161/CIRCGENETICS.113.000386},\n\tissn = {1942-3268},\n\tissn-linking = {1942-3268},\n\tissue = {4},\n\tjournal = {Circulation. Cardiovascular genetics},\n\tkeywords = {African Americans, genetics; Aged; Alleles; Apolipoprotein A-V; Apolipoprotein C-I, genetics; Apolipoproteins A, genetics; Blood Glucose, analysis; Cholesterol Ester Transfer Proteins, genetics; Cholesterol, HDL, blood; Female; Genetic Loci; Genetic Pleiotropy; Genetic Predisposition to Disease; Genetic Variation; Genomics; Genotype; Hispanic Americans, genetics; Humans; Lipoprotein Lipase, genetics; Male; Metabolic Syndrome, epidemiology, genetics; Middle Aged; Odds Ratio; Phenotype; Polymorphism, Single Nucleotide; Transcription Factor 7-Like 2 Protein, genetics; African continental ancestry group; Hispanic Americans; genetic pleiotropy; genetic variation; high-density lipoprotein cholesterol; hyperglycemia; metabolic syndrome},\n\tmid = {NIHMS614064},\n\tmonth = aug,\n\tnlm-id = {101489144},\n\towner = {NLM},\n\tpages = {505--513},\n\tpii = {CIRCGENETICS.113.000386},\n\tpmc = {PMC4142758},\n\tpmid = {25023634},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/25023634/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Analysis of metabolic syndrome components in $>$15 000 {African Americans} identifies pleiotropic variants: results from the {Population Architecture using Genomics and Epidemiology} study.},\n\tvolume = {7},\n\tyear = {2014},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/25023634/},\n\tbdsk-url-2 = {https://doi.org/10.1161/CIRCGENETICS.113.000386}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Gene-carbohydrate and gene-fiber interactions and type 2 diabetes in diverse populations from the National Health and Nutrition Examination Surveys (NHANES) as part of the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study.\n \n \n \n \n\n\n \n Villegas, R.; Goodloe, R. J.; McClellan, B. E.; Boston, J.; and Crawford, D. C.\n\n\n \n\n\n\n BMC genetics, 15: 69. June 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Gene-carbohydrate$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{VillegasGoodloeMcClellanEtAl2014,\n\tabstract = {Both environmental and genetic factors impact type 2 diabetes (T2D). To identify such modifiers, we genotyped 15 T2D-associated variants from genome-wide association studies ({GWAS}) in 6,414 non-Hispanic whites, 3,073 non-Hispanic blacks, and 3,633 Mexican American participants from the National Health and Nutrition Examination Surveys (NHANES) and evaluated interactions between these variants and carbohydrate intake and fiber intake. We calculated a genetic risk score (GRS) with the 15 SNPs. The odds ratio for T2D with each GRS point was 1.10 (95% CI: 1.05-1.14) for non-Hispanic whites, 1.07 (95% CI: 1.02-1.13) for non-Hispanic blacks, and 1.11 (95% CI: 1.06-1.17) for Mexican Americans. We identified two gene-carbohydrate interactions (P < 0.05) in non-Hispanic whites (with CDKAL1 rs471253 and FTO rs8050136), two in non-Hispanic blacks (with IGFBP2 rs4402960 and THADA rs7578597), and two in Mexican Americans (with NOTCH2 rs1092398 and TSPAN8-LGRS rs7961581). We found three gene-fiber interactions in non-Hispanic whites (with ADAMT59 rs4607103, CDKN2A/2B rs1801282, and FTO rs8050136), two in non-Hispanic blacks (with ADAMT59 rs4607103 and THADA rs7578597), and two in Mexican Americans (with THADA rs7578597 and TSPAN8-LGRS rs796158) at the P < 0.05 level. Interactions between the GRS and nutrients failed to reach significance in all the racial/ethnic groups. Our results suggest that dietary carbohydrates and fiber may modify T2D-associated variants, highlighting the importance of dietary nutrients in predicting T2D risk.},\n\tauthor = {Villegas, Raquel and Goodloe, Robert J. and McClellan, Bob E. and Boston, Jonathan and Crawford, Dana C.},\n\tchemicals = {Dietary Carbohydrates, Dietary Fiber},\n\tcitation-subset = {IM},\n\tcompleted = {2014-11-04},\n\tcountry = {England},\n\tdoi = {10.1186/1471-2156-15-69},\n\tissn = {1471-2156},\n\tissn-linking = {1471-2156},\n\tjournal = {BMC genetics},\n\tkeywords = {Adolescent; Adult; African Continental Ancestry Group, genetics; Aged; Aged, 80 and over; Diabetes Mellitus, Type 2, genetics; Dietary Carbohydrates; Dietary Fiber; European Continental Ancestry Group, genetics; Female; Gene-Environment Interaction; Genome-Wide Association Study; Genotype; Humans; Male; Mexican Americans, genetics; Middle Aged; Nutrition Surveys; Polymorphism, Single Nucleotide; Risk Factors; Young Adult},\n\tmonth = jun,\n\tnlm-id = {100966978},\n\towner = {NLM},\n\tpages = {69},\n\tpii = {1471-2156-15-69},\n\tpmc = {PMC4094781},\n\tpmid = {24929251},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/24929251/},\n\n\tpubmodel = {Electronic},\n\tpubstate = {epublish},\n\trevised = {2018-11-13},\n\ttitle = {Gene-carbohydrate and gene-fiber interactions and type 2 diabetes in diverse populations from the {National Health and Nutrition Examination Surveys (NHANES)} as part of the {Epidemiologic Architecture for Genes Linked to Environment (EAGLE)} study.},\n\tvolume = {15},\n\tyear = {2014},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/24929251/},\n\tbdsk-url-2 = {https://doi.org/10.1186/1471-2156-15-69}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Cross-cancer pleiotropic analysis of endometrial cancer: PAGE and E2C2 consortia.\n \n \n \n \n\n\n \n Setiawan, V. W.; Schumacher, F.; Prescott, J.; Haessler, J.; Malinowski, J.; Wentzensen, N.; Yang, H.; Chanock, S.; Brinton, L.; Hartge, P.; Lissowska, J.; Park, S. L.; Cheng, I.; Bush, W. S.; Crawford, D. C.; Ursin, G.; Horn-Ross, P.; Bernstein, L.; Lu, L.; Risch, H.; Yu, H.; Sakoda, L. C.; Doherty, J.; Chen, C.; Jackson, R.; Yasmeen, S.; Cote, M.; Kocarnik, J. M.; Peters, U.; Kraft, P.; De Vivo, I.; Haiman, C. A.; Kooperberg, C.; and Le Marchand, L.\n\n\n \n\n\n\n Carcinogenesis, 35: 2068–2073. September 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Cross-cancer$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{SetiawanSchumacherPrescottEtAl2014,\n\tabstract = {Genome-wide association studies ({GWAS}) have identified a large number of cancer-associated single nucleotide polymorphisms (SNPs), several of which have been associated with multiple cancer sites suggesting pleiotropic effects and shared biological mechanisms across some cancers. We hypothesized that SNPs associated with other cancers may be additionally associated with endometrial cancer. We examined 213 SNPs previously associated with 14 other cancers for their associations with endometrial cancer in 3758 endometrial cancer cases and 5966 controls of European ancestry from two consortia: Population Architecture Using Genomics and Epidemiology and the Epidemiology of Endometrial Cancer Consortium. Study-specific logistic regression estimates adjusted for age, body mass index and the most significant principal components of genetic ancestry were combined using fixed-effect meta-analysis to evaluate the association between each {SNP} and endometrial cancer risk. A Bonferroni-corrected P value of 2.35×10(-4) was used to determine statistical significance of the associations. {SNP} rs7679673, ~6.3kb upstream of TET2 and previously reported to be associated with prostate cancer risk, was associated with endometrial cancer risk in the direction opposite to that for prostate cancer [meta-analysis odds ratio = 0.87 (per copy of the C allele), 95% confidence interval = 0.81, 0.93; P = 7.37×10(-5)] with no evidence of heterogeneity across studies (P heterogeneity = 0.66). This pleiotropic analysis is the first to suggest TET2 as a susceptibility locus for endometrial cancer.},\n\tauthor = {Setiawan, Veronica Wendy and Schumacher, Fredrick and Prescott, Jennifer and Haessler, Jeffrey and Malinowski, Jennifer and Wentzensen, Nicolas and Yang, Hannah and Chanock, Stephen and Brinton, Louise and Hartge, Patricia and Lissowska, Jolanta and Park, S. Lani and Cheng, Iona and Bush, William S. and Crawford, Dana C. and Ursin, Giske and Horn-Ross, Pamela and Bernstein, Leslie and Lu, Lingeng and Risch, Harvey and Yu, Herbert and Sakoda, Lori C. and Doherty, Jennifer and Chen, Chu and Jackson, Rebecca and Yasmeen, Shagufta and Cote, Michele and Kocarnik, Jonathan M. and Peters, Ulrike and Kraft, Peter and De Vivo, Immaculata and Haiman, Christopher A. and Kooperberg, Charles and Le Marchand, Loic},\n\tchemicals = {DNA-Binding Proteins, Proto-Oncogene Proteins, TET2 protein, human},\n\tcitation-subset = {IM},\n\tcompleted = {2014-10-21},\n\tcountry = {England},\n\tdoi = {10.1093/carcin/bgu107},\n\tissn = {1460-2180},\n\tissn-linking = {0143-3334},\n\tissue = {9},\n\tjournal = {Carcinogenesis},\n\tkeywords = {Case-Control Studies; DNA-Binding Proteins, genetics; Endometrial Neoplasms, genetics; Female; Genetic Pleiotropy; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Polymorphism, Single Nucleotide; Proto-Oncogene Proteins, genetics; Risk Factors},\n\tmonth = sep,\n\tnlm-id = {8008055},\n\towner = {NLM},\n\tpages = {2068--2073},\n\tpii = {carcin/bgu107},\n\tpmc = {PMC4146418},\n\tpmid = {24832084},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/24832084/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Cross-cancer pleiotropic analysis of endometrial cancer: {PAGE} and {E2C2} consortia.},\n\tvolume = {35},\n\tyear = {2014},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/24832084/},\n\tbdsk-url-2 = {https://doi.org/10.1093/carcin/bgu107}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Investigating the relationship between mitochondrial genetic variation and cardiovascular-related traits to develop a framework for mitochondrial phenome-wide association studies.\n \n \n \n \n\n\n \n Mitchell, S. L.; Hall, J. B.; Goodloe, R. J.; Boston, J.; Farber-Eger, E.; Pendergrass, S. A.; Bush, W. S.; and Crawford, D. C.\n\n\n \n\n\n\n BioData mining, 7: 6. 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Investigating$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@article{MitchellHallGoodloeEtAl2014,\n\tabstract = {Mitochondria play a critical role in the cell and have DNA independent of the nuclear genome. There is much evidence that mitochondrial DNA (mtDNA) variation plays a role in human health and disease, however, this area of investigation has lagged behind research into the role of nuclear genetic variation on complex traits and phenotypic outcomes. Phenome-wide association studies (PheWAS) investigate the association between a wide range of traits and genetic variation. To date, this approach has not been used to investigate the relationship between mtDNA variants and phenotypic variation. Herein, we describe the development of a PheWAS framework for mtDNA variants (mt-PheWAS). Using the Metabochip custom genotyping array, nuclear and mitochondrial DNA variants were genotyped in 11,519 African Americans from the Vanderbilt University biorepository, BioVU. We employed both polygenic modeling and association testing with mitochondrial single nucleotide polymorphisms (mtSNPs) to explore the relationship between mtDNA variants and a group of eight cardiovascular-related traits obtained from de-identified electronic medical records within BioVU. Using polygenic modeling we found evidence for an effect of mtDNA variation on total cholesterol and type 2 diabetes (T2D). After performing comprehensive mitochondrial single {SNP} associations, we identified an increased number of single mtSNP associations with total cholesterol and T2D compared to the other phenotypes examined, which did not have more significantly associated SNPs than would be expected by chance. Among the mtSNPs significantly associated with T2D we identified variant mt16189, an association previously reported only in Asian and European-descent populations. Our replication of previous findings and identification of novel associations from this initial study suggest that our mt-PheWAS approach is robust for investigating the relationship between mitochondrial genetic variation and a range of phenotypes, providing a framework for future mt-PheWAS.},\n\tauthor = {Mitchell, Sabrina L. and Hall, Jacob B. and Goodloe, Robert J. and Boston, Jonathan and Farber-Eger, Eric and Pendergrass, Sarah A. and Bush, William S. and Crawford, Dana C.},\n\tcompleted = {2014-05-20},\n\tcountry = {England},\n\tdoi = {10.1186/1756-0381-7-6},\n\tissn = {1756-0381},\n\tissn-linking = {1756-0381},\n\tjournal = {BioData mining},\n\tkeywords = {GCTA; Mitochondrial DNA variation; Mixed modeling; PheWAS; Polygenic analysis; mtSNP},\n\tnlm-id = {101319161},\n\towner = {NLM},\n\tpages = {6},\n\tpii = {1756-0381-7-6},\n\tpmc = {PMC4021623},\n\tpmid = {24731735},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/24731735/},\n\n\tpubmodel = {Electronic-eCollection},\n\tpubstate = {epublish},\n\trevised = {2018-11-13},\n\ttitle = {Investigating the relationship between mitochondrial genetic variation and cardiovascular-related traits to develop a framework for mitochondrial phenome-wide association studies.},\n\tvolume = {7},\n\tyear = {2014},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/24731735/},\n\tbdsk-url-2 = {https://doi.org/10.1186/1756-0381-7-6}}\n\n

\n\n\n

\n Mitochondria play a critical role in the cell and have DNA independent of the nuclear genome. There is much evidence that mitochondrial DNA (mtDNA) variation plays a role in human health and disease, however, this area of investigation has lagged behind research into the role of nuclear genetic variation on complex traits and phenotypic outcomes. Phenome-wide association studies (PheWAS) investigate the association between a wide range of traits and genetic variation. To date, this approach has not been used to investigate the relationship between mtDNA variants and phenotypic variation. Herein, we describe the development of a PheWAS framework for mtDNA variants (mt-PheWAS). Using the Metabochip custom genotyping array, nuclear and mitochondrial DNA variants were genotyped in 11,519 African Americans from the Vanderbilt University biorepository, BioVU. We employed both polygenic modeling and association testing with mitochondrial single nucleotide polymorphisms (mtSNPs) to explore the relationship between mtDNA variants and a group of eight cardiovascular-related traits obtained from de-identified electronic medical records within BioVU. Using polygenic modeling we found evidence for an effect of mtDNA variation on total cholesterol and type 2 diabetes (T2D). After performing comprehensive mitochondrial single SNP associations, we identified an increased number of single mtSNP associations with total cholesterol and T2D compared to the other phenotypes examined, which did not have more significantly associated SNPs than would be expected by chance. Among the mtSNPs significantly associated with T2D we identified variant mt16189, an association previously reported only in Asian and European-descent populations. Our replication of previous findings and identification of novel associations from this initial study suggest that our mt-PheWAS approach is robust for investigating the relationship between mitochondrial genetic variation and a range of phenotypes, providing a framework for future mt-PheWAS.\n

\n\n\n

\n \n\n \n \n \n \n \n \n Pleiotropic associations of risk variants identified for other cancers with lung cancer risk: the PAGE and TRICL consortia.\n \n \n \n \n\n\n \n Park, S. L.; Fesinmeyer, M. D.; Timofeeva, M.; Caberto, C. P.; Kocarnik, J. M.; Han, Y.; Love, S.; Young, A.; Dumitrescu, L.; Lin, Y.; Goodloe, R.; Wilkens, L. R.; Hindorff, L.; Fowke, J. H.; Carty, C.; Buyske, S.; Schumacher, F. R.; Butler, A.; Dilks, H.; Deelman, E.; Cote, M. L.; Chen, W.; Pande, M.; Christiani, D. C.; Field, J. K.; Bickebller, H.; Risch, A.; Heinrich, J.; Brennan, P.; Wang, Y.; Eisen, T.; Houlston, R. S.; Thun, M.; Albanes, D.; Caporaso, N.; Peters, U.; North, K. E.; Heiss, G.; Crawford, D. C.; Bush, W. S.; Haiman, C. A.; Landi, M. T.; Hung, R. J.; Kooperberg, C.; Amos, C. I.; Le Marchand, L.; and Cheng, I.\n\n\n \n\n\n\n Journal of the National Cancer Institute, 106: dju061. April 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Pleiotropic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{ParkFesinmeyerTimofeevaEtAl2014,\n\tabstract = {Genome-wide association studies have identified hundreds of genetic variants associated with specific cancers. A few of these risk regions have been associated with more than one cancer site; however, a systematic evaluation of the associations between risk variants for other cancers and lung cancer risk has yet to be performed. We included 18023 patients with lung cancer and 60543 control subjects from two consortia, {Population Architecture using Genomics and Epidemiology} (PAGE) and Transdisciplinary Research in Cancer of the Lung (TRICL). We examined 165 single-nucleotide polymorphisms (SNPs) that were previously associated with at least one of 16 non-lung cancer sites. Study-specific logistic regression results underwent meta-analysis, and associations were also examined by race/ethnicity, histological cell type, sex, and smoking status. A Bonferroni-corrected P value of 2.5×10(-5) was used to assign statistical significance. The breast cancer {SNP} LSP1 rs3817198 was associated with an increased risk of lung cancer (odds ratio [OR] = 1.10; 95% confidence interval [CI] = 1.05 to 1.14; P = 2.8×10(-6)). This association was strongest for women with adenocarcinoma (P = 1.2×10(-4)) and not statistically significant in men (P = .14) with this cell type (P het by sex = .10). Two glioma risk variants, TERT rs2853676 and CDKN2BAS1 rs4977756, which are located in regions previously associated with lung cancer, were associated with increased risk of adenocarcinoma (OR = 1.16; 95% CI = 1.10 to 1.22; P = 1.1×10(-8)) and squamous cell carcinoma (OR = 1.13; CI = 1.07 to 1.19; P = 2.5×10(-5)), respectively. Our findings demonstrate a novel pleiotropic association between the breast cancer LSP1 risk region marked by variant rs3817198 and lung cancer risk.},\n\tauthor = {Park, S. Lani and Fesinmeyer, Megan D. and Timofeeva, Maria and Caberto, Christian P. and Kocarnik, Jonathan M. and Han, Younghun and Love, Shelly-Ann and Young, Alicia and Dumitrescu, Logan and Lin, Yi and Goodloe, Robert and Wilkens, Lynne R. and Hindorff, Lucia and Fowke, Jay H. and Carty, Cara and Buyske, Steven and Schumacher, Frederick R. and Butler, Anne and Dilks, Holli and Deelman, Ewa and Cote, Michele L. and Chen, Wei and Pande, Mala and Christiani, David C. and Field, John K. and Bickebller, Heike and Risch, Angela and Heinrich, Joachim and Brennan, Paul and Wang, Yufei and Eisen, Timothy and Houlston, Richard S. and Thun, Michael and Albanes, Demetrius and Caporaso, Neil and Peters, Ulrike and North, Kari E. and Heiss, Gerardo and Crawford, Dana C. and Bush, William S. and Haiman, Christopher A. and Landi, Maria Teresa and Hung, Rayjean J. and Kooperberg, Charles and Amos, Christopher I. and Le Marchand, Lo{\\"\\i}c and Cheng, Iona},\n\tchemicals = {CDKN2B protein, human, Cyclin-Dependent Kinase Inhibitor p15, LSP1 protein, human, Microfilament Proteins, Proto-Oncogene Proteins, TERT protein, human, Telomerase},\n\tcitation-subset = {IM},\n\tcompleted = {2014-06-02},\n\tcountry = {United States},\n\tdoi = {10.1093/jnci/dju061},\n\tissn = {1460-2105},\n\tissn-linking = {0027-8874},\n\tissue = {4},\n\tjournal = {Journal of the National Cancer Institute},\n\tkeywords = {Adenocarcinoma, epidemiology, ethnology, genetics, pathology; Adult; Aged; Breast Neoplasms, epidemiology, ethnology, genetics, pathology; Cyclin-Dependent Kinase Inhibitor p15, genetics; Female; Genome-Wide Association Study; Humans; Interdisciplinary Communication; Logistic Models; Lung Neoplasms, epidemiology, ethnology, genetics, pathology; Male; Microfilament Proteins, genetics, metabolism; Middle Aged; Odds Ratio; Polymorphism, Single Nucleotide; Proto-Oncogene Proteins, genetics, metabolism; Risk Factors; Sex Factors; Smoking, epidemiology; Telomerase, genetics},\n\tmonth = apr,\n\tnlm-id = {7503089},\n\towner = {NLM},\n\tpages = {dju061},\n\tpii = {dju061},\n\tpmc = {PMC3982896},\n\tpmid = {24681604},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/24681604/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Pleiotropic associations of risk variants identified for other cancers with lung cancer risk: the {PAGE} and {TRICL} consortia.},\n\tvolume = {106},\n\tyear = {2014},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/24681604/},\n\tbdsk-url-2 = {https://doi.org/10.1093/jnci/dju061}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Multiancestral analysis of inflammation-related genetic variants and C-reactive protein in the Population Architecture using Genomics and Epidemiology study.\n \n \n \n \n\n\n \n Kocarnik, J. M.; Pendergrass, S. A.; Carty, C. L.; Pankow, J. S.; Schumacher, F. R.; Cheng, I.; Durda, P.; Ambite, J. L.; Deelman, E.; Cook, N. R.; Liu, S.; Wactawski-Wende, J.; Hutter, C.; Brown-Gentry, K.; Wilson, S.; Best, L. G.; Pankratz, N.; Hong, C.; Cole, S. A.; Voruganti, V. S.; B ̊u ̌zkova, P.; Jorgensen, N. W.; Jenny, N. S.; Wilkens, L. R.; Haiman, C. A.; Kolonel, L. N.; Lacroix, A.; North, K.; Jackson, R.; Le Marchand, L.; Hindorff, L. A.; Crawford, D. C.; Gross, M.; and Peters, U.\n\n\n \n\n\n\n Circulation. Cardiovascular genetics, 7: 178–188. April 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Multiancestral$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{KocarnikPendergrassCartyEtAl2014,\n\tabstract = {C-reactive protein (CRP) is a biomarker of inflammation. Genome-wide association studies ({GWAS}) have identified single-nucleotide polymorphisms (SNPs) associated with CRP concentrations and inflammation-related traits such as cardiovascular disease, type 2 diabetes mellitus, and obesity. We aimed to replicate previous CRP-{SNP} associations, assess whether these associations generalize to additional race/ethnicity groups, and evaluate inflammation-related SNPs for a potentially pleiotropic association with CRP. We selected and analyzed 16 CRP-associated and 250 inflammation-related {GWAS} SNPs among 40 473 African American, American Indian, Asian/Pacific Islander, European American, and Hispanic participants from 7 studies collaborating in the {Population Architecture using Genomics and Epidemiology} (PAGE) study. Fixed-effect meta-analyses combined study-specific race/ethnicity-stratified linear regression estimates to evaluate the association between each {SNP} and high-sensitivity CRP. Overall, 18 SNPs in 8 loci were significantly associated with CRP (Bonferroni-corrected P<3.1×10(-3) for replication, P<2.0×10(-4) for pleiotropy): Seven of these were specific to European Americans, while 9 additionally generalized to African Americans (1), Hispanics (5), or both (3); 1 {SNP} was seen only in African Americans and Hispanics. Two SNPs in the CELSR2/PSRC1/SORT1 locus showed a potentially novel association with CRP: rs599839 (P=2.0×10(-6)) and rs646776 (P=3.1×10(-5)). We replicated 16 {SNP}-CRP associations, 10 of which generalized to African Americans and/or Hispanics. We also identified potentially novel pleiotropic associations with CRP for two SNPs previously associated with coronary artery disease and/or low-density lipoprotein-cholesterol. These findings demonstrate the benefit of evaluating genotype-phenotype associations in multiple race/ethnicity groups and looking for pleiotropic relationships among SNPs previously associated with related phenotypes.},\n\tauthor = {Kocarnik, Jonathan M. and Pendergrass, Sarah A. and Carty, Cara L. and Pankow, James S. and Schumacher, Fredrick R. and Cheng, Iona and Durda, Peter and Ambite, Jos{\\'e} Luis and Deelman, Ewa and Cook, Nancy R. and Liu, Simin and Wactawski-Wende, Jean and Hutter, Carolyn and Brown-Gentry, Kristin and Wilson, Sarah and Best, Lyle G. and Pankratz, Nathan and Hong, Ching-Ping and Cole, Shelley A. and Voruganti, V. Saroja and B{\\r u}{\\v z}kova, Petra and Jorgensen, Neal W. and Jenny, Nancy S. and Wilkens, Lynne R. and Haiman, Christopher A. and Kolonel, Laurence N. and Lacroix, Andrea and North, Kari and Jackson, Rebecca and Le Marchand, Loic and Hindorff, Lucia A. and Crawford, Dana C. and Gross, Myron and Peters, Ulrike},\n\tchemicals = {C-Reactive Protein},\n\tcitation-subset = {IM},\n\tcompleted = {2015-03-09},\n\tcountry = {United States},\n\tdoi = {10.1161/CIRCGENETICS.113.000173},\n\tissn = {1942-3268},\n\tissn-linking = {1942-3268},\n\tissue = {2},\n\tjournal = {Circulation. Cardiovascular genetics},\n\tkeywords = {Adult; African Continental Ancestry Group, genetics; Aged; Asian Continental Ancestry Group, genetics; C-Reactive Protein, metabolism; Female; Genetic Variation; Genome-Wide Association Study; Hispanic Americans, genetics; Humans; Indians, North American, genetics; Inflammation, blood, epidemiology, ethnology, genetics; Male; Middle Aged; Polymorphism, Single Nucleotide; United States, epidemiology; Young Adult; C-reactive protein; continental population groups; ethnic groups; genetic pleiotropy; inflammation; molecular epidemiology; polymorphism, single nucleotide},\n\tmid = {NIHMS575195},\n\tmonth = apr,\n\tnlm-id = {101489144},\n\towner = {NLM},\n\tpages = {178--188},\n\tpii = {CIRCGENETICS.113.000173},\n\tpmc = {PMC4104750},\n\tpmid = {24622110},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/24622110/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2019-12-20},\n\ttitle = {Multiancestral analysis of inflammation-related genetic variants and {C}-reactive protein in the {Population Architecture using Genomics and Epidemiology} study.},\n\tvolume = {7},\n\tyear = {2014},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/24622110/},\n\tbdsk-url-2 = {https://doi.org/10.1161/CIRCGENETICS.113.000173}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Pleiotropy of cancer susceptibility variants on the risk of non-Hodgkin lymphoma: the PAGE consortium.\n \n \n \n \n\n\n \n Lim, U.; Kocarnik, J. M.; Bush, W. S.; Matise, T. C.; Caberto, C.; Park, S. L.; Carlson, C. S.; Deelman, E.; Duggan, D.; Fesinmeyer, M.; Haiman, C. A.; Henderson, B. E.; Hindorff, L. A.; Kolonel, L. N.; Peters, U.; Stram, D. O.; Tiirikainen, M.; Wilkens, L. R.; Wu, C.; Kooperberg, C.; and Le Marchand, L.\n\n\n \n\n\n\n PloS one, 9: e89791. 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Pleiotropy$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{LimKocarnikBushEtAl2014,\n\tabstract = {Risk of non-Hodgkin lymphoma (NHL) is higher among individuals with a family history or a prior diagnosis of other cancers. Genome-wide association studies ({GWAS}) have suggested that some genetic susceptibility variants are associated with multiple complex traits (pleiotropy). We investigated whether common risk variants identified in cancer {GWAS} may also increase the risk of developing NHL as the first primary cancer. As part of the {Population Architecture using Genomics and Epidemiology} (PAGE) consortium, 113 cancer risk variants were analyzed in 1,441 NHL cases and 24,183 controls from three studies (BioVU, Multiethnic Cohort Study, Women's Health Initiative) for their association with the risk of overall NHL and common subtypes [diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), chronic lymphocytic leukemia or small lymphocytic lymphoma (CLL/SLL)] using an additive genetic model adjusted for age, sex and ethnicity. Study-specific results for each variant were meta-analyzed across studies. The analysis of NHL subtype-specific {GWAS} SNPs and overall NHL suggested a shared genetic susceptibility between FL and DLBCL, particularly involving variants in the major histocompatibility complex region (rs6457327 in 6p21.33: FL OR=1.29, p=0.013; DLBCL OR=1.23, p=0.013; NHL OR=1.22, p=5.9 × E-05). In the pleiotropy analysis, six risk variants for other cancers were associated with NHL risk, including variants for lung (rs401681 in TERT: OR per C allele=0.89, p=3.7 × E-03; rs4975616 in TERT: OR per A allele=0.90, p=0.01; rs3131379 in MSH5: OR per T allele=1.16, p=0.03), prostate (rs7679673 in TET2: OR per C allele=0.89, p=5.7 × E-03; rs10993994 in MSMB: OR per T allele=1.09, p=0.04), and breast (rs3817198 in LSP1: OR per C allele=1.12, p=0.01) cancers, but none of these associations remained significant after multiple test correction. This study does not support strong pleiotropic effects of non-NHL cancer risk variants in NHL etiology; however, larger studies are warranted.},\n\tauthor = {Lim, Unhee and Kocarnik, Jonathan M. and Bush, William S. and Matise, Tara C. and Caberto, Christian and Park, Sungshim Lani and Carlson, Christopher S. and Deelman, Ewa and Duggan, David and Fesinmeyer, Megan and Haiman, Christopher A. and Henderson, Brian E. and Hindorff, Lucia A. and Kolonel, Laurence N. and Peters, Ulrike and Stram, Daniel O. and Tiirikainen, Maarit and Wilkens, Lynne R. and Wu, Chunyuan and Kooperberg, Charles and Le Marchand, Lo{\\"\\i}c},\n\tcitation-subset = {IM},\n\tcompleted = {2015-01-05},\n\tcountry = {United States},\n\tdoi = {10.1371/journal.pone.0089791},\n\tissn = {1932-6203},\n\tissn-linking = {1932-6203},\n\tissue = {3},\n\tjournal = {PloS one},\n\tkeywords = {Case-Control Studies; Genetic Pleiotropy; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Lymphoma, Non-Hodgkin, genetics; Polymorphism, Single Nucleotide; Risk},\n\tnlm-id = {101285081},\n\towner = {NLM},\n\tpages = {e89791},\n\tpii = {PONE-D-13-29762},\n\tpmc = {PMC3943855},\n\tpmid = {24598796},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/24598796/},\n\n\tpubmodel = {Electronic-eCollection},\n\tpubstate = {epublish},\n\trevised = {2018-11-13},\n\ttitle = {Pleiotropy of cancer susceptibility variants on the risk of non-Hodgkin lymphoma: the {PAGE} consortium.},\n\tvolume = {9},\n\tyear = {2014},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/24598796/},\n\tbdsk-url-2 = {https://doi.org/10.1371/journal.pone.0089791}}\n\n

\n\n\n

\n Risk of non-Hodgkin lymphoma (NHL) is higher among individuals with a family history or a prior diagnosis of other cancers. Genome-wide association studies (GWAS) have suggested that some genetic susceptibility variants are associated with multiple complex traits (pleiotropy). We investigated whether common risk variants identified in cancer GWAS may also increase the risk of developing NHL as the first primary cancer. As part of the Population Architecture using Genomics and Epidemiology (PAGE) consortium, 113 cancer risk variants were analyzed in 1,441 NHL cases and 24,183 controls from three studies (BioVU, Multiethnic Cohort Study, Women's Health Initiative) for their association with the risk of overall NHL and common subtypes [diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), chronic lymphocytic leukemia or small lymphocytic lymphoma (CLL/SLL)] using an additive genetic model adjusted for age, sex and ethnicity. Study-specific results for each variant were meta-analyzed across studies. The analysis of NHL subtype-specific GWAS SNPs and overall NHL suggested a shared genetic susceptibility between FL and DLBCL, particularly involving variants in the major histocompatibility complex region (rs6457327 in 6p21.33: FL OR=1.29, p=0.013; DLBCL OR=1.23, p=0.013; NHL OR=1.22, p=5.9 × E-05). In the pleiotropy analysis, six risk variants for other cancers were associated with NHL risk, including variants for lung (rs401681 in TERT: OR per C allele=0.89, p=3.7 × E-03; rs4975616 in TERT: OR per A allele=0.90, p=0.01; rs3131379 in MSH5: OR per T allele=1.16, p=0.03), prostate (rs7679673 in TET2: OR per C allele=0.89, p=5.7 × E-03; rs10993994 in MSMB: OR per T allele=1.09, p=0.04), and breast (rs3817198 in LSP1: OR per C allele=1.12, p=0.01) cancers, but none of these associations remained significant after multiple test correction. This study does not support strong pleiotropic effects of non-NHL cancer risk variants in NHL etiology; however, larger studies are warranted.\n

\n\n\n

\n \n\n \n \n \n \n \n \n Characterization of mitochondrial haplogroups in a large population-based sample from the United States.\n \n \n \n \n\n\n \n Mitchell, S. L.; Goodloe, R.; Brown-Gentry, K.; Pendergrass, S. A.; Murdock, D. G.; and Crawford, D. C.\n\n\n \n\n\n\n Human genetics, 133: 861–868. July 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Characterization$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{MitchellGoodloeBrownGentryEtAl2014,\n\tabstract = {Mitochondrial DNA (mtDNA) haplogroups are valuable for investigations in forensic science, molecular anthropology, and human genetics. In this study, we developed a custom panel of 61 mtDNA markers for high-throughput classification of European, African, and Native American/Asian mitochondrial haplogroup lineages. Using these mtDNA markers, we constructed a mitochondrial haplogroup classification tree and classified 18,832 participants from the National Health and Nutrition Examination Surveys (NHANES). To our knowledge, this is the largest study to date characterizing mitochondrial haplogroups in a population-based sample from the United States, and the first study characterizing mitochondrial haplogroup distributions in self-identified Mexican Americans separately from Hispanic Americans of other descent. We observed clear differences in the distribution of maternal genetic ancestry consistent with proposed admixture models for these subpopulations, underscoring the genetic heterogeneity of the United States Hispanic population. The mitochondrial haplogroup distributions in the other self-identified racial/ethnic groups within NHANES were largely comparable to previous studies. Mitochondrial haplogroup classification was highly concordant with self-identified race/ethnicity (SIRE) in non-Hispanic whites (94.8 %), but was considerably lower in admixed populations including non-Hispanic blacks (88.3 %), Mexican Americans (81.8 %), and other Hispanics (61.6 %), suggesting SIRE does not accurately reflect maternal genetic ancestry, particularly in populations with greater proportions of admixture. Thus, it is important to consider inconsistencies between SIRE and genetic ancestry when performing genetic association studies. The mitochondrial haplogroup data that we have generated, coupled with the epidemiologic variables in NHANES, is a valuable resource for future studies investigating the contribution of mtDNA variation to human health and disease.},\n\tauthor = {Mitchell, Sabrina L. and Goodloe, Robert and Brown-Gentry, Kristin and Pendergrass, Sarah A. and Murdock, Deborah G. and Crawford, Dana C.},\n\tchemicals = {DNA, Mitochondrial, Genetic Markers},\n\tcitation-subset = {IM},\n\tcompleted = {2014-08-04},\n\tcountry = {Germany},\n\tdoi = {10.1007/s00439-014-1421-9},\n\tissn = {1432-1203},\n\tissn-linking = {0340-6717},\n\tissue = {7},\n\tjournal = {Human genetics},\n\tkeywords = {Adolescent; Adult; Aged; Child; Continental Population Groups; DNA, Mitochondrial, genetics; Female; Genetic Association Studies; Genetic Markers; Genetics, Population; Genome, Human; Genotype; Haplotypes; Hispanic Americans, genetics; Humans; Middle Aged; Nutrition Surveys; Phenotype; Polymorphism, Single Nucleotide; Sequence Analysis, DNA; United States; Young Adult},\n\tmid = {NIHMS562367},\n\tmonth = jul,\n\tnlm-id = {7613873},\n\towner = {NLM},\n\tpages = {861--868},\n\tpmc = {PMC4113317},\n\tpmid = {24488180},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/24488180/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Characterization of mitochondrial haplogroups in a large population-based sample from the {United States}.},\n\tvolume = {133},\n\tyear = {2014},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/24488180/},\n\tbdsk-url-2 = {https://doi.org/10.1007/s00439-014-1421-9}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Replication of associations between GWAS SNPs and melanoma risk in the Population Architecture Using Genomics and Epidemiology (PAGE) Study.\n \n \n \n \n\n\n \n Kocarnik, J. M.; Park, S. L.; Han, J.; Dumitrescu, L.; Cheng, I.; Wilkens, L. R.; Schumacher, F. R.; Kolonel, L.; Carlson, C. S.; Crawford, D. C.; Goodloe, R. J.; Dilks, H.; Baker, P.; Richardson, D.; Ambite, J. L.; Song, F.; Quresh, A. A.; Zhang, M.; Duggan, D.; Hutter, C.; Hindorff, L. A.; Bush, W. S.; Kooperberg, C.; Le Marchand, L.; and Peters, U.\n\n\n \n\n\n\n The Journal of investigative dermatology, 134: 2049–2052. July 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Replication$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{KocarnikParkHanEtAl2014,\n\tauthor = {Kocarnik, Jonathan M. and Park, Sungshim Lani and Han, Jiali and Dumitrescu, Logan and Cheng, Iona and Wilkens, Lynne R. and Schumacher, Fredrick R. and Kolonel, Laurence and Carlson, Chris S. and Crawford, Dana C. and Goodloe, Robert J. and Dilks, Holli and Baker, Paxton and Richardson, Danielle and Ambite, Jos{\\'e} Luis and Song, Fengju and Quresh, Abrar A. and Zhang, Mingfeng and Duggan, David and Hutter, Carolyn and Hindorff, Lucia A. and Bush, William S. and Kooperberg, Charles and Le Marchand, Loic and Peters, Ulrike},\n\tcitation-subset = {IM},\n\tcompleted = {2014-08-19},\n\tcountry = {United States},\n\tdoi = {10.1038/jid.2014.53},\n\tissn = {1523-1747},\n\tissn-linking = {0022-202X},\n\tissue = {7},\n\tjournal = {The Journal of investigative dermatology},\n\tkeywords = {Aged; Female; Genetic Predisposition to Disease, epidemiology, genetics; Genome-Wide Association Study; Humans; Male; Melanoma, epidemiology, genetics; Middle Aged; Polymorphism, Single Nucleotide; Risk Factors; Skin Neoplasms, epidemiology, genetics},\n\tmid = {NIHMS560811},\n\tmonth = jul,\n\tnlm-id = {0426720},\n\towner = {NLM},\n\tpages = {2049--2052},\n\tpii = {S0022-202X(15)36885-8},\n\tpmc = {PMC4057959},\n\tpmid = {24480881},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/24480881/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Replication of associations between {GWAS} {SNP}s and melanoma risk in the {Population Architecture Using Genomics and Epidemiology} ({PAGE}) Study.},\n\tvolume = {134},\n\tyear = {2014},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/24480881/},\n\tbdsk-url-2 = {https://doi.org/10.1038/jid.2014.53}}\n\n

\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Replication of the effect of SLC2A9 genetic variation on serum uric acid levels in American Indians.\n \n \n \n \n\n\n \n Voruganti, V. S.; Franceschini, N.; Haack, K.; Laston, S.; MacCluer, J. W.; Umans, J. G.; Comuzzie, A. G.; North, K. E.; and Cole, S. A.\n\n\n \n\n\n\n European journal of human genetics : EJHG, 22: 938–943. July 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Replication$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{VorugantiFranceschiniHaackEtAl2014,\n\tabstract = {Increased serum uric acid (SUA) or hyperuricemia, a risk factor for gout, renal and cardiovascular diseases, is caused by either increased production or decreased excretion of uric acid or a mix of both. The solute carrier protein 2 family, member 9 (SLC2A9) gene encodes a transporter that mediates urate flux across the renal proximal tubule. Genome-wide association studies have consistently shown the association of single-nucleotide polymorphisms in this gene with SUA in majority populations. American Indian participants of the Strong Heart Family Study, belonging to multigenerational families, have high prevalence of hyperuricemia. We conducted measured genotype analyses, based on variance components decomposition method and accounting for family relationships, to assess whether the association between SUA and SLC2A9 gene polymorphisms generalized to American Indians (n=3604) of this study. Seven polymorphisms were selected for genotyping based on their association with SUA levels in other populations. A strong association was found between SLC2A9 gene polymorphisms and SUA in all centers combined (P-values: 1.3 × 10(-31)-5.1 × 10(-23)) and also when stratified by recruitment center; P-values: 1.2 × 10(-14)-1.0 × 10(-5). These polymorphisms were also associated with the estimated glomerular filtration rate and serum creatinine but not albumin-creatinine ratio. In summary, the association of polymorphisms in the uric acid transporter gene with SUA levels extends to a new population of American Indians.},\n\tauthor = {Voruganti, V. Saroja and Franceschini, Nora and Haack, Karin and Laston, Sandra and MacCluer, Jean W. and Umans, Jason G. and Comuzzie, Anthony G. and North, Kari E. and Cole, Shelley A.},\n\tchemicals = {Glucose Transport Proteins, Facilitative, SLC2A9 protein, human, Uric Acid},\n\tcitation-subset = {IM},\n\tcompleted = {2015-02-12},\n\tcountry = {England},\n\tdoi = {10.1038/ejhg.2013.264},\n\tissn = {1476-5438},\n\tissn-linking = {1018-4813},\n\tissue = {7},\n\tjournal = {European journal of human genetics : EJHG},\n\tkeywords = {Adult; Female; Genetic Predisposition to Disease; Genome-Wide Association Study; Genotype; Glomerular Filtration Rate, genetics; Glucose Transport Proteins, Facilitative, genetics, metabolism; Humans; Hyperuricemia, blood, genetics; Indians, North American, genetics; Male; Middle Aged; Polymorphism, Genetic; Uric Acid, blood},\n\tmonth = jul,\n\tnlm-id = {9302235},\n\towner = {NLM},\n\tpages = {938--943},\n\tpii = {ejhg2013264},\n\tpmc = {PMC4060115},\n\tpmid = {24301058},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/24301058/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Replication of the effect of {SLC2A9} genetic variation on serum uric acid levels in {American Indians}.},\n\tvolume = {22},\n\tyear = {2014},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/24301058/},\n\tbdsk-url-2 = {https://doi.org/10.1038/ejhg.2013.264}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Development of a data-mining algorithm to identify ages at reproductive milestones in electronic medical records.\n \n \n \n \n\n\n \n Malinowski, J.; Farber-Eger, E.; and Crawford, D. C.\n\n\n \n\n\n\n Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing,376–387. 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Development$ Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{MalinowskiFarberEgerCrawford2014,\n\tabstract = {Electronic medical records (EMRs) are becoming more widely implemented following directives from the federal government and incentives for supplemental reimbursements for Medicare and Medicaid claims. Replete with rich phenotypic data, EMRs offer a unique opportunity for clinicians and researchers to identify potential research cohorts and perform epidemiologic studies. Notable limitations to the traditional epidemiologic study include cost, time to complete the study, and limited ancestral diversity; EMR-based epidemiologic studies offer an alternative. The Epidemiologic Architecture for Genes Linked to Environment (EAGLE) Study, as part of the {Population Architecture using Genomics and Epidemiology} (PAGE) I Study, has genotyped more than 15,000 patients of diverse ancestry in BioVU, the Vanderbilt University Medical Center's biorepository linked to the EMR (EAGLE BioVU). We report here the development and performance of data-mining techniques used to identify the age at menarche (AM) and age at menopause (AAM), important milestones in the reproductive lifespan, in women from EAGLE BioVU for genetic association studies. In addition, we demonstrate the ability to discriminate age at naturally-occurring menopause (ANM) from medically-induced menopause. Unusual timing of these events may indicate underlying pathologies and increased risk for some complex diseases and cancer; however, they are not consistently recorded in the EMR. Our algorithm offers a mechanism by which to extract these data for clinical and research goals.},\n\tauthor = {Malinowski, Jennifer and Farber-Eger, Eric and Crawford, Dana C.},\n\tcitation-subset = {IM},\n\tcompleted = {2014-08-14},\n\tcountry = {United States},\n\tissn = {2335-6936},\n\tissn-linking = {2335-6928},\n\tjournal = {Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing},\n\tkeywords = {Adolescent; Adult; Age Factors; Algorithms; Child; Computational Biology; Data Mining, statistics & numerical data; Electronic Health Records, statistics & numerical data; Female; Humans; Menarche, genetics; Menopause, genetics; Middle Aged; Reproductive History; Tennessee},\n\tmid = {NIHMS544695},\n\tnlm-id = {9711271},\n\towner = {NLM},\n\tpages = {376--387},\n\tpii = {9789814583220_0036},\n\tpmc = {PMC3905575},\n\tpmid = {24297563},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/24297563/},\n\n\tpubmodel = {Print},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Development of a data-mining algorithm to identify ages at reproductive milestones in electronic medical records.},\n\tyear = {2014},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/24297563/}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Pleiotropic effects of genetic risk variants for other cancers on colorectal cancer risk: PAGE, GECCO and CCFR consortia.\n \n \n \n \n\n\n \n Cheng, I.; Kocarnik, J. M.; Dumitrescu, L.; Lindor, N. M.; Chang-Claude, J.; Avery, C. L.; Caberto, C. P.; Love, S.; Slattery, M. L.; Chan, A. T.; Baron, J. A.; Hindorff, L. A.; Park, S. L.; Schumacher, F. R.; Hoffmeister, M.; Kraft, P.; Butler, A. M.; Duggan, D. J.; Hou, L.; Carlson, C. S.; Monroe, K. R.; Lin, Y.; Carty, C. L.; Mann, S.; Ma, J.; Giovannucci, E. L.; Fuchs, C. S.; Newcomb, P. A.; Jenkins, M. A.; Hopper, J. L.; Haile, R. W.; Conti, D. V.; Campbell, P. T.; Potter, J. D.; Caan, B. J.; Schoen, R. E.; Hayes, R. B.; Chanock, S. J.; Berndt, S. I.; Küry, S.; Bézieau, S.; Ambite, J. L.; Kumaraguruparan, G.; Richardson, D. M.; Goodloe, R. J.; Dilks, H. H.; Baker, P.; Zanke, B. W.; Lemire, M.; Gallinger, S.; Hsu, L.; Jiao, S.; Harrison, T. A.; Seminara, D.; Haiman, C. A.; Kooperberg, C.; Wilkens, L. R.; Hutter, C. M.; White, E.; Crawford, D. C.; Heiss, G.; Hudson, T. J.; Brenner, H.; Bush, W. S.; Casey, G.; Le Marchand, L.; and Peters, U.\n\n\n \n\n\n\n Gut, 63: 800–807. May 2014.\n \n\n\n\n
\n\n\n\n \n \n $\"Pleiotropic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{ChengKocarnikDumitrescuEtAl2014,\n\tabstract = {Genome-wide association studies have identified a large number of single nucleotide polymorphisms (SNPs) associated with a wide array of cancer sites. Several of these variants demonstrate associations with multiple cancers, suggesting pleiotropic effects and shared biological mechanisms across some cancers. We hypothesised that SNPs previously associated with other cancers may additionally be associated with colorectal cancer. In a large-scale study, we examined 171 SNPs previously associated with 18 different cancers for their associations with colorectal cancer. We examined 13 338 colorectal cancer cases and 40 967 controls from three consortia: {Population Architecture using Genomics and Epidemiology} (PAGE), Genetic Epidemiology of Colorectal Cancer (GECCO), and the Colon Cancer Family Registry (CCFR). Study-specific logistic regression results, adjusted for age, sex, principal components of genetic ancestry, and/or study specific factors (as relevant) were combined using fixed-effect meta-analyses to evaluate the association between each {SNP} and colorectal cancer risk. A Bonferroni-corrected p value of 2.92×10(-4) was used to determine statistical significance of the associations. Two correlated SNPs--rs10090154 and rs4242382--in Region 1 of chromosome 8q24, a prostate cancer susceptibility region, demonstrated statistically significant associations with colorectal cancer risk. The most significant association was observed with rs4242382 (meta-analysis OR=1.12; 95% CI 1.07 to 1.18; p=1.74×10(-5)), which also demonstrated similar associations across racial/ethnic populations and anatomical sub-sites. This is the first study to clearly demonstrate Region 1 of chromosome 8q24 as a susceptibility locus for colorectal cancer; thus, adding colorectal cancer to the list of cancer sites linked to this particular multicancer risk region at 8q24.},\n\tauthor = {Cheng, Iona and Kocarnik, Jonathan M. and Dumitrescu, Logan and Lindor, Noralane M. and Chang-Claude, Jenny and Avery, Christy L. and Caberto, Christian P. and Love, Shelly-Ann and Slattery, Martha L. and Chan, Andrew T. and Baron, John A. and Hindorff, Lucia A. and Park, Sungshim Lani and Schumacher, Fredrick R. and Hoffmeister, Michael and Kraft, Peter and Butler, Anne M. and Duggan, David J. and Hou, Lifang and Carlson, Chris S. and Monroe, Kristine R. and Lin, Yi and Carty, Cara L. and Mann, Sue and Ma, Jing and Giovannucci, Edward L. and Fuchs, Charles S. and Newcomb, Polly A. and Jenkins, Mark A. and Hopper, John L. and Haile, Robert W. and Conti, David V. and Campbell, Peter T. and Potter, John D. and Caan, Bette J. and Schoen, Robert E. and Hayes, Richard B. and Chanock, Stephen J. and Berndt, Sonja I. and K{\\"u}ry, Sebastien and B{\\'e}zieau, Stephane and Ambite, Jose Luis and Kumaraguruparan, Gowri and Richardson, Danielle M. and Goodloe, Robert J. and Dilks, Holli H. and Baker, Paxton and Zanke, Brent W. and Lemire, Mathieu and Gallinger, Steven and Hsu, Li and Jiao, Shuo and Harrison, Tabitha A. and Seminara, Daniela and Haiman, Christopher A. and Kooperberg, Charles and Wilkens, Lynne R. and Hutter, Carolyn M. and White, Emily and Crawford, Dana C. and Heiss, Gerardo and Hudson, Thomas J. and Brenner, Hermann and Bush, William S. and Casey, Graham and Le Marchand, Lo{\\"\\i}c and Peters, Ulrike},\n\tchemicals = {Genetic Markers},\n\tcitation-subset = {AIM, IM},\n\tcompleted = {2014-06-02},\n\tcountry = {England},\n\tdoi = {10.1136/gutjnl-2013-305189},\n\tissn = {1468-3288},\n\tissn-linking = {0017-5749},\n\tissue = {5},\n\tjournal = {Gut},\n\tkeywords = {Aged; Chromosomes, Human, Pair 8; Colorectal Neoplasms, genetics; Female; Genetic Markers; Genetic Pleiotropy; Genetic Predisposition to Disease; Genome-Wide Association Study; Genotyping Techniques; Humans; Logistic Models; Male; Middle Aged; Polymorphism, Single Nucleotide; Principal Component Analysis; Registries; Risk Factors; COLORECTAL CANCER; EPIDEMIOLOGY; POLYMORPHIC VARIATION},\n\tmid = {NIHMS522475},\n\tmonth = may,\n\tnlm-id = {2985108R},\n\towner = {NLM},\n\tpages = {800--807},\n\tpii = {gutjnl-2013-305189},\n\tpmc = {PMC3918490},\n\tpmid = {23935004},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/23935004/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2020-03-11},\n\ttitle = {Pleiotropic effects of genetic risk variants for other cancers on colorectal cancer risk: {PAGE}, {GECCO} and {CCFR} consortia.},\n\tvolume = {63},\n\tyear = {2014},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/23935004/},\n\tbdsk-url-2 = {https://doi.org/10.1136/gutjnl-2013-305189}}\n\n

\n\n\n\n\n\n

\n\n

\n \n 2013\n \n \n (23)\n \n \n

\n \n \n

\n \n\n \n \n \n \n \n \n Lipid trait-associated genetic variation is associated with gallstone disease in the diverse Third National Health and Nutrition Examination Survey (NHANES III).\n \n \n \n \n\n\n \n Goodloe, R.; Brown-Gentry, K.; Gillani, N. B.; Jin, H.; Mayo, P.; Allen, M.; McClellan, B.; Boston, J.; Sutcliffe, C.; Schnetz-Boutaud, N.; Dilks, H. H.; and Crawford, D. C.\n\n\n \n\n\n\n BMC medical genetics, 14: 120. November 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Lipid$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{GoodloeBrownGentryGillaniEtAl2013,\n\tabstract = {Gallstone disease is one of the most common digestive disorders, affecting more than 30 million Americans. Previous twin studies suggest a heritability of 25% for gallstone formation. To date, one genome-wide association study ({GWAS}) has been performed in a population of European-descent. Several candidate gene studies have been performed in various populations, but most have been inconclusive. Given that gallstones consist of up to 80% cholesterol, we hypothesized that common genetic variants associated with high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG) would also be associated with gallstone risk. To test this hypothesis, the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study as part of the {Population Architecture using Genomics and Epidemiology} (PAGE) study performed tests of association between 49 {GWAS}-identified lipid trait SNPs and gallstone disease in non-Hispanic whites (446 cases and 1,962 controls), non-Hispanic blacks (179 cases and 1,540 controls), and Mexican Americans (227 cases and 1,478 controls) ascertained for the population-based Third National Health and Nutrition Examination Survey (NHANES III). At a liberal significance threshold of 0.05, five, four, and four {SNP}(s) were associated with disease risk in non-Hispanic whites, non-Hispanic blacks, and Mexican Americans, respectively. No one {SNP} was associated with gallstone disease risk in all three racial/ethnic groups. The most significant association was observed for ABCG5 rs6756629 in non-Hispanic whites [odds ratio (OR) = 1.89; 95% confidence interval (CI) = 1.44-2.49; p = 0.0001). ABCG5 rs6756629 is in strong linkage disequilibrium with rs11887534 (D19H), a variant previously associated with gallstone disease risk in populations of European-descent. We replicated a previously associated variant for gallstone disease risk in non-Hispanic whites. Further discovery and fine-mapping efforts in diverse populations are needed to fully describe the genetic architecture of gallstone disease risk in humans.},\n\tauthor = {Goodloe, Robert and Brown-Gentry, Kristin and Gillani, Niloufar B. and Jin, Hailing and Mayo, Ping and Allen, Melissa and McClellan, Bob and Boston, Jonathan and Sutcliffe, Cara and Schnetz-Boutaud, Nathalie and Dilks, Holli H. and Crawford, Dana C.},\n\tchemicals = {ABCG5 protein, human, ATP Binding Cassette Transporter, Subfamily G, Member 5, ATP-Binding Cassette Transporters, Cholesterol, HDL, Cholesterol, LDL, Lipoproteins, Triglycerides},\n\tcitation-subset = {IM},\n\tcompleted = {2014-04-08},\n\tcountry = {England},\n\tdoi = {10.1186/1471-2350-14-120},\n\tissn = {1471-2350},\n\tissn-linking = {1471-2350},\n\tjournal = {BMC medical genetics},\n\tkeywords = {ATP Binding Cassette Transporter, Subfamily G, Member 5; ATP-Binding Cassette Transporters, genetics; Adult; African Americans, genetics; Aged; Case-Control Studies; Cholesterol, HDL, genetics; Cholesterol, LDL, genetics; European Continental Ancestry Group, genetics; Female; Gallstones, genetics; Genetic Predisposition to Disease; Genetic Variation; Genome-Wide Association Study; Health Surveys; Humans; Linkage Disequilibrium; Lipoproteins, genetics; Male; Mexican Americans, genetics; Middle Aged; Nutrition Surveys; Polymorphism, Single Nucleotide; Triglycerides, genetics; United States},\n\tmonth = nov,\n\tnlm-id = {100968552},\n\towner = {NLM},\n\tpages = {120},\n\tpii = {1471-2350-14-120},\n\tpmc = {PMC3870971},\n\tpmid = {24256507},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/24256507/},\n\n\tpubmodel = {Electronic},\n\tpubstate = {epublish},\n\trevised = {2018-12-03},\n\ttitle = {Lipid trait-associated genetic variation is associated with gallstone disease in the diverse {Third National Health and Nutrition Examination Survey} ({NHANES III}).},\n\tvolume = {14},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/24256507/},\n\tbdsk-url-2 = {https://doi.org/10.1186/1471-2350-14-120}}\n\n

\n\n\n

\n Gallstone disease is one of the most common digestive disorders, affecting more than 30 million Americans. Previous twin studies suggest a heritability of 25% for gallstone formation. To date, one genome-wide association study (GWAS) has been performed in a population of European-descent. Several candidate gene studies have been performed in various populations, but most have been inconclusive. Given that gallstones consist of up to 80% cholesterol, we hypothesized that common genetic variants associated with high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG) would also be associated with gallstone risk. To test this hypothesis, the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study as part of the Population Architecture using Genomics and Epidemiology (PAGE) study performed tests of association between 49 GWAS-identified lipid trait SNPs and gallstone disease in non-Hispanic whites (446 cases and 1,962 controls), non-Hispanic blacks (179 cases and 1,540 controls), and Mexican Americans (227 cases and 1,478 controls) ascertained for the population-based Third National Health and Nutrition Examination Survey (NHANES III). At a liberal significance threshold of 0.05, five, four, and four SNP(s) were associated with disease risk in non-Hispanic whites, non-Hispanic blacks, and Mexican Americans, respectively. No one SNP was associated with gallstone disease risk in all three racial/ethnic groups. The most significant association was observed for ABCG5 rs6756629 in non-Hispanic whites [odds ratio (OR) = 1.89; 95% confidence interval (CI) = 1.44-2.49; p = 0.0001). ABCG5 rs6756629 is in strong linkage disequilibrium with rs11887534 (D19H), a variant previously associated with gallstone disease risk in populations of European-descent. We replicated a previously associated variant for gallstone disease risk in non-Hispanic whites. Further discovery and fine-mapping efforts in diverse populations are needed to fully describe the genetic architecture of gallstone disease risk in humans.\n

\n\n\n

\n \n\n \n \n \n \n \n \n No evidence of interaction between known lipid-associated genetic variants and smoking in the multi-ethnic PAGE population.\n \n \n \n \n\n\n \n Dumitrescu, L.; Carty, C. L.; Franceschini, N.; Hindorff, L. A.; Cole, S. A.; B ̊u ̌zková, P.; Schumacher, F. R.; Eaton, C. B.; Goodloe, R. J.; Duggan, D. J.; Haessler, J.; Cochran, B.; Henderson, B. E.; Cheng, I.; Johnson, K. C.; Carlson, C. S.; Love, S.; Brown-Gentry, K.; Nato, A. Q.; Quibrera, M.; Shohet, R. V.; Ambite, J. L.; Wilkens, L. R.; Le Marchand, L.; Haiman, C. A.; Buyske, S.; Kooperberg, C.; North, K. E.; Fornage, M.; and Crawford, D. C.\n\n\n \n\n\n\n Human genetics, 132: 1427–1431. December 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"No$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{DumitrescuCartyFranceschiniEtAl2013,\n\tabstract = {Genome-wide association studies ({GWAS}) have identified many variants that influence high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and/or triglycerides. However, environmental modifiers, such as smoking, of these known genotype-phenotype associations are just recently emerging in the literature. We have tested for interactions between smoking and 49 {GWAS}-identified variants in over 41,000 racially/ethnically diverse samples with lipid levels from the Population Architecture Using Genomics and Epidemiology (PAGE) study. Despite their biological plausibility, we were unable to detect significant {SNP} × smoking interactions.},\n\tauthor = {Dumitrescu, Logan and Carty, Cara L. and Franceschini, Nora and Hindorff, Lucia A. and Cole, Shelley A. and B{\\r u}{\\v z}kov{\\'a}, Petra and Schumacher, Fredrick R. and Eaton, Charles B. and Goodloe, Robert J. and Duggan, David J. and Haessler, Jeff and Cochran, Barbara and Henderson, Brian E. and Cheng, Iona and Johnson, Karen C. and Carlson, Chris S. and Love, Shelly-Anne and Brown-Gentry, Kristin and Nato, Alejandro Q. and Quibrera, Miguel and Shohet, Ralph V. and Ambite, Jos{\\'e} Luis and Wilkens, Lynne R. and Le Marchand, Lo{\\"\\i}c and Haiman, Christopher A. and Buyske, Steven and Kooperberg, Charles and North, Kari E. and Fornage, Myriam and Crawford, Dana C.},\n\tchemicals = {Cholesterol, HDL, Cholesterol, LDL, Triglycerides},\n\tcitation-subset = {IM},\n\tcompleted = {2014-01-22},\n\tcountry = {Germany},\n\tdoi = {10.1007/s00439-013-1375-3},\n\tissn = {1432-1203},\n\tissn-linking = {0340-6717},\n\tissue = {12},\n\tjournal = {Human genetics},\n\tkeywords = {Cholesterol, HDL, metabolism; Cholesterol, LDL, metabolism; Cohort Studies; Ethnic Groups, genetics; Female; Gene Frequency; Gene-Environment Interaction; Genetics, Population; Genome-Wide Association Study, statistics & numerical data; Humans; Lipid Metabolism, genetics; Male; Polymorphism, Single Nucleotide; Prevalence; Smoking, epidemiology, ethnology, genetics, metabolism; Triglycerides, metabolism; Young Adult},\n\tmid = {NIHMS530028},\n\tmonth = dec,\n\tnlm-id = {7613873},\n\towner = {NLM},\n\tpages = {1427--1431},\n\tpmc = {PMC3895337},\n\tpmid = {24100633},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/24100633/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {No evidence of interaction between known lipid-associated genetic variants and smoking in the multi-ethnic {PAGE} population.},\n\tvolume = {132},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/24100633/},\n\tbdsk-url-2 = {https://doi.org/10.1007/s00439-013-1375-3}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Generalization and dilution of association results from European GWAS in populations of non-European ancestry: the PAGE study.\n \n \n \n \n\n\n \n Carlson, C. S.; Matise, T. C.; North, K. E.; Haiman, C. A.; Fesinmeyer, M. D.; Buyske, S.; Schumacher, F. R.; Peters, U.; Franceschini, N.; Ritchie, M. D.; Duggan, D. J.; Spencer, K. L.; Dumitrescu, L.; Eaton, C. B.; Thomas, F.; Young, A.; Carty, C.; Heiss, G.; Le Marchand, L.; Crawford, D. C.; Hindorff, L. A.; Kooperberg, C. L.; and Consortium, P.\n\n\n \n\n\n\n PLoS biology, 11: e1001661. September 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Generalization$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{CarlsonMatiseNorthEtAl2013,\n\tabstract = {The vast majority of genome-wide association study ({GWAS}) findings reported to date are from populations with European Ancestry (EA), and it is not yet clear how broadly the genetic associations described will generalize to populations of diverse ancestry. The Population Architecture Using Genomics and Epidemiology (PAGE) study is a consortium of multi-ancestry, population-based studies formed with the objective of refining our understanding of the genetic architecture of common traits emerging from {GWAS}. In the present analysis of five common diseases and traits, including body mass index, type 2 diabetes, and lipid levels, we compare direction and magnitude of effects for {GWAS}-identified variants in multiple non-EA populations against EA findings. We demonstrate that, in all populations analyzed, a significant majority of {GWAS}-identified variants have allelic associations in the same direction as in EA, with none showing a statistically significant effect in the opposite direction, after adjustment for multiple testing. However, 25% of tagSNPs identified in EA {GWAS} have significantly different effect sizes in at least one non-EA population, and these differential effects were most frequent in African Americans where all differential effects were diluted toward the null. We demonstrate that differential LD between tagSNPs and functional variants within populations contributes significantly to dilute effect sizes in this population. Although most variants identified from {GWAS} in EA populations generalize to all non-EA populations assessed, genetic models derived from {GWAS} findings in EA may generate spurious results in non-EA populations due to differential effect sizes. Regardless of the origin of the differential effects, caution should be exercised in applying any genetic risk prediction model based on tagSNPs outside of the ancestry group in which it was derived. Models based directly on functional variation may generalize more robustly, but the identification of functional variants remains challenging.},\n\tauthor = {Carlson, Christopher S. and Matise, Tara C. and North, Kari E. and Haiman, Christopher A. and Fesinmeyer, Megan D. and Buyske, Steven and Schumacher, Fredrick R. and Peters, Ulrike and Franceschini, Nora and Ritchie, Marylyn D. and Duggan, David J. and Spencer, Kylee L. and Dumitrescu, Logan and Eaton, Charles B. and Thomas, Fridtjof and Young, Alicia and Carty, Cara and Heiss, Gerardo and Le Marchand, Loic and Crawford, Dana C. and Hindorff, Lucia A. and Kooperberg, Charles L. and PAGE Consortium},\n\tchemicals = {Lipids},\n\tcitation-subset = {IM},\n\tcompleted = {2014-04-08},\n\tcountry = {United States},\n\tdoi = {10.1371/journal.pbio.1001661},\n\tinvestigator = {Matise, Tara and Buyske, Steve and Higashio, Julia and Nato, Andrew and Ambite, Jose Luis and Deelman, Ewa and Manolio, Teri and Hindorff, Lucia and Junkins, Heather and Ramos, Erin and North, Kari E and Heiss, Gerardo and Taylor, Kira and Franceschini, Nora and Avery, Christy and Graff, Misa and Lin, Danyu and Quibrera, Miguel and Cochran, Barbara and Kao, Linda and Umans, Jason and Cole, Shelley and MacCluer, Jean and Person, Sharina and Pankow, James and Boerwinkle, Eric and Fornage, Myriam and Durda, Peter and Jenny, Nancy and Patsy, Bruce and Arnold, Alice and Buzkova, Petra and Crawford, Dana and Haines, Jonathan and Murdock, Deborah and Glenn, Kim and Brown-Gentry, Kristin and Thornton-Wells, Tricia and Dumitrescu, Logan and Jeff, Janina and Bush, William S and Mitchell, Sabrina L and Goodloe, Robert and Wilson, Sarah and Boston, Jonathan and Malinowski, Jennifer and Restrepo, Nicole and Oetjens, Matthew and Fowke, Jay and Zheng, Wei and Spencer, Kylee and Ritchie, Marylyn and Pendergrass, Sarah and Le Marchand, Lo{\\"\\i}c and Wilkens, Lynne and Park, Lani and Tiirikainen, Maarit and Kolonel, Laurence and Lim, Unhee and Cheng, Iona and Wang, Hansong and Shohet, Ralph and Haiman, Christopher and Stram, Daniel and Henderson, Brian and Monroe, Kristine and Schumacher, Fredrick and Kooperberg, Charles and Peters, Ulrike and Anderson, Garnet and Carlson, Chris and Prentice, Ross and LaCroix, Andrea and Wu, Chunyuan and Carty, Cara and Gong, Jian and Rosse, Stephanie and Young, Alicia and Haessler, Jeff and Kocarnik, Jonathan and Fesinmeyer, Megan and Lin, Yi and Jackson, Rebecca and Duggan, David and Kuller, Lew},\n\tissn = {1545-7885},\n\tissn-linking = {1544-9173},\n\tissue = {9},\n\tjournal = {PLoS biology},\n\tkeywords = {African Americans, genetics; Asian Americans, genetics; Body Mass Index; Diabetes Mellitus, Type 2, genetics; European Continental Ancestry Group, genetics; Gene Frequency; Genetic Predisposition to Disease; Genetic Variation; Genome-Wide Association Study, methods; Hispanic Americans, genetics; Humans; Indians, North American, genetics; Lipids, blood, genetics; Metagenomics, methods; Oceanic Ancestry Group, genetics; Polymorphism, Single Nucleotide, genetics},\n\tmonth = sep,\n\tnlm-id = {101183755},\n\towner = {NLM},\n\tpages = {e1001661},\n\tpii = {PBIOLOGY-D-13-00491},\n\tpmc = {PMC3775722},\n\tpmid = {24068893},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/24068893/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Generalization and dilution of association results from {European} {GWAS} in populations of non-{European} ancestry: the {PAGE} study.},\n\tvolume = {11},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/24068893/},\n\tbdsk-url-2 = {https://doi.org/10.1371/journal.pbio.1001661}}\n\n

\n\n\n

\n The vast majority of genome-wide association study (GWAS) findings reported to date are from populations with European Ancestry (EA), and it is not yet clear how broadly the genetic associations described will generalize to populations of diverse ancestry. The Population Architecture Using Genomics and Epidemiology (PAGE) study is a consortium of multi-ancestry, population-based studies formed with the objective of refining our understanding of the genetic architecture of common traits emerging from GWAS. In the present analysis of five common diseases and traits, including body mass index, type 2 diabetes, and lipid levels, we compare direction and magnitude of effects for GWAS-identified variants in multiple non-EA populations against EA findings. We demonstrate that, in all populations analyzed, a significant majority of GWAS-identified variants have allelic associations in the same direction as in EA, with none showing a statistically significant effect in the opposite direction, after adjustment for multiple testing. However, 25% of tagSNPs identified in EA GWAS have significantly different effect sizes in at least one non-EA population, and these differential effects were most frequent in African Americans where all differential effects were diluted toward the null. We demonstrate that differential LD between tagSNPs and functional variants within populations contributes significantly to dilute effect sizes in this population. Although most variants identified from GWAS in EA populations generalize to all non-EA populations assessed, genetic models derived from GWAS findings in EA may generate spurious results in non-EA populations due to differential effect sizes. Regardless of the origin of the differential effects, caution should be exercised in applying any genetic risk prediction model based on tagSNPs outside of the ancestry group in which it was derived. Models based directly on functional variation may generalize more robustly, but the identification of functional variants remains challenging.\n

\n\n\n

\n \n\n \n \n \n \n \n \n Genetic variants associated with fasting glucose and insulin concentrations in an ethnically diverse population: results from the Population Architecture using Genomics and Epidemiology (PAGE) study.\n \n \n \n \n\n\n \n Fesinmeyer, M. D.; Meigs, J. B.; North, K. E.; Schumacher, F. R.; B ̊u ̌zková, P.; Franceschini, N.; Haessler, J.; Goodloe, R.; Spencer, K. L.; Voruganti, V. S.; Howard, B. V.; Jackson, R.; Kolonel, L. N.; Liu, S.; Manson, J. E.; Monroe, K. R.; Mukamal, K.; Dilks, H. H.; Pendergrass, S. A.; Nato, A.; Wan, P.; Wilkens, L. R.; Le Marchand, L.; Ambite, J. L.; Buyske, S.; Florez, J. C.; Crawford, D. C.; Hindorff, L. A.; Haiman, C. A.; Peters, U.; and Pankow, J. S.\n\n\n \n\n\n\n BMC medical genetics, 14: 98. September 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Genetic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{FesinmeyerMeigsNorthEtAl2013,\n\tabstract = {Multiple genome-wide association studies ({GWAS}) within European populations have implicated common genetic variants associated with insulin and glucose concentrations. In contrast, few studies have been conducted within minority groups, which carry the highest burden of impaired glucose homeostasis and type 2 diabetes in the U.S. As part of the '{Population Architecture using Genomics and Epidemiology} (PAGE) Consortium, we investigated the association of up to 10 {GWAS}-identified single nucleotide polymorphisms (SNPs) in 8 genetic regions with glucose or insulin concentrations in up to 36,579 non-diabetic subjects including 23,323 European Americans (EA) and 7,526 African Americans (AA), 3,140 Hispanics, 1,779 American Indians (AI), and 811 Asians. We estimated the association between each {SNP} and fasting glucose or log-transformed fasting insulin, followed by meta-analysis to combine results across PAGE sites. Overall, our results show that 9/9 {GWAS} SNPs are associated with glucose in EA (p = 0.04 to 9 × 10-15), versus 3/9 in AA (p= 0.03 to 6 × 10-5), 3/4 SNPs in Hispanics, 2/4 SNPs in AI, and 1/2 SNPs in Asians. For insulin we observed a significant association with rs780094/GCKR in EA, Hispanics and AI only. Generalization of results across multiple racial/ethnic groups helps confirm the relevance of some of these loci for glucose and insulin metabolism. Lack of association in non-EA groups may be due to insufficient power, or to unique patterns of linkage disequilibrium.},\n\tauthor = {Fesinmeyer, Megan D. and Meigs, James B. and North, Kari E. and Schumacher, Fredrick R. and B{\\r u}{\\v z}kov{\\'a}, Petra and Franceschini, Nora and Haessler, Jeffrey and Goodloe, Robert and Spencer, Kylee L. and Voruganti, Venkata Saroja and Howard, Barbara V. and Jackson, Rebecca and Kolonel, Laurence N. and Liu, Simin and Manson, JoAnn E. and Monroe, Kristine R. and Mukamal, Kenneth and Dilks, Holli H. and Pendergrass, Sarah A. and Nato, Andrew and Wan, Peggy and Wilkens, Lynne R. and Le Marchand, Loic and Ambite, Jos{\\'e} Luis and Buyske, Steven and Florez, Jose C. and Crawford, Dana C. and Hindorff, Lucia A. and Haiman, Christopher A. and Peters, Ulrike and Pankow, James S.},\n\tchemicals = {Adaptor Proteins, Signal Transducing, Blood Glucose, GCKR protein, human, Insulin, TCF7L2 protein, human, Transcription Factor 7-Like 2 Protein},\n\tcitation-subset = {IM},\n\tcompleted = {2014-01-06},\n\tcountry = {England},\n\tdoi = {10.1186/1471-2350-14-98},\n\tissn = {1471-2350},\n\tissn-linking = {1471-2350},\n\tjournal = {BMC medical genetics},\n\tkeywords = {Adaptor Proteins, Signal Transducing, genetics; Adult; African Americans, genetics; Aged; Alleles; Asian Continental Ancestry Group, genetics; Blood Glucose, analysis; Diabetes Mellitus, Type 2, epidemiology, ethnology, genetics; European Continental Ancestry Group, genetics; Female; Gene Frequency; Genetic Loci; Genome-Wide Association Study; Genomics; Hispanic Americans, genetics; Humans; Indians, North American, genetics; Insulin, blood, genetics; Male; Middle Aged; Polymorphism, Single Nucleotide; Transcription Factor 7-Like 2 Protein, genetics},\n\tmonth = sep,\n\tnlm-id = {100968552},\n\towner = {NLM},\n\tpages = {98},\n\tpii = {1471-2350-14-98},\n\tpmc = {PMC3849560},\n\tpmid = {24063630},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/24063630/},\n\n\tpubmodel = {Electronic},\n\tpubstate = {epublish},\n\trevised = {2018-11-13},\n\ttitle = {Genetic variants associated with fasting glucose and insulin concentrations in an ethnically diverse population: results from the {Population Architecture using Genomics and Epidemiology} ({PAGE}) study.},\n\tvolume = {14},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/24063630/},\n\tbdsk-url-2 = {https://doi.org/10.1186/1471-2350-14-98}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Imputation of coding variants in African Americans: better performance using data from the exome sequencing project.\n \n \n \n \n\n\n \n Duan, Q.; Liu, E. Y.; Auer, P. L.; Zhang, G.; Lange, E. M.; Jun, G.; Bizon, C.; Jiao, S.; Buyske, S.; Franceschini, N.; Carlson, C. S.; Hsu, L.; Reiner, A. P.; Peters, U.; Haessler, J.; Curtis, K.; Wassel, C. L.; Robinson, J. G.; Martin, L. W.; Haiman, C. A.; Le Marchand, L.; Matise, T. C.; Hindorff, L. A.; Crawford, D. C.; Assimes, T. L.; Kang, H. M.; Heiss, G.; Jackson, R. D.; Kooperberg, C.; Wilson, J. G.; Abecasis, G. R.; North, K. E.; Nickerson, D. A.; Lange, L. A.; and Li, Y.\n\n\n \n\n\n\n Bioinformatics (Oxford, England), 29: 2744–2749. November 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Imputation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{DuanLiuAuerEtAl2013,\n\tabstract = {Although the 1000 Genomes haplotypes are the most commonly used reference panel for imputation, medical sequencing projects are generating large alternate sets of sequenced samples. Imputation in African Americans using 3384 haplotypes from the Exome Sequencing Project, compared with 2184 haplotypes from 1000 Genomes Project, increased effective sample size by 8.3-11.4% for coding variants with minor allele frequency <1%. No loss of imputation quality was observed using a panel built from phenotypic extremes. We recommend using haplotypes from Exome Sequencing Project alone or concatenation of the two panels over quality score-based post-imputation selection or IMPUTE2's two-panel combination. yunli@med.unc.edu. Supplementary data are available at Bioinformatics online.},\n\tauthor = {Duan, Qing and Liu, Eric Yi and Auer, Paul L. and Zhang, Guosheng and Lange, Ethan M. and Jun, Goo and Bizon, Chris and Jiao, Shuo and Buyske, Steven and Franceschini, Nora and Carlson, Chris S. and Hsu, Li and Reiner, Alex P. and Peters, Ulrike and Haessler, Jeffrey and Curtis, Keith and Wassel, Christina L. and Robinson, Jennifer G. and Martin, Lisa W. and Haiman, Christopher A. and Le Marchand, Loic and Matise, Tara C. and Hindorff, Lucia A. and Crawford, Dana C. and Assimes, Themistocles L. and Kang, Hyun Min and Heiss, Gerardo and Jackson, Rebecca D. and Kooperberg, Charles and Wilson, James G. and Abecasis, Gon{\\c c}alo R. and North, Kari E. and Nickerson, Deborah A. and Lange, Leslie A. and Li, Yun},\n\tcitation-subset = {IM},\n\tcompleted = {2014-03-31},\n\tcountry = {England},\n\tdoi = {10.1093/bioinformatics/btt477},\n\tissn = {1367-4811},\n\tissn-linking = {1367-4803},\n\tissue = {21},\n\tjournal = {Bioinformatics (Oxford, England)},\n\tkeywords = {African Americans, genetics; Exome; Gene Frequency; Genetic Variation; Genome, Human; Genome-Wide Association Study; Haplotypes; Humans; Phenotype; Polymorphism, Single Nucleotide; Sequence Analysis, DNA, methods},\n\tmonth = nov,\n\tnlm-id = {9808944},\n\towner = {NLM},\n\tpages = {2744--2749},\n\tpii = {btt477},\n\tpmc = {PMC3799474},\n\tpmid = {23956302},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/23956302/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-12-02},\n\ttitle = {Imputation of coding variants in {African Americans}: better performance using data from the exome sequencing project.},\n\tvolume = {29},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/23956302/},\n\tbdsk-url-2 = {https://doi.org/10.1093/bioinformatics/btt477}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Epidemiology and genetic determinants of progressive deterioration of glycaemia in American Indians: the Strong Heart Family Study.\n \n \n \n \n\n\n \n Franceschini, N.; Haack, K.; Göring, H. H. H.; Voruganti, V. S.; Laston, S.; Almasy, L.; Lee, E. T.; Best, L. G.; Fabsitz, R. R.; North, K. E.; Maccluer, J. W.; Meigs, J. B.; Pankow, J. S.; and Cole, S. A.\n\n\n \n\n\n\n Diabetologia, 56: 2194–2202. October 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Epidemiology$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{FranceschiniHaackGoeringEtAl2013,\n\tabstract = {Type 2 diabetes is a chronic, heterogeneous disease and a major risk factor for cardiovascular diseases. The underlying mechanisms leading to progression to type 2 diabetes are not fully understood and genetic tools may help to identify important pathways of glycaemic deterioration. Using prospective data on American Indians from the Strong Heart Family Study, we identified 373 individuals defined as progressors (diabetes incident cases), 566 individuals with transitory impaired fasting glucose (IFG) and 1,011 controls (normal fasting glycaemia at all visits). We estimated the heritability (h(2)) of the traits and the evidence for association with 16 known variants identified in type 2 diabetes genome-wide association studies. We noted high h(2) for diabetes progression (h(2) = 0.65 $\\pm$ 0.16, p = 2.7 × 10(-6)) but little contribution of genetic factors to transitory IFG (h(2) = 0.09 $\\pm$ 0.10, p = 0.19) for models adjusted for multiple risk factors. At least three variants (in WFS1, TSPAN8 and THADA) were nominally associated with diabetes progression in age- and sex-adjusted analyses with estimates showing the same direction of effects as reported in the discovery European ancestry studies. Our findings do not exclude these loci for diabetes susceptibility in American Indians and suggest phenotypic heterogeneity of the IFG trait, which may have implications for genetic studies when diagnosis is based on a single time-point measure.},\n\tauthor = {Franceschini, N. and Haack, K. and G{\\"o}ring, H. H. H. and Voruganti, V. S. and Laston, S. and Almasy, L. and Lee, E. T. and Best, L. G. and Fabsitz, R. R. and North, K. E. and Maccluer, J. W. and Meigs, J. B. and Pankow, J. S. and Cole, S. A.},\n\tchemicals = {Blood Glucose},\n\tcitation-subset = {IM},\n\tcompleted = {2014-04-11},\n\tcountry = {Germany},\n\tdoi = {10.1007/s00125-013-2988-8},\n\tissn = {1432-0428},\n\tissn-linking = {0012-186X},\n\tissue = {10},\n\tjournal = {Diabetologia},\n\tkeywords = {Adult; Blood Glucose, analysis, genetics; Diabetes Mellitus, Type 2, blood, epidemiology, genetics; Female; Genetic Predisposition to Disease; Humans; Indians, North American; Male; Middle Aged; Polymorphism, Single Nucleotide, genetics},\n\tmid = {NIHMS505886},\n\tmonth = oct,\n\tnlm-id = {0006777},\n\towner = {NLM},\n\tpages = {2194--2202},\n\tpmc = {PMC3773080},\n\tpmid = {23851660},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/23851660/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Epidemiology and genetic determinants of progressive deterioration of glycaemia in {American Indians}: the {Strong Heart Family Study}.},\n\tvolume = {56},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/23851660/},\n\tbdsk-url-2 = {https://doi.org/10.1007/s00125-013-2988-8}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Association of the FTO obesity risk variant rs8050136 with percentage of energy intake from fat in multiple racial/ethnic populations: the PAGE study.\n \n \n \n \n\n\n \n Park, S. L.; Cheng, I.; Pendergrass, S. A.; Kucharska-Newton, A. M.; Lim, U.; Ambite, J. L.; Caberto, C. P.; Monroe, K. R.; Schumacher, F.; Hindorff, L. A.; Oetjens, M. T.; Wilson, S.; Goodloe, R. J.; Love, S.; Henderson, B. E.; Kolonel, L. N.; Haiman, C. A.; Crawford, D. C.; North, K. E.; Heiss, G.; Ritchie, M. D.; Wilkens, L. R.; and Le Marchand, L.\n\n\n \n\n\n\n American journal of epidemiology, 178: 780–790. September 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Association$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{ParkChengPendergrassEtAl2013,\n\tabstract = {Common obesity risk variants have been associated with macronutrient intake; however, these associations' generalizability across populations has not been demonstrated. We investigated the associations between 6 obesity risk variants in (or near) the NEGR1, TMEM18, BDNF, FTO, MC4R, and KCTD15 genes and macronutrient intake (carbohydrate, protein, ethanol, and fat) in 3 {Population Architecture using Genomics and Epidemiology} (PAGE) studies: the Multiethnic Cohort Study (1993-2006) (n = 19,529), the Atherosclerosis Risk in Communities Study (1987-1989) (n = 11,114), and the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) Study, which accesses data from the Third National Health and Nutrition Examination Survey (1991-1994) (n = 6,347). We used linear regression, with adjustment for age, sex, and ethnicity, to estimate the associations between obesity risk genotypes and macronutrient intake. A fixed-effects meta-analysis model showed that the FTO rs8050136 A allele (n = 36,973) was positively associated with percentage of calories derived from fat (βmeta = 0.2244 (standard error, 0.0548); P = 4 × 10(-5)) and inversely associated with percentage of calories derived from carbohydrate (βmeta = -0.2796 (standard error, 0.0709); P = 8 × 10(-5)). In the Multiethnic Cohort Study, percentage of calories from fat assessed at baseline was a partial mediator of the rs8050136 effect on body mass index (weight (kg)/height (m)(2)) obtained at 10 years of follow-up (mediation of effect = 0.0823 kg/m(2), 95% confidence interval: 0.0559, 0.1128). Our data provide additional evidence that the association of FTO with obesity is partially mediated by dietary intake.},\n\tauthor = {Park, Sungshim Lani and Cheng, Iona and Pendergrass, Sarah A. and Kucharska-Newton, Anna M. and Lim, Unhee and Ambite, Jose Luis and Caberto, Christian P. and Monroe, Kristine R. and Schumacher, Fredrick and Hindorff, Lucia A. and Oetjens, Matthew T. and Wilson, Sarah and Goodloe, Robert J. and Love, Shelly-Ann and Henderson, Brian E. and Kolonel, Laurence N. and Haiman, Christopher A. and Crawford, Dana C. and North, Kari E. and Heiss, Gerardo and Ritchie, Marylyn D. and Wilkens, Lynne R. and Le Marchand, Lo{\\"\\i}c},\n\tchemicals = {Dietary Fats, Proteins, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, FTO protein, human},\n\tcitation-subset = {IM},\n\tcompleted = {2013-11-04},\n\tcountry = {United States},\n\tdoi = {10.1093/aje/kwt028},\n\tissn = {1476-6256},\n\tissn-linking = {0002-9262},\n\tissue = {5},\n\tjournal = {American journal of epidemiology},\n\tkeywords = {Adult; Aged; Alpha-Ketoglutarate-Dependent Dioxygenase FTO; Continental Population Groups, genetics; Diet; Dietary Fats, administration & dosage; Energy Intake; Ethnic Groups, genetics; Female; Genotype; Humans; Male; Middle Aged; Obesity, ethnology, genetics; Polymorphism, Single Nucleotide; Proteins, genetics; Risk Factors; energy intake; fat mass and obesity-associated (FTO) gene; obesity; percent calories from fat; race/ethnicity},\n\tmonth = sep,\n\tnlm-id = {7910653},\n\towner = {NLM},\n\tpages = {780--790},\n\tpii = {kwt028},\n\tpmc = {PMC3755639},\n\tpmid = {23820787},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/23820787/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Association of the {FTO} obesity risk variant rs8050136 with percentage of energy intake from fat in multiple racial/ethnic populations: the {PAGE} study.},\n\tvolume = {178},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/23820787/},\n\tbdsk-url-2 = {https://doi.org/10.1093/aje/kwt028}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Post-genome-wide association study challenges for lipid traits: describing age as a modifier of gene-lipid associations in the Population Architecture using Genomics and Epidemiology (PAGE) study.\n \n \n \n \n\n\n \n Dumitrescu, L.; Carty, C. L.; Franceschini, N.; Hindorff, L. A.; Cole, S. A.; B ̊u ̌zková, P.; Schumacher, F. R.; Eaton, C. B.; Goodloe, R. J.; Duggan, D. J.; Haessler, J.; Cochran, B.; Henderson, B. E.; Cheng, I.; Johnson, K. C.; Carlson, C. S.; Love, S.; Brown-Gentry, K.; Nato, A. Q.; Quibrera, M.; Anderson, G.; Shohet, R. V.; Ambite, J. L.; Wilkens, L. R.; Marchand, L.; Haiman, C. A.; Buyske, S.; Kooperberg, C.; North, K. E.; Fornage, M.; and Crawford, D. C.\n\n\n \n\n\n\n Annals of human genetics, 77: 416–425. September 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Post-genome-wide$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{DumitrescuCartyFranceschiniEtAl2013a,\n\tabstract = {Numerous common genetic variants that influence plasma high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), and triglyceride distributions have been identified via genome-wide association studies ({GWAS}). However, whether or not these associations are age-dependent has largely been overlooked. We conducted an association study and meta-analysis in more than 22,000 European Americans between 49 previously identified {GWAS} variants and the three lipid traits, stratified by age (males: <50 or ≥50 years of age; females: pre- or postmenopausal). For each variant, a test of heterogeneity was performed between the two age strata and significant Phet values were used as evidence of age-specific genetic effects. We identified seven associations in females and eight in males that displayed suggestive heterogeneity by age (Phet < 0.05). The association between rs174547 (FADS1) and LDL-C in males displayed the most evidence for heterogeneity between age groups (Phet = 1.74E-03, I(2) = 89.8), with a significant association in older males (P = 1.39E-06) but not younger males (P = 0.99). However, none of the suggestive modifying effects survived adjustment for multiple testing, highlighting the challenges of identifying modifiers of modest {SNP}-trait associations despite large sample sizes.},\n\tauthor = {Dumitrescu, Logan and Carty, Cara L. and Franceschini, Nora and Hindorff, Lucia A. and Cole, Shelley A. and B{\\r u}{\\v z}kov{\\'a}, Petra and Schumacher, Fredrick R. and Eaton, Charles B. and Goodloe, Robert J. and Duggan, David J. and Haessler, Jeff and Cochran, Barbara and Henderson, Brian E. and Cheng, Iona and Johnson, Karen C. and Carlson, Chris S. and Love, Shelly-Ann and Brown-Gentry, Kristin and Nato, Alejandro Q. and Quibrera, Miguel and Anderson, Garnet and Shohet, Ralph V. and Ambite, Jos{\\'e} Luis and Wilkens, Lynne R. and Marchand, Lo{\\"\\i}c Le and Haiman, Christopher A. and Buyske, Steven and Kooperberg, Charles and North, Kari E. and Fornage, Myriam and Crawford, Dana C.},\n\tchemicals = {Lipids},\n\tcitation-subset = {IM},\n\tcompleted = {2014-09-25},\n\tcountry = {England},\n\tdoi = {10.1111/ahg.12027},\n\tissn = {1469-1809},\n\tissn-linking = {0003-4800},\n\tissue = {5},\n\tjournal = {Annals of human genetics},\n\tkeywords = {Adult; Aged; European Continental Ancestry Group, genetics; Female; Genetic Association Studies; Genome-Wide Association Study; Humans; Lipids, blood; Male; Middle Aged; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Quantitative Trait, Heritable; Risk Factors; PAGE; age; genetic association; lipids; modifier},\n\tmid = {NIHMS482879},\n\tmonth = sep,\n\tnlm-id = {0416661},\n\towner = {NLM},\n\tpages = {416--425},\n\tpmc = {PMC3796061},\n\tpmid = {23808484},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/23808484/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2020-03-05},\n\ttitle = {Post-genome-wide association study challenges for lipid traits: describing age as a modifier of gene-lipid associations in the {Population Architecture using Genomics and Epidemiology} ({PAGE}) study.},\n\tvolume = {77},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/23808484/},\n\tbdsk-url-2 = {https://doi.org/10.1111/ahg.12027}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Enabling high-throughput genotype-phenotype associations in the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) project as part of the Population Architecture using Genomics and Epidemiology (PAGE) study.\n \n \n \n \n\n\n \n Bush, W. S.; Boston, J.; Pendergrass, S. A.; Dumitrescu, L.; Goodloe, R.; Brown-Gentry, K.; Wilson, S.; McClellan, B.; Torstenson, E.; Basford, M. A.; Spencer, K. L.; Ritchie, M. D.; and Crawford, D. C.\n\n\n \n\n\n\n Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing,373–384. 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Enabling$ Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{BushBostonPendergrassEtAl2013,\n\tabstract = {Genetic association studies have rapidly become a major tool for identifying the genetic basis of common human diseases. The advent of cost-effective genotyping coupled with large collections of samples linked to clinical outcomes and quantitative traits now make it possible to systematically characterize genotype-phenotype relationships in diverse populations and extensive datasets. To capitalize on these advancements, the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) project, as part of the collaborative {Population Architecture using Genomics and Epidemiology} (PAGE) study, accesses two collections: the National Health and Nutrition Examination Surveys (NHANES) and BioVU, Vanderbilt University's biorepository linked to de-identified electronic medical records. We describe herein the workflows for accessing and using the epidemiologic (NHANES) and clinical (BioVU) collections, where each workflow has been customized to reflect the content and data access limitations of each respective source. We also describe the process by which these data are generated, standardized, and shared for meta-analysis among the PAGE study sites. As a specific example of the use of BioVU, we describe the data mining efforts to define cases and controls for genetic association studies of common cancers in PAGE. Collectively, the efforts described here are a generalized outline for many of the successful approaches that can be used in the era of high-throughput genotype-phenotype associations for moving biomedical discovery forward to new frontiers of data generation and analysis.},\n\tauthor = {Bush, William S. and Boston, Jonathan and Pendergrass, Sarah A. and Dumitrescu, Logan and Goodloe, Robert and Brown-Gentry, Kristin and Wilson, Sarah and McClellan, Bob and Torstenson, Eric and Basford, Melissa A. and Spencer, Kylee L. and Ritchie, Marylyn D. and Crawford, Dana C.},\n\tcitation-subset = {IM},\n\tcompleted = {2013-12-10},\n\tcountry = {United States},\n\tissn = {2335-6936},\n\tissn-linking = {2335-6928},\n\tjournal = {Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing},\n\tkeywords = {Computational Biology; Databases, Nucleic Acid, statistics & numerical data; Gene-Environment Interaction; Genetic Association Studies, statistics & numerical data; Genetics, Population, statistics & numerical data; High-Throughput Screening Assays, statistics & numerical data; Humans; Linear Models; Neoplasms, genetics; Nutrition Surveys, statistics & numerical data; Polymorphism, Single Nucleotide; Registries, statistics & numerical data},\n\tmid = {NIHMS431925},\n\tnlm-id = {9711271},\n\towner = {NLM},\n\tpages = {373--384},\n\tpii = {9789814447973_0037},\n\tpmc = {PMC3579641},\n\tpmid = {23424142},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/23424142/},\n\n\tpubmodel = {Print},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Enabling high-throughput genotype-phenotype associations in the {Epidemiologic Architecture for Genes Linked to Environment} ({EAGLE}) project as part of the {Population Architecture using Genomics and Epidemiology} ({PAGE}) study.},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/23424142/}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Replication of genetic loci for ages at menarche and menopause in the multi-ethnic Population Architecture using Genomics and Epidemiology (PAGE) study.\n \n \n \n \n\n\n \n Carty, C. L.; Spencer, K. L.; Setiawan, V. W.; Fernandez-Rhodes, L.; Malinowski, J.; Buyske, S.; Young, A.; Jorgensen, N. W.; Cheng, I.; Carlson, C. S.; Brown-Gentry, K.; Goodloe, R.; Park, A.; Parikh, N. I.; Henderson, B.; Le Marchand, L.; Wactawski-Wende, J.; Fornage, M.; Matise, T. C.; Hindorff, L. A.; Arnold, A. M.; Haiman, C. A.; Franceschini, N.; Peters, U.; and Crawford, D. C.\n\n\n \n\n\n\n Human reproduction (Oxford, England), 28: 1695–1706. June 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Replication$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{CartySpencerSetiawanEtAl2013,\n\tabstract = {Do genetic associations identified in genome-wide association studies ({GWAS}) of age at menarche (AM) and age at natural menopause (ANM) replicate in women of diverse race/ancestry from the {Population Architecture using Genomics and Epidemiology} (PAGE) Study? We replicated {GWAS} reproductive trait single nucleotide polymorphisms (SNPs) in our European descent population and found that many SNPs were also associated with AM and ANM in populations of diverse ancestry. Menarche and menopause mark the reproductive lifespan in women and are important risk factors for chronic diseases including obesity, cardiovascular disease and cancer. Both events are believed to be influenced by environmental and genetic factors, and vary in populations differing by genetic ancestry and geography. Most genetic variants associated with these traits have been identified in {GWAS} of European-descent populations. A total of 42 251 women of diverse ancestry from PAGE were included in cross-sectional analyses of AM and ANM. SNPs previously associated with ANM (n = 5 SNPs) and AM (n = 3 SNPs) in {GWAS} were genotyped in American Indians, African Americans, Asians, European Americans, Hispanics and Native Hawaiians. To test {SNP} associations with ANM or AM, we used linear regression models stratified by race/ethnicity and PAGE sub-study. Results were then combined in race-specific fixed effect meta-analyses for each outcome. For replication and generalization analyses, significance was defined at P < 0.01 for ANM analyses and P < 0.017 for AM analyses. We replicated findings for AM SNPs in the LIN28B locus and an intergenic region on 9q31 in European Americans. The LIN28B SNPs (rs314277 and rs314280) were also significantly associated with AM in Asians, but not in other race/ethnicity groups. Linkage disequilibrium (LD) patterns at this locus varied widely among the ancestral groups. With the exception of an intergenic {SNP} at 13q34, all ANM SNPs replicated in European Americans. Three were significantly associated with ANM in other race/ethnicity populations: rs2153157 (6p24.2/SYCP2L), rs365132 (5q35/UIMC1) and rs16991615 (20p12.3/MCM8). While rs1172822 (19q13/BRSK1) was not significant in the populations of non-European descent, effect sizes showed similar trends. Lack of association for the {GWAS} SNPs in the non-European American groups may be due to differences in locus LD patterns between these groups and the European-descent populations included in the {GWAS} discovery studies; and in some cases, lower power may also contribute to non-significant findings. The discovery of genetic variants associated with the reproductive traits provides an important opportunity to elucidate the biological mechanisms involved with normal variation and disorders of menarche and menopause. In this study we replicated most, but not all reported SNPs in European descent populations and examined the epidemiologic architecture of these early reported variants, describing their generalizability and effect size across differing ancestral populations. Such data will be increasingly important for prioritizing {GWAS} SNPs for follow-up in fine-mapping and resequencing studies, as well as in translational research.},\n\tauthor = {Carty, C. L. and Spencer, K. L. and Setiawan, V. W. and Fernandez-Rhodes, L. and Malinowski, J. and Buyske, S. and Young, A. and Jorgensen, N. W. and Cheng, I. and Carlson, C. S. and Brown-Gentry, K. and Goodloe, R. and Park, A. and Parikh, N. I. and Henderson, B. and Le Marchand, L. and Wactawski-Wende, J. and Fornage, M. and Matise, T. C. and Hindorff, L. A. and Arnold, A. M. and Haiman, C. A. and Franceschini, N. and Peters, U. and Crawford, D. C.},\n\tcitation-subset = {IM},\n\tcompleted = {2014-01-29},\n\tcountry = {England},\n\tdoi = {10.1093/humrep/det071},\n\tissn = {1460-2350},\n\tissn-linking = {0268-1161},\n\tissue = {6},\n\tjournal = {Human reproduction (Oxford, England)},\n\tkeywords = {Age Factors; Cross-Sectional Studies; Female; Genome-Wide Association Study; Genotype; Humans; Menarche, ethnology, genetics; Menopause, ethnology, genetics; Polymorphism, Single Nucleotide; genome-wide association study; menarche; menopause; race/ethnicity; single nucleotide polymorphism},\n\tmonth = jun,\n\tnlm-id = {8701199},\n\towner = {NLM},\n\tpages = {1695--1706},\n\tpii = {det071},\n\tpmc = {PMC3657124},\n\tpmid = {23508249},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/23508249/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Replication of genetic loci for ages at menarche and menopause in the multi-ethnic {Population Architecture using Genomics and Epidemiology} ({PAGE}) study.},\n\tvolume = {28},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/23508249/},\n\tbdsk-url-2 = {https://doi.org/10.1093/humrep/det071}}\n\n

\n\n\n

\n Do genetic associations identified in genome-wide association studies (GWAS) of age at menarche (AM) and age at natural menopause (ANM) replicate in women of diverse race/ancestry from the Population Architecture using Genomics and Epidemiology (PAGE) Study? We replicated GWAS reproductive trait single nucleotide polymorphisms (SNPs) in our European descent population and found that many SNPs were also associated with AM and ANM in populations of diverse ancestry. Menarche and menopause mark the reproductive lifespan in women and are important risk factors for chronic diseases including obesity, cardiovascular disease and cancer. Both events are believed to be influenced by environmental and genetic factors, and vary in populations differing by genetic ancestry and geography. Most genetic variants associated with these traits have been identified in GWAS of European-descent populations. A total of 42 251 women of diverse ancestry from PAGE were included in cross-sectional analyses of AM and ANM. SNPs previously associated with ANM (n = 5 SNPs) and AM (n = 3 SNPs) in GWAS were genotyped in American Indians, African Americans, Asians, European Americans, Hispanics and Native Hawaiians. To test SNP associations with ANM or AM, we used linear regression models stratified by race/ethnicity and PAGE sub-study. Results were then combined in race-specific fixed effect meta-analyses for each outcome. For replication and generalization analyses, significance was defined at P < 0.01 for ANM analyses and P < 0.017 for AM analyses. We replicated findings for AM SNPs in the LIN28B locus and an intergenic region on 9q31 in European Americans. The LIN28B SNPs (rs314277 and rs314280) were also significantly associated with AM in Asians, but not in other race/ethnicity groups. Linkage disequilibrium (LD) patterns at this locus varied widely among the ancestral groups. With the exception of an intergenic SNP at 13q34, all ANM SNPs replicated in European Americans. Three were significantly associated with ANM in other race/ethnicity populations: rs2153157 (6p24.2/SYCP2L), rs365132 (5q35/UIMC1) and rs16991615 (20p12.3/MCM8). While rs1172822 (19q13/BRSK1) was not significant in the populations of non-European descent, effect sizes showed similar trends. Lack of association for the GWAS SNPs in the non-European American groups may be due to differences in locus LD patterns between these groups and the European-descent populations included in the GWAS discovery studies; and in some cases, lower power may also contribute to non-significant findings. The discovery of genetic variants associated with the reproductive traits provides an important opportunity to elucidate the biological mechanisms involved with normal variation and disorders of menarche and menopause. In this study we replicated most, but not all reported SNPs in European descent populations and examined the epidemiologic architecture of these early reported variants, describing their generalizability and effect size across differing ancestral populations. Such data will be increasingly important for prioritizing GWAS SNPs for follow-up in fine-mapping and resequencing studies, as well as in translational research.\n

\n\n\n

\n \n\n \n \n \n \n \n \n Characterization of the Metabochip in diverse populations from the International HapMap Project in the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) project.\n \n \n \n \n\n\n \n Crawford, D. C.; Goodloe, R.; Brown-Gentry, K.; Wilson, S.; Roberson, J.; Gillani, N. B.; Ritchie, M. D.; Dilks, H. H.; and Bush, W. S.\n\n\n \n\n\n\n Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing,188–199. 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Characterization$ Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{CrawfordGoodloeBrownGentryEtAl2013,\n\tabstract = {Genome-wide association studies ({GWAS}) have identified hundreds of genomic regions associated with common human disease and quantitative traits. A major research avenue for mature genotype-phenotype associations is the identification of the true risk or functional variant for downstream molecular studies or personalized medicine applications. As part of the {Population Architecture using Genomics and Epidemiology} (PAGE) study, we as Epidemiologic Architecture for Genes Linked to Environment (EAGLE) are fine-mapping {GWAS}-identified genomic regions for common diseases and quantitative traits. We are currently genotyping the Metabochip, a custom content BeadChip designed for fine-mapping metabolic diseases and traits, in∼15,000 DNA samples from patients of African, Hispanic, and Asian ancestry linked to de-identified electronic medical records from the Vanderbilt University biorepository (BioVU). As an initial study of quality control, we report here the genotyping data for 360 samples of European, African, Asian, and Mexican descent from the International HapMap Project. In addition to quality control metrics, we report the overall allele frequency distribution, overall population differentiation (as measured by FST), and linkage disequilibrium patterns for a select {GWAS}-identified region associated with low-density lipoprotein cholesterol levels to illustrate the utility of the Metabochip for fine-mapping studies in the diverse populations expected in EAGLE, the PAGE study, and other efforts underway designed to characterize the complex genetic architecture underlying common human disease and quantitative traits.},\n\tauthor = {Crawford, Dana C. and Goodloe, Robert and Brown-Gentry, Kristin and Wilson, Sarah and Roberson, Jamie and Gillani, Niloufar B. and Ritchie, Marylyn D. and Dilks, Holli H. and Bush, William S.},\n\tchemicals = {Cholesterol, LDL},\n\tcitation-subset = {IM},\n\tcompleted = {2013-12-10},\n\tcountry = {United States},\n\tissn = {2335-6936},\n\tissn-linking = {2335-6928},\n\tjournal = {Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing},\n\tkeywords = {Cholesterol, LDL, blood, genetics; Chromosome Mapping, statistics & numerical data; Computational Biology; Gene Frequency; Gene Regulatory Networks; Gene-Environment Interaction; Genome-Wide Association Study, statistics & numerical data; HapMap Project; Humans; Linkage Disequilibrium; Oligonucleotide Array Sequence Analysis, statistics & numerical data; Polymorphism, Single Nucleotide; Precision Medicine; Quantitative Trait Loci},\n\tmid = {NIHMS433093},\n\tnlm-id = {9711271},\n\towner = {NLM},\n\tpages = {188--199},\n\tpii = {9789814447973_0019},\n\tpmc = {PMC3584704},\n\tpmid = {23424124},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/23424124/},\n\n\tpubmodel = {Print},\n\tpubstate = {ppublish},\n\trevised = {2018-12-21},\n\ttitle = {Characterization of the {Metabochip} in diverse populations from the {International HapMap Project} in the {Epidemiologic Architecture for Genes Linked to Environment (EAGLE)} project.},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/23424124/}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Effects of smoking on the genetic risk of obesity: the Population Architecture using Genomics and Epidemiology study.\n \n \n \n \n\n\n \n Fesinmeyer, M. D.; North, K. E.; Lim, U.; B ̊u ̌zková, P.; Crawford, D. C.; Haessler, J.; Gross, M. D.; Fowke, J. H.; Goodloe, R.; Love, S.; Graff, M.; Carlson, C. S.; Kuller, L. H.; Matise, T. C.; Hong, C.; Henderson, B. E.; Allen, M.; Rohde, R. R.; Mayo, P.; Schnetz-Boutaud, N.; Monroe, K. R.; Ritchie, M. D.; Prentice, R. L.; Kolonel, L. N.; Manson, J. E.; Pankow, J.; Hindorff, L. A.; Franceschini, N.; Wilkens, L. R.; Haiman, C. A.; Le Marchand, L.; and Peters, U.\n\n\n \n\n\n\n BMC medical genetics, 14: 6. January 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Effects$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{FesinmeyerNorthLimEtAl2013,\n\tabstract = {Although smoking behavior is known to affect body mass index (BMI), the potential for smoking to influence genetic associations with BMI is largely unexplored. As part of the '{Population Architecture using Genomics and Epidemiology} (PAGE)' Consortium, we investigated interaction between genetic risk factors associated with BMI and smoking for 10 single nucleotide polymorphisms (SNPs) previously identified in genome-wide association studies. We included 6 studies with a total of 56,466 subjects (16,750 African Americans (AA) and 39,716 European Americans (EA)). We assessed effect modification by testing an interaction term for each {SNP} and smoking (current vs. former/never) in the linear regression and by stratified analyses. We did not observe strong evidence for interactions and only observed two interactions with p-values <0.1: for rs6548238/TMEM18, the risk allele (C) was associated with BMI only among AA females who were former/never smokers (β = 0.018, p = 0.002), vs. current smokers (β = 0.001, p = 0.95, p(interaction) = 0.10). For rs9939609/FTO, the A allele was more strongly associated with BMI among current smoker EA females (β = 0.017, p = 3.5 x 10(-5)), vs. former/never smokers (β = 0.006, p = 0.05, p(interaction) = 0.08). These analyses provide limited evidence that smoking status may modify genetic effects of previously identified genetic risk factors for BMI. Larger studies are needed to follow up our results. NCT00000611.},\n\tauthor = {Fesinmeyer, Megan D. and North, Kari E. and Lim, Unhee and B{\\r u}{\\v z}kov{\\'a}, Petra and Crawford, Dana C. and Haessler, Jeffrey and Gross, Myron D. and Fowke, Jay H. and Goodloe, Robert and Love, Shelley-Ann and Graff, Misa and Carlson, Christopher S. and Kuller, Lewis H. and Matise, Tara C. and Hong, Ching-Ping and Henderson, Brian E. and Allen, Melissa and Rohde, Rebecca R. and Mayo, Ping and Schnetz-Boutaud, Nathalie and Monroe, Kristine R. and Ritchie, Marylyn D. and Prentice, Ross L. and Kolonel, Lawrence N. and Manson, JoAnn E. and Pankow, James and Hindorff, Lucia A. and Franceschini, Nora and Wilkens, Lynne R. and Haiman, Christopher A. and Le Marchand, Loic and Peters, Ulrike},\n\tchemicals = {Membrane Proteins, Proteins, TMEM18 protein, human, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, FTO protein, human},\n\tcitation-subset = {IM},\n\tcompleted = {2013-04-12},\n\tcountry = {England},\n\tdoi = {10.1186/1471-2350-14-6},\n\tissn = {1471-2350},\n\tissn-linking = {1471-2350},\n\tjournal = {BMC medical genetics},\n\tkeywords = {Adolescent; Adult; African Americans, genetics; Aged; Alpha-Ketoglutarate-Dependent Dioxygenase FTO; Body Mass Index; European Continental Ancestry Group, genetics; Female; Genetic Predisposition to Disease; Humans; Male; Membrane Proteins, genetics; Middle Aged; Obesity, epidemiology, genetics; Polymorphism, Single Nucleotide; Proteins, genetics; Risk Factors; Smoking, adverse effects, genetics; Young Adult},\n\tmonth = jan,\n\tnlm-id = {100968552},\n\towner = {NLM},\n\tpages = {6},\n\tpii = {1471-2350-14-6},\n\tpmc = {PMC3564691},\n\tpmid = {23311614},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/23311614/},\n\n\tpubmodel = {Electronic},\n\tpubstate = {epublish},\n\trevised = {2018-11-13},\n\ttitle = {Effects of smoking on the genetic risk of obesity: the {Population Architecture using Genomics and Epidemiology} study.},\n\tvolume = {14},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/23311614/},\n\tbdsk-url-2 = {https://doi.org/10.1186/1471-2350-14-6}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n The influence of obesity-related single nucleotide polymorphisms on BMI across the life course: the PAGE study.\n \n \n \n \n\n\n \n Graff, M.; Gordon-Larsen, P.; Lim, U.; Fowke, J. H.; Love, S.; Fesinmeyer, M.; Wilkens, L. R.; Vertilus, S.; Ritchie, M. D.; Prentice, R. L.; Pankow, J.; Monroe, K.; Manson, J. E.; Le Marchand, L.; Kuller, L. H.; Kolonel, L. N.; Hong, C. P.; Henderson, B. E.; Haessler, J.; Gross, M. D.; Goodloe, R.; Franceschini, N.; Carlson, C. S.; Buyske, S.; B ̊u ̌zková, P.; Hindorff, L. A.; Matise, T. C.; Crawford, D. C.; Haiman, C. A.; Peters, U.; and North, K. E.\n\n\n \n\n\n\n Diabetes, 62: 1763–1767. May 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{GraffGordonLarsenLimEtAl2013,\n\tabstract = {Evidence is limited as to whether heritable risk of obesity varies throughout adulthood. Among >34,000 European Americans, aged 18-100 years, from multiple U.S. studies in the {Population Architecture using Genomics and Epidemiology} (PAGE) Consortium, we examined evidence for heterogeneity in the associations of five established obesity risk variants (near FTO, GNPDA2, MTCH2, TMEM18, and NEGR1) with BMI across four distinct epochs of adulthood: 1) young adulthood (ages 18-25 years), adulthood (ages 26-49 years), middle-age adulthood (ages 50-69 years), and older adulthood (ages ≥70 years); or 2) by menopausal status in women and stratification by age 50 years in men. Summary-effect estimates from each meta-analysis were compared for heterogeneity across the life epochs. We found heterogeneity in the association of the FTO (rs8050136) variant with BMI across the four adulthood epochs (P = 0.0006), with larger effects in young adults relative to older adults (β [SE] = 1.17 [0.45] vs. 0.09 [0.09] kg/m², respectively, per A allele) and smaller intermediate effects. We found no evidence for heterogeneity in the association of GNPDA2, MTCH2, TMEM18, and NEGR1 with BMI across adulthood. Genetic predisposition to obesity may have greater effects on body weight in young compared with older adulthood for FTO, suggesting changes by age, generation, or secular trends. Future research should compare and contrast our findings with results using longitudinal data.},\n\tauthor = {Graff, Mariaelisa and Gordon-Larsen, Penny and Lim, Unhee and Fowke, Jay H. and Love, Shelly-Ann and Fesinmeyer, Megan and Wilkens, Lynne R. and Vertilus, Shawyntee and Ritchie, Marilyn D. and Prentice, Ross L. and Pankow, Jim and Monroe, Kristine and Manson, JoAnn E. and Le Marchand, Lo{\\"\\i}c and Kuller, Lewis H. and Kolonel, Laurence N. and Hong, Ching P. and Henderson, Brian E. and Haessler, Jeff and Gross, Myron D. and Goodloe, Robert and Franceschini, Nora and Carlson, Christopher S. and Buyske, Steven and B{\\r u}{\\v z}kov{\\'a}, Petra and Hindorff, Lucia A. and Matise, Tara C. and Crawford, Dana C. and Haiman, Christopher A. and Peters, Ulrike and North, Kari E.},\n\tchemicals = {Proteins, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, FTO protein, human},\n\tcitation-subset = {AIM, IM},\n\tcompleted = {2013-06-28},\n\tcountry = {United States},\n\tdoi = {10.2337/db12-0863},\n\tissn = {1939-327X},\n\tissn-linking = {0012-1797},\n\tissue = {5},\n\tjournal = {Diabetes},\n\tkeywords = {Adolescent; Adult; Aged; Aged, 80 and over; Aging; Alpha-Ketoglutarate-Dependent Dioxygenase FTO; Body Mass Index; Cohort Studies; Cross-Sectional Studies; European Continental Ancestry Group; Female; Genetic Association Studies; Health Surveys; Humans; Male; Middle Aged; Obesity, genetics, metabolism; Polymorphism, Single Nucleotide; Proteins, genetics, metabolism; United States; Young Adult},\n\tmonth = may,\n\tnlm-id = {0372763},\n\towner = {NLM},\n\tpages = {1763--1767},\n\tpii = {db12-0863},\n\tpmc = {PMC3636619},\n\tpmid = {23300277},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/23300277/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {The influence of obesity-related single nucleotide polymorphisms on {BMI} across the life course: the {PAGE} study.},\n\tvolume = {62},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/23300277/},\n\tbdsk-url-2 = {https://doi.org/10.2337/db12-0863}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Phenome-wide association study (PheWAS) for detection of pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network.\n \n \n \n \n\n\n \n Pendergrass, S. A.; Brown-Gentry, K.; Dudek, S.; Frase, A.; Torstenson, E. S.; Goodloe, R.; Ambite, J. L.; Avery, C. L.; Buyske, S.; B ̊u ̌zková, P.; Deelman, E.; Fesinmeyer, M. D.; Haiman, C. A.; Heiss, G.; Hindorff, L. A.; Hsu, C.; Jackson, R. D.; Kooperberg, C.; Le Marchand, L.; Lin, Y.; Matise, T. C.; Monroe, K. R.; Moreland, L.; Park, S. L.; Reiner, A.; Wallace, R.; Wilkens, L. R.; Crawford, D. C.; and Ritchie, M. D.\n\n\n \n\n\n\n PLoS genetics, 9: e1003087. 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Phenome-wide$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{PendergrassBrownGentryDudekEtAl2013,\n\tabstract = {Using a phenome-wide association study (PheWAS) approach, we comprehensively tested genetic variants for association with phenotypes available for 70,061 study participants in the {Population Architecture using Genomics and Epidemiology} (PAGE) network. Our aim was to better characterize the genetic architecture of complex traits and identify novel pleiotropic relationships. This PheWAS drew on five population-based studies representing four major racial/ethnic groups (European Americans (EA), African Americans (AA), Hispanics/Mexican-Americans, and Asian/Pacific Islanders) in PAGE, each site with measurements for multiple traits, associated laboratory measures, and intermediate biomarkers. A total of 83 single nucleotide polymorphisms (SNPs) identified by genome-wide association studies ({GWAS}) were genotyped across two or more PAGE study sites. Comprehensive tests of association, stratified by race/ethnicity, were performed, encompassing 4,706 phenotypes mapped to 105 phenotype-classes, and association results were compared across study sites. A total of 111 PheWAS results had significant associations for two or more PAGE study sites with consistent direction of effect with a significance threshold of p<0.01 for the same racial/ethnic group, {SNP}, and phenotype-class. Among results identified for SNPs previously associated with phenotypes such as lipid traits, type 2 diabetes, and body mass index, 52 replicated previously published genotype-phenotype associations, 26 represented phenotypes closely related to previously known genotype-phenotype associations, and 33 represented potentially novel genotype-phenotype associations with pleiotropic effects. The majority of the potentially novel results were for single PheWAS phenotype-classes, for example, for CDKN2A/B rs1333049 (previously associated with type 2 diabetes in EA) a PheWAS association was identified for hemoglobin levels in AA. Of note, however, GALNT2 rs2144300 (previously associated with high-density lipoprotein cholesterol levels in EA) had multiple potentially novel PheWAS associations, with hypertension related phenotypes in AA and with serum calcium levels and coronary artery disease phenotypes in EA. PheWAS identifies associations for hypothesis generation and exploration of the genetic architecture of complex traits.},\n\tauthor = {Pendergrass, Sarah A. and Brown-Gentry, Kristin and Dudek, Scott and Frase, Alex and Torstenson, Eric S. and Goodloe, Robert and Ambite, Jose Luis and Avery, Christy L. and Buyske, Steve and B{\\r u}{\\v z}kov{\\'a}, Petra and Deelman, Ewa and Fesinmeyer, Megan D. and Haiman, Christopher A. and Heiss, Gerardo and Hindorff, Lucia A. and Hsu, Chu-Nan and Jackson, Rebecca D. and Kooperberg, Charles and Le Marchand, Loic and Lin, Yi and Matise, Tara C. and Monroe, Kristine R. and Moreland, Larry and Park, Sungshim L. and Reiner, Alex and Wallace, Robert and Wilkens, Lynn R. and Crawford, Dana C. and Ritchie, Marylyn D.},\n\tchemicals = {Cyclin-Dependent Kinase Inhibitor p16, Hemoglobins, N-Acetylgalactosaminyltransferases, polypeptide N-acetylgalactosaminyltransferase, Calcium},\n\tcitation-subset = {IM},\n\tcompleted = {2013-05-30},\n\tcountry = {United States},\n\tdoi = {10.1371/journal.pgen.1003087},\n\tissn = {1553-7404},\n\tissn-linking = {1553-7390},\n\tissue = {1},\n\tjournal = {PLoS genetics},\n\tkeywords = {Calcium, blood; Coronary Artery Disease, genetics; Cyclin-Dependent Kinase Inhibitor p16, genetics; Ethnic Groups, genetics; Gene Regulatory Networks; Genetic Association Studies; Genetic Pleiotropy; Genetic Predisposition to Disease; Genome-Wide Association Study; Genomics; Hemoglobins, genetics; Humans; Hypertension, genetics; N-Acetylgalactosaminyltransferases; Phenotype; Polymorphism, Single Nucleotide, genetics},\n\tnlm-id = {101239074},\n\towner = {NLM},\n\tpages = {e1003087},\n\tpii = {PGENETICS-D-12-01377},\n\tpmc = {PMC3561060},\n\tpmid = {23382687},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/23382687/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Phenome-wide association study ({PheWAS}) for detection of pleiotropy within the {Population Architecture using Genomics and Epidemiology} ({PAGE}) Network.},\n\tvolume = {9},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/23382687/},\n\tbdsk-url-2 = {https://doi.org/10.1371/journal.pgen.1003087}}\n\n

\n\n\n

\n Using a phenome-wide association study (PheWAS) approach, we comprehensively tested genetic variants for association with phenotypes available for 70,061 study participants in the Population Architecture using Genomics and Epidemiology (PAGE) network. Our aim was to better characterize the genetic architecture of complex traits and identify novel pleiotropic relationships. This PheWAS drew on five population-based studies representing four major racial/ethnic groups (European Americans (EA), African Americans (AA), Hispanics/Mexican-Americans, and Asian/Pacific Islanders) in PAGE, each site with measurements for multiple traits, associated laboratory measures, and intermediate biomarkers. A total of 83 single nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) were genotyped across two or more PAGE study sites. Comprehensive tests of association, stratified by race/ethnicity, were performed, encompassing 4,706 phenotypes mapped to 105 phenotype-classes, and association results were compared across study sites. A total of 111 PheWAS results had significant associations for two or more PAGE study sites with consistent direction of effect with a significance threshold of p<0.01 for the same racial/ethnic group, SNP, and phenotype-class. Among results identified for SNPs previously associated with phenotypes such as lipid traits, type 2 diabetes, and body mass index, 52 replicated previously published genotype-phenotype associations, 26 represented phenotypes closely related to previously known genotype-phenotype associations, and 33 represented potentially novel genotype-phenotype associations with pleiotropic effects. The majority of the potentially novel results were for single PheWAS phenotype-classes, for example, for CDKN2A/B rs1333049 (previously associated with type 2 diabetes in EA) a PheWAS association was identified for hemoglobin levels in AA. Of note, however, GALNT2 rs2144300 (previously associated with high-density lipoprotein cholesterol levels in EA) had multiple potentially novel PheWAS associations, with hypertension related phenotypes in AA and with serum calcium levels and coronary artery disease phenotypes in EA. PheWAS identifies associations for hypothesis generation and exploration of the genetic architecture of complex traits.\n

\n\n\n

\n \n\n \n \n \n \n \n \n A systematic mapping approach of 16q12.2/FTO and BMI in more than 20,000 African Americans narrows in on the underlying functional variation: results from the Population Architecture using Genomics and Epidemiology (PAGE) study.\n \n \n \n \n\n\n \n Peters, U.; North, K. E.; Sethupathy, P.; Buyske, S.; Haessler, J.; Jiao, S.; Fesinmeyer, M. D.; Jackson, R. D.; Kuller, L. H.; Rajkovic, A.; Lim, U.; Cheng, I.; Schumacher, F.; Wilkens, L.; Li, R.; Monda, K.; Ehret, G.; Nguyen, K. H.; Cooper, R.; Lewis, C. E.; Leppert, M.; Irvin, M. R.; Gu, C. C.; Houston, D.; Buzkova, P.; Ritchie, M.; Matise, T. C.; Le Marchand, L.; Hindorff, L. A.; Crawford, D. C.; Haiman, C. A.; and Kooperberg, C.\n\n\n \n\n\n\n PLoS genetics, 9: e1003171. 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{PetersNorthSethupathyEtAl2013,\n\tabstract = {Genetic variants in intron 1 of the fat mass- and obesity-associated (FTO) gene have been consistently associated with body mass index (BMI) in Europeans. However, follow-up studies in African Americans (AA) have shown no support for some of the most consistently BMI-associated FTO index single nucleotide polymorphisms (SNPs). This is most likely explained by different race-specific linkage disequilibrium (LD) patterns and lower correlation overall in AA, which provides the opportunity to fine-map this region and narrow in on the functional variant. To comprehensively explore the 16q12.2/FTO locus and to search for second independent signals in the broader region, we fine-mapped a 646-kb region, encompassing the large FTO gene and the flanking gene RPGRIP1L by investigating a total of 3,756 variants (1,529 genotyped and 2,227 imputed variants) in 20,488 AAs across five studies. We observed associations between BMI and variants in the known FTO intron 1 locus: the {SNP} with the most significant p-value, rs56137030 (8.3 × 10(-6)) had not been highlighted in previous studies. While rs56137030was correlated at r(2)>0.5 with 103 SNPs in Europeans (including the {GWAS} index SNPs), this number was reduced to 28 SNPs in AA. Among rs56137030 and the 28 correlated SNPs, six were located within candidate intronic regulatory elements, including rs1421085, for which we predicted allele-specific binding affinity for the transcription factor CUX1, which has recently been implicated in the regulation of FTO. We did not find strong evidence for a second independent signal in the broader region. In summary, this large fine-mapping study in AA has substantially reduced the number of common alleles that are likely to be functional candidates of the known FTO locus. Importantly our study demonstrated that comprehensive fine-mapping in AA provides a powerful approach to narrow in on the functional candidate(s) underlying the initial {GWAS} findings in European populations.},\n\tauthor = {Peters, Ulrike and North, Kari E. and Sethupathy, Praveen and Buyske, Steve and Haessler, Jeff and Jiao, Shuo and Fesinmeyer, Megan D. and Jackson, Rebecca D. and Kuller, Lew H. and Rajkovic, Aleksandar and Lim, Unhee and Cheng, Iona and Schumacher, Fred and Wilkens, Lynne and Li, Rongling and Monda, Keri and Ehret, Georg and Nguyen, Khanh-Dung H. and Cooper, Richard and Lewis, Cora E. and Leppert, Mark and Irvin, Marguerite R. and Gu, C. Charles and Houston, Denise and Buzkova, Petra and Ritchie, Marylyn and Matise, Tara C. and Le Marchand, Loic and Hindorff, Lucia A. and Crawford, Dana C. and Haiman, Christopher A. and Kooperberg, Charles},\n\tchemicals = {Adaptor Proteins, Signal Transducing, Proteins, RPGRIP1L protein, human, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, FTO protein, human},\n\tcitation-subset = {IM},\n\tcompleted = {2013-05-30},\n\tcountry = {United States},\n\tdoi = {10.1371/journal.pgen.1003171},\n\tissn = {1553-7404},\n\tissn-linking = {1553-7390},\n\tissue = {1},\n\tjournal = {PLoS genetics},\n\tkeywords = {Adaptor Proteins, Signal Transducing, genetics; Adult; African Americans, genetics; Aged; Aged, 80 and over; Alleles; Alpha-Ketoglutarate-Dependent Dioxygenase FTO; Body Mass Index; Chromosome Mapping; Continental Population Groups, genetics; European Continental Ancestry Group, genetics; Female; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Linkage Disequilibrium; Male; Metagenomics; Middle Aged; Obesity, genetics; Proteins, genetics},\n\tnlm-id = {101239074},\n\towner = {NLM},\n\tpages = {e1003171},\n\tpii = {PGENETICS-D-12-02320},\n\tpmc = {PMC3547789},\n\tpmid = {23341774},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/23341774/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {A systematic mapping approach of 16q12.2/{FTO} and {BMI} in more than 20,000 {African Americans} narrows in on the underlying functional variation: results from the {Population Architecture using Genomics and Epidemiology} ({PAGE}) study.},\n\tvolume = {9},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/23341774/},\n\tbdsk-url-2 = {https://doi.org/10.1371/journal.pgen.1003171}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Investigation of gene-by-sex interactions for lipid traits in diverse populations from the Population Architecture using Genomics and Epidemiology study.\n \n \n \n \n\n\n \n Taylor, K. C.; Carty, C. L.; Dumitrescu, L.; B ̊u ̌zková, P.; Cole, S. A.; Hindorff, L.; Schumacher, F. R.; Wilkens, L. R.; Shohet, R. V.; Quibrera, P. M.; Johnson, K. C.; Henderson, B. E.; Haessler, J.; Franceschini, N.; Eaton, C. B.; Duggan, D. J.; Cochran, B.; Cheng, I.; Carlson, C. S.; Brown-Gentry, K.; Anderson, G.; Ambite, J. L.; Haiman, C.; Le Marchand, L.; Kooperberg, C.; Crawford, D. C.; Buyske, S.; North, K. E.; Fornage, M.; and Study, P. A. G. E.\n\n\n \n\n\n\n BMC genetics, 14: 33. May 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Investigation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{TaylorCartyDumitrescuEtAl2013,\n\tabstract = {High-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) levels are influenced by both genes and the environment. Genome-wide association studies ({GWAS}) have identified ~100 common genetic variants associated with HDL-C, LDL-C, and/or TG levels, mostly in populations of European descent, but little is known about the modifiers of these associations. Here, we investigated whether {GWAS}-identified SNPs for lipid traits exhibited heterogeneity by sex in the {Population Architecture using Genomics and Epidemiology} (PAGE) study. A sex-stratified meta-analysis was performed for 49 {GWAS}-identified SNPs for fasting HDL-C, LDL-C, and ln(TG) levels among adults self-identified as European American (25,013). Heterogeneity by sex was established when phet < 0.001. There was evidence for heterogeneity by sex for two SNPs for ln(TG) in the APOA1/C3/A4/A5/BUD13 gene cluster: rs28927680 (p(het) = 7.4 x 10(-7)) and rs3135506 (p(het) = 4.3 x 10(-4)one {SNP} in PLTP for HDL levels (rs7679; p(het) = 9.9 x 10(-4)), and one in HMGCR for LDL levels (rs12654264; p(het) = 3.1 x 10(-5)). We replicated heterogeneity by sex in five of seventeen loci previously reported by genome-wide studies (binomial p = 0.0009). We also present results for other racial/ethnic groups in the supplementary materials, to provide a resource for future meta-analyses. We provide further evidence for sex-specific effects of SNPs in the APOA1/C3/A4/A5/BUD13 gene cluster, PLTP, and HMGCR on fasting triglyceride levels in European Americans from the PAGE study. Our findings emphasize the need for considering context-specific effects when interpreting genetic associations emerging from {GWAS}, and also highlight the difficulties in replicating interaction effects across studies and across racial/ethnic groups.},\n\tauthor = {Taylor, Kira C. and Carty, Cara L. and Dumitrescu, Logan and B{\\r u}{\\v z}kov{\\'a}, Petra and Cole, Shelley A. and Hindorff, Lucia and Schumacher, Fred R. and Wilkens, Lynne R. and Shohet, Ralph V. and Quibrera, P. Miguel and Johnson, Karen C. and Henderson, Brian E. and Haessler, Jeff and Franceschini, Nora and Eaton, Charles B. and Duggan, David J. and Cochran, Barbara and Cheng, Iona and Carlson, Chris S. and Brown-Gentry, Kristin and Anderson, Garnet and Ambite, Jose Luis and Haiman, Christopher and Le Marchand, Lo{\\"\\i}c and Kooperberg, Charles and Crawford, Dana C. and Buyske, Steven and North, Kari E. and Fornage, Myriam and Study, P. A. G. E.},\n\tchemicals = {Lipids},\n\tcitation-subset = {IM},\n\tcompleted = {2013-10-17},\n\tcountry = {England},\n\tdoi = {10.1186/1471-2156-14-33},\n\tissn = {1471-2156},\n\tissn-linking = {1471-2156},\n\tjournal = {BMC genetics},\n\tkeywords = {Female; Genetic Heterogeneity; Genome, Human; Genome-Wide Association Study; Humans; Lipids, genetics; Male; Polymorphism, Single Nucleotide; Population Groups, genetics},\n\tmonth = may,\n\tnlm-id = {100966978},\n\towner = {NLM},\n\tpages = {33},\n\tpii = {1471-2156-14-33},\n\tpmc = {PMC3669109},\n\tpmid = {23634756},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/23634756/},\n\n\tpubmodel = {Electronic},\n\tpubstate = {epublish},\n\trevised = {2018-11-13},\n\ttitle = {Investigation of gene-by-sex interactions for lipid traits in diverse populations from the {Population Architecture using Genomics and Epidemiology} study.},\n\tvolume = {14},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/23634756/},\n\tbdsk-url-2 = {https://doi.org/10.1186/1471-2156-14-33}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Genetic variation and reproductive timing: African American women from the Population Architecture using Genomics and Epidemiology (PAGE) Study.\n \n \n \n \n\n\n \n Spencer, K. L.; Malinowski, J.; Carty, C. L.; Franceschini, N.; Fernández-Rhodes, L.; Young, A.; Cheng, I.; Ritchie, M. D.; Haiman, C. A.; Wilkens, L.; Chunyuanwu; Matise, T. C.; Carlson, C. S.; Brennan, K.; Park, A.; Rajkovic, A.; Hindorff, L. A.; Buyske, S.; and Crawford, D. C.\n\n\n \n\n\n\n PloS one, 8: e55258. 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Genetic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{SpencerMalinowskiCartyEtAl2013,\n\tabstract = {Age at menarche (AM) and age at natural menopause (ANM) define the boundaries of the reproductive lifespan in women. Their timing is associated with various diseases, including cancer and cardiovascular disease. Genome-wide association studies have identified several genetic variants associated with either AM or ANM in populations of largely European or Asian descent women. The extent to which these associations generalize to diverse populations remains unknown. Therefore, we sought to replicate previously reported AM and ANM findings and to identify novel AM and ANM variants using the Metabochip (n = 161,098 SNPs) in 4,159 and 1,860 African American women, respectively, in the Women's Health Initiative (WHI) and Atherosclerosis Risk in Communities (ARIC) studies, as part of the {Population Architecture using Genomics and Epidemiology} (PAGE) Study. We replicated or generalized one previously identified variant for AM, rs1361108/CENPW, and two variants for ANM, rs897798/BRSK1 and rs769450/APOE, to our African American cohort. Overall, generalization of the majority of previously-identified variants for AM and ANM, including LIN28B and MCM8, was not observed in this African American sample. We identified three novel loci associated with ANM that reached significance after multiple testing correction (LDLR rs189596789, p = 5×10⁻⁰⁸; KCNQ1 rs79972789, p = 1.9×10⁻⁰⁷; COL4A3BP rs181686584, p = 2.9×10⁻⁰⁷). Our most significant AM association was upstream of RSF1, a gene implicated in ovarian and breast cancers (rs11604207, p = 1.6×10⁻⁰⁶). While most associations were identified in either AM or ANM, we did identify genes suggestively associated with both: PHACTR1 and ARHGAP42. The lack of generalization coupled with the potentially novel associations identified here emphasize the need for additional genetic discovery efforts for AM and ANM in diverse populations.},\n\tauthor = {Spencer, Kylee L. and Malinowski, Jennifer and Carty, Cara L. and Franceschini, Nora and Fern{\\'a}ndez-Rhodes, Lindsay and Young, Alicia and Cheng, Iona and Ritchie, Marylyn D. and Haiman, Christopher A. and Wilkens, Lynne and Chunyuanwu and Matise, Tara C. and Carlson, Christopher S. and Brennan, Kathleen and Park, Amy and Rajkovic, Aleksandar and Hindorff, Lucia A. and Buyske, Steven and Crawford, Dana C.},\n\tcitation-subset = {IM},\n\tcompleted = {2013-08-08},\n\tcountry = {United States},\n\tdoi = {10.1371/journal.pone.0055258},\n\tissn = {1932-6203},\n\tissn-linking = {1932-6203},\n\tissue = {2},\n\tjournal = {PloS one},\n\tkeywords = {Adolescent; African Americans, genetics, statistics & numerical data; Epidemiologic Studies; Female; Genetic Variation; Genomics; Humans; Menarche, ethnology, genetics, physiology; Menopause, ethnology, genetics, physiology; Middle Aged; Reproduction, genetics},\n\tnlm-id = {101285081},\n\towner = {NLM},\n\tpages = {e55258},\n\tpii = {PONE-D-12-20445},\n\tpmc = {PMC3570525},\n\tpmid = {23424626},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/23424626/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Genetic variation and reproductive timing: {African American} women from the {Population Architecture using Genomics and Epidemiology} ({PAGE}) Study.},\n\tvolume = {8},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/23424626/},\n\tbdsk-url-2 = {https://doi.org/10.1371/journal.pone.0055258}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Lack of associations of ten candidate coronary heart disease risk genetic variants and subclinical atherosclerosis in four US populations: the Population Architecture using Genomics and Epidemiology (PAGE) study.\n \n \n \n \n\n\n \n Zhang, L.; Buzkova, P.; Wassel, C. L.; Roman, M. J.; North, K. E.; Crawford, D. C.; Boston, J.; Brown-Gentry, K. D.; Cole, S. A.; Deelman, E.; Goodloe, R.; Wilson, S.; Heiss, G.; Jenny, N. S.; Jorgensen, N. W.; Matise, T. C.; McClellan, B. E.; Nato, A. Q.; Ritchie, M. D.; Franceschini, N.; and Kao, W. H. L.\n\n\n \n\n\n\n Atherosclerosis, 228: 390–399. June 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Lack$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{ZhangBuzkovaWasselEtAl2013,\n\tabstract = {A number of genetic variants have been discovered by recent genome-wide association studies for their associations with clinical coronary heart disease (CHD). However, it is unclear whether these variants are also associated with the development of CHD as measured by subclinical atherosclerosis phenotypes, ankle brachial index (ABI), carotid artery intima-media thickness (cIMT) and carotid plaque. Ten CHD risk single nucleotide polymorphisms (SNPs) were genotyped in individuals of European American (EA), African American (AA), American Indian (AI), and Mexican American (MA) ancestry in the {Population Architecture using Genomics and Epidemiology} (PAGE) study. In each individual study, we performed linear or logistic regression to examine population-specific associations between SNPs and ABI, common and internal cIMT, and plaque. The results from individual studies were meta-analyzed using a fixed effect inverse variance weighted model. None of the ten SNPs was significantly associated with ABI and common or internal cIMT, after Bonferroni correction. In the sample of 13,337 EA, 3809 AA, and 5353 AI individuals with carotid plaque measurement, the GCKR {SNP} rs780094 was significantly associated with the presence of plaque in AI only (OR = 1.32, 95% confidence interval: 1.17, 1.49, P = 1.08 × 10(-5)), but not in the other populations (P = 0.90 in EA and P = 0.99 in AA). A 9p21 region {SNP}, rs1333049, was nominally associated with plaque in EA (OR = 1.07, P = 0.02) and in AI (OR = 1.10, P = 0.05). We identified a significant association between rs780094 and plaque in AI populations, which needs to be replicated in future studies. There was little evidence that the index CHD risk variants identified through genome-wide association studies in EA influence the development of CHD through subclinical atherosclerosis as assessed by cIMT and ABI across ancestries.},\n\tauthor = {Zhang, Lili and Buzkova, Petra and Wassel, Christina L. and Roman, Mary J. and North, Kari E. and Crawford, Dana C. and Boston, Jonathan and Brown-Gentry, Kristin D. and Cole, Shelley A. and Deelman, Ewa and Goodloe, Robert and Wilson, Sarah and Heiss, Gerardo and Jenny, Nancy S. and Jorgensen, Neal W. and Matise, Tara C. and McClellan, Bob E. and Nato, Alejandro Q. and Ritchie, Marylyn D. and Franceschini, Nora and Kao, W. H. Linda},\n\tcitation-subset = {IM},\n\tcompleted = {2013-10-21},\n\tcountry = {Ireland},\n\tdoi = {10.1016/j.atherosclerosis.2013.02.038},\n\tissn = {1879-1484},\n\tissn-linking = {0021-9150},\n\tissue = {2},\n\tjournal = {Atherosclerosis},\n\tkeywords = {African Americans, genetics; Aged; Ankle Brachial Index; Asymptomatic Diseases; Carotid Artery Diseases, diagnosis, epidemiology, ethnology, genetics; Carotid Intima-Media Thickness; Coronary Disease, diagnosis, epidemiology, ethnology, genetics; European Continental Ancestry Group, genetics; Female; Gene Frequency; Genetic Association Studies; Genetic Predisposition to Disease; Humans; Indians, North American, genetics; Linear Models; Logistic Models; Male; Mexican Americans, genetics; Middle Aged; Odds Ratio; Phenotype; Polymorphism, Single Nucleotide; Predictive Value of Tests; Risk Assessment; Risk Factors; United States, epidemiology},\n\tmid = {NIHMS470261},\n\tmonth = jun,\n\tnlm-id = {0242543},\n\towner = {NLM},\n\tpages = {390--399},\n\tpii = {S0021-9150(13)00174-3},\n\tpmc = {PMC3717342},\n\tpmid = {23587283},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/23587283/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2019-12-20},\n\ttitle = {Lack of associations of ten candidate coronary heart disease risk genetic variants and subclinical atherosclerosis in four {US} populations: the {Population Architecture using Genomics and Epidemiology} ({PAGE}) study.},\n\tvolume = {228},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/23587283/},\n\tbdsk-url-2 = {https://doi.org/10.1016/j.atherosclerosis.2013.02.038}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Trans-ethnic fine-mapping of lipid loci identifies population-specific signals and allelic heterogeneity that increases the trait variance explained.\n \n \n \n \n\n\n \n Wu, Y.; Waite, L. L.; Jackson, A. U.; Sheu, W. H.; Buyske, S.; Absher, D.; Arnett, D. K.; Boerwinkle, E.; Bonnycastle, L. L.; Carty, C. L.; Cheng, I.; Cochran, B.; Croteau-Chonka, D. C.; Dumitrescu, L.; Eaton, C. B.; Franceschini, N.; Guo, X.; Henderson, B. E.; Hindorff, L. A.; Kim, E.; Kinnunen, L.; Komulainen, P.; Lee, W.; Le Marchand, L.; Lin, Y.; Lindström, J.; Lingaas-Holmen, O.; Mitchell, S. L.; Narisu, N.; Robinson, J. G.; Schumacher, F.; Stan ̌cáková, A.; Sundvall, J.; Sung, Y.; Swift, A. J.; Wang, W.; Wilkens, L.; Wilsgaard, T.; Young, A. M.; Adair, L. S.; Ballantyne, C. M.; B ̊u ̌zková, P.; Chakravarti, A.; Collins, F. S.; Duggan, D.; Feranil, A. B.; Ho, L.; Hung, Y.; Hunt, S. C.; Hveem, K.; Juang, J. J.; Kesäniemi, A. Y.; Kuusisto, J.; Laakso, M.; Lakka, T. A.; Lee, I.; Leppert, M. F.; Matise, T. C.; Moilanen, L.; Njølstad, I.; Peters, U.; Quertermous, T.; Rauramaa, R.; Rotter, J. I.; Saramies, J.; Tuomilehto, J.; Uusitupa, M.; Wang, T.; Boehnke, M.; Haiman, C. A.; Chen, Y. I.; Kooperberg, C.; Assimes, T. L.; Crawford, D. C.; Hsiung, C. A.; North, K. E.; and Mohlke, K. L.\n\n\n \n\n\n\n PLoS genetics, 9: e1003379. March 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Trans-ethnic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{WuWaiteJacksonEtAl2013,\n\tabstract = {Genome-wide association studies ({GWAS}) have identified ~100 loci associated with blood lipid levels, but much of the trait heritability remains unexplained, and at most loci the identities of the trait-influencing variants remain unknown. We conducted a trans-ethnic fine-mapping study at 18, 22, and 18 {GWAS} loci on the Metabochip for their association with triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C), respectively, in individuals of African American (n = 6,832), East Asian (n = 9,449), and European (n = 10,829) ancestry. We aimed to identify the variants with strongest association at each locus, identify additional and population-specific signals, refine association signals, and assess the relative significance of previously described functional variants. Among the 58 loci, 33 exhibited evidence of association at P<1 × 10(-4) in at least one ancestry group. Sequential conditional analyses revealed that ten, nine, and four loci in African Americans, Europeans, and East Asians, respectively, exhibited two or more signals. At these loci, accounting for all signals led to a 1.3- to 1.8-fold increase in the explained phenotypic variance compared to the strongest signals. Distinct signals across ancestry groups were identified at PCSK9 and APOA5. Trans-ethnic analyses narrowed the signals to smaller sets of variants at GCKR, PPP1R3B, ABO, LCAT, and ABCA1. Of 27 variants reported previously to have functional effects, 74% exhibited the strongest association at the respective signal. In conclusion, trans-ethnic high-density genotyping and analysis confirm the presence of allelic heterogeneity, allow the identification of population-specific variants, and limit the number of candidate SNPs for functional studies.},\n\tauthor = {Wu, Ying and Waite, Lindsay L. and Jackson, Anne U. and Sheu, Wayne H.-H. and Buyske, Steven and Absher, Devin and Arnett, Donna K. and Boerwinkle, Eric and Bonnycastle, Lori L. and Carty, Cara L. and Cheng, Iona and Cochran, Barbara and Croteau-Chonka, Damien C. and Dumitrescu, Logan and Eaton, Charles B. and Franceschini, Nora and Guo, Xiuqing and Henderson, Brian E. and Hindorff, Lucia A. and Kim, Eric and Kinnunen, Leena and Komulainen, Pirjo and Lee, Wen-Jane and Le Marchand, Loic and Lin, Yi and Lindstr{\\"o}m, Jaana and Lingaas-Holmen, Oddgeir and Mitchell, Sabrina L. and Narisu, Narisu and Robinson, Jennifer G. and Schumacher, Fred and Stan{\\v c}{\\'a}kov{\\'a}, Alena and Sundvall, Jouko and Sung, Yun-Ju and Swift, Amy J. and Wang, Wen-Chang and Wilkens, Lynne and Wilsgaard, Tom and Young, Alicia M. and Adair, Linda S. and Ballantyne, Christie M. and B{\\r u}{\\v z}kov{\\'a}, Petra and Chakravarti, Aravinda and Collins, Francis S. and Duggan, David and Feranil, Alan B. and Ho, Low-Tone and Hung, Yi-Jen and Hunt, Steven C. and Hveem, Kristian and Juang, Jyh-Ming J. and Kes{\\"a}niemi, Antero Y. and Kuusisto, Johanna and Laakso, Markku and Lakka, Timo A. and Lee, I.-Te and Leppert, Mark F. and Matise, Tara C. and Moilanen, Leena and Nj{\\o}lstad, Inger and Peters, Ulrike and Quertermous, Thomas and Rauramaa, Rainer and Rotter, Jerome I. and Saramies, Jouko and Tuomilehto, Jaakko and Uusitupa, Matti and Wang, Tzung-Dau and Boehnke, Michael and Haiman, Christopher A. and Chen, Yii-Der I. and Kooperberg, Charles and Assimes, Themistocles L. and Crawford, Dana C. and Hsiung, Chao A. and North, Kari E. and Mohlke, Karen L.},\n\tchemicals = {APOA5 protein, human, Apolipoprotein A-V, Apolipoproteins A, Cholesterol, HDL, Cholesterol, LDL, Lipoproteins, HDL, Lipoproteins, LDL, Triglycerides, PCSK9 protein, human, Proprotein Convertase 9, Proprotein Convertases, Serine Endopeptidases},\n\tcitation-subset = {IM},\n\tcompleted = {2013-06-25},\n\tcountry = {United States},\n\tdoi = {10.1371/journal.pgen.1003379},\n\tissn = {1553-7404},\n\tissn-linking = {1553-7390},\n\tissue = {3},\n\tjournal = {PLoS genetics},\n\tkeywords = {African Americans, genetics; Apolipoprotein A-V; Apolipoproteins A, genetics; Cholesterol, HDL, blood, genetics; Cholesterol, LDL, blood, genetics; European Continental Ancestry Group, genetics; Genome-Wide Association Study; Humans; Lipoproteins, HDL, blood, genetics; Lipoproteins, LDL, blood, genetics; Proprotein Convertase 9; Proprotein Convertases, genetics; Serine Endopeptidases, genetics; Triglycerides, blood, genetics},\n\tmonth = mar,\n\tnlm-id = {101239074},\n\towner = {NLM},\n\tpages = {e1003379},\n\tpii = {PGENETICS-D-12-01954},\n\tpmc = {PMC3605054},\n\tpmid = {23555291},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/23555291/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2019-11-08},\n\ttitle = {Trans-ethnic fine-mapping of lipid loci identifies population-specific signals and allelic heterogeneity that increases the trait variance explained.},\n\tvolume = {9},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/23555291/},\n\tbdsk-url-2 = {https://doi.org/10.1371/journal.pgen.1003379}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n ICD-9 tobacco use codes are effective identifiers of smoking status.\n \n \n \n \n\n\n \n Wiley, L. K.; Shah, A.; Xu, H.; and Bush, W. S.\n\n\n \n\n\n\n Journal of the American Medical Informatics Association : JAMIA, 20: 652–658. 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"ICD-9$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n

@article{WileyShahXuEtAl2013,\n\tabstract = {To evaluate the validity of, characterize the usage of, and propose potential research applications for International Classification of Diseases, Ninth Revision (ICD-9) tobacco codes in clinical populations. Using data on cancer cases and cancer-free controls from Vanderbilt's biorepository, BioVU, we evaluated the utility of ICD-9 tobacco use codes to identify ever-smokers in general and high smoking prevalence (lung cancer) clinic populations. We assessed potential biases in documentation, and performed temporal analysis relating transitions between smoking codes to smoking cessation attempts. We also examined the suitability of these codes for use in genetic association analyses. ICD-9 tobacco use codes can identify smokers in a general clinic population (specificity of 1, sensitivity of 0.32), and there is little evidence of documentation bias. Frequency of code transitions between 'current' and 'former' tobacco use was significantly correlated with initial success at smoking cessation (p<0.0001). Finally, code-based smoking status assignment is a comparable covariate to text-based smoking status for genetic association studies. Our results support the use of ICD-9 tobacco use codes for identifying smokers in a clinical population. Furthermore, with some limitations, these codes are suitable for adjustment of smoking status in genetic studies utilizing electronic health records. Researchers should not be deterred by the unavailability of full-text records to determine smoking status if they have ICD-9 code histories.},\n\tauthor = {Wiley, Laura K. and Shah, Anushi and Xu, Hua and Bush, William S.},\n\tcitation-subset = {IM},\n\tcompleted = {2013-09-03},\n\tcountry = {England},\n\tdoi = {10.1136/amiajnl-2012-001557},\n\tissn = {1527-974X},\n\tissn-linking = {1067-5027},\n\tissue = {4},\n\tjournal = {Journal of the American Medical Informatics Association : JAMIA},\n\tkeywords = {Clinical Coding; Humans; International Classification of Diseases; Sensitivity and Specificity; Smoking; Electronic Health Records; International Classification of Diseases; Phenotype},\n\tnlm-id = {9430800},\n\towner = {NLM},\n\tpages = {652--658},\n\tpii = {amiajnl-2012-001557},\n\tpmc = {PMC3721171},\n\tpmid = {23396545},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/23396545/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2019-12-10},\n\ttitle = {{ICD-9} tobacco use codes are effective identifiers of smoking status.},\n\tvolume = {20},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/23396545/},\n\tbdsk-url-2 = {https://doi.org/10.1136/amiajnl-2012-001557}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Fine Mapping and Identification of BMI Loci in African Americans.\n \n \n \n \n\n\n \n Gong, J.; Schumacher, F.; Lim, U.; Hindorff, L. A.; Haessler, J.; Buyske, S.; Carlson, C. S.; Rosse, S.; B ̊u ̌zková, P.; Fornage, M.; Gross, M.; Pankratz, N.; Pankow, J. S.; Schreiner, P. J.; Cooper, R.; Ehret, G.; Gu, C. C.; Houston, D.; Irvin, M. R.; Jackson, R.; Kuller, L.; Henderson, B.; Cheng, I.; Wilkens, L.; Leppert, M.; Lewis, C. E.; Li, R.; Nguyen, K. H.; Goodloe, R.; Farber-Eger, E.; Boston, J.; Dilks, H. H.; Ritchie, M. D.; Fowke, J.; Pooler, L.; Graff, M.; Fernandez-Rhodes, L.; Cochrane, B.; Boerwinkle, E.; Kooperberg, C.; Matise, T. C.; Le Marchand, L.; Crawford, D. C.; Haiman, C. A.; North, K. E.; and Peters, U.\n\n\n \n\n\n\n American journal of human genetics, 93: 661–671. October 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Fine$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{GongSchumacherLimEtAl2013a,\n\tabstract = {Genome-wide association studies (GWASs) primarily performed in European-ancestry (EA) populations have identified numerous loci associated with body mass index (BMI). However, it is still unclear whether these {GWAS} loci can be generalized to other ethnic groups, such as African Americans (AAs). Furthermore, the putative functional variant or variants in these loci mostly remain under investigation. The overall lower linkage disequilibrium in AA compared to EA populations provides the opportunity to narrow in or fine-map these BMI-related loci. Therefore, we used the Metabochip to densely genotype and evaluate 21 BMI {GWAS} loci identified in EA studies in 29,151 AAs from the {Population Architecture using Genomics and Epidemiology} (PAGE) study. Eight of the 21 loci (SEC16B, TMEM18, ETV5, GNPDA2, TFAP2B, BDNF, FTO, and MC4R) were found to be associated with BMI in AAs at 5.8 × 10(-5). Within seven out of these eight loci, we found that, on average, a substantially smaller number of variants was correlated (r(2) > 0.5) with the most significant {SNP} in AA than in EA populations (16 versus 55). Conditional analyses revealed GNPDA2 harboring a potential additional independent signal. Moreover, Metabochip-wide discovery analyses revealed two BMI-related loci, BRE (rs116612809, p = 3.6 × 10(-8)) and DHX34 (rs4802349, p = 1.2 × 10(-7)), which were significant when adjustment was made for the total number of SNPs tested across the chip. These results demonstrate that fine mapping in AAs is a powerful approach for both narrowing in on the underlying causal variants in known loci and discovering BMI-related loci.},\n\tauthor = {Gong, Jian and Schumacher, Fredrick and Lim, Unhee and Hindorff, Lucia A. and Haessler, Jeff and Buyske, Steven and Carlson, Christopher S. and Rosse, Stephanie and B{\\r u}{\\v z}kov{\\'a}, Petra and Fornage, Myriam and Gross, Myron and Pankratz, Nathan and Pankow, James S. and Schreiner, Pamela J. and Cooper, Richard and Ehret, Georg and Gu, C. Charles and Houston, Denise and Irvin, Marguerite R. and Jackson, Rebecca and Kuller, Lew and Henderson, Brian and Cheng, Iona and Wilkens, Lynne and Leppert, Mark and Lewis, Cora E. and Li, Rongling and Nguyen, Khanh-Dung H. and Goodloe, Robert and Farber-Eger, Eric and Boston, Jonathan and Dilks, Holli H. and Ritchie, Marylyn D. and Fowke, Jay and Pooler, Loreall and Graff, Misa and Fernandez-Rhodes, Lindsay and Cochrane, Barbara and Boerwinkle, Eric and Kooperberg, Charles and Matise, Tara C. and Le Marchand, Loic and Crawford, Dana C. and Haiman, Christopher A. and North, Kari E. and Peters, Ulrike},\n\tcitation-subset = {IM},\n\tcompleted = {2014-02-19},\n\tcountry = {United States},\n\tdoi = {10.1016/j.ajhg.2013.08.012},\n\tissn = {1537-6605},\n\tissn-linking = {0002-9297},\n\tissue = {4},\n\tjournal = {American journal of human genetics},\n\tkeywords = {Adult; African Americans, genetics; Aged; Aged, 80 and over; Body Mass Index; Female; Genetic Loci; Genetic Predisposition to Disease; Genome, Human; Genome-Wide Association Study, methods; Genotype; Humans; Linkage Disequilibrium; Male; Middle Aged; Obesity, ethnology, genetics; Polymorphism, Single Nucleotide; Young Adult},\n\tmonth = oct,\n\tnlm-id = {0370475},\n\towner = {NLM},\n\tpages = {661--671},\n\tpii = {S0002-9297(13)00387-X},\n\tpmc = {PMC3791273},\n\tpmid = {24094743},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/24094743/},\n\n\tpubmodel = {Print},\n\tpubstate = {ppublish},\n\trevised = {2020-08-24},\n\ttitle = {Fine Mapping and Identification of {BMI} Loci in {African Americans}.},\n\tvolume = {93},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/24094743/},\n\tbdsk-url-2 = {https://doi.org/10.1016/j.ajhg.2013.08.012}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Genetic risk factors for BMI and obesity in an ethnically diverse population: results from the Population Architecture using Genomics and Epidemiology (PAGE) study.\n \n \n \n \n\n\n \n Fesinmeyer, M. D.; North, K. E.; Ritchie, M. D.; Lim, U.; Franceschini, N.; Wilkens, L. R.; Gross, M. D.; B ̊u ̌zková, P.; Glenn, K.; Quibrera, P. M.; Fernández-Rhodes, L.; Li, Q.; Fowke, J. H.; Li, R.; Carlson, C. S.; Prentice, R. L.; Kuller, L. H.; Manson, J. E.; Matise, T. C.; Cole, S. A.; Chen, C. T. L.; Howard, B. V.; Kolonel, L. N.; Henderson, B. E.; Monroe, K. R.; Crawford, D. C.; Hindorff, L. A.; Buyske, S.; Haiman, C. A.; Le Marchand, L.; and Peters, U.\n\n\n \n\n\n\n Obesity (Silver Spring, Md.), 21: 835–846. April 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Genetic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{FesinmeyerNorthRitchieEtAl2013,\n\tabstract = {Several genome-wide association studies ({GWAS}) have demonstrated that common genetic variants contribute to obesity. However, studies of this complex trait have focused on ancestrally European populations, despite the high prevalence of obesity in some minority groups. As part of the "{Population Architecture using Genomics and Epidemiology} (PAGE)" Consortium, we investigated the association between 13 {GWAS}-identified single-nucleotide polymorphisms (SNPs) and BMI and obesity in 69,775 subjects, including 6,149 American Indians, 15,415 African-Americans, 2,438 East Asians, 7,346 Hispanics, 604 Pacific Islanders, and 37,823 European Americans. For the BMI-increasing allele of each {SNP}, we calculated β coefficients using linear regression (for BMI) and risk estimates using logistic regression (for obesity defined as BMI ≥ 30) followed by fixed-effects meta-analysis to combine results across PAGE sites. Analyses stratified by racial/ethnic group assumed an additive genetic model and were adjusted for age, sex, and current smoking. We defined "replicating SNPs" (in European Americans) and "generalizing SNPs" (in other racial/ethnic groups) as those associated with an allele frequency-specific increase in BMI. By this definition, we replicated 9/13 {SNP} associations (5 out of 8 loci) in European Americans. We also generalized 8/13 {SNP} associations (5/8 loci) in East Asians, 7/13 (5/8 loci) in African Americans, 6/13 (4/8 loci) in Hispanics, 5/8 in Pacific Islanders (5/8 loci), and 5/9 (4/8 loci) in American Indians. Linkage disequilibrium patterns suggest that tagSNPs selected for European Americans may not adequately tag causal variants in other ancestry groups. Accordingly, fine-mapping in large samples is needed to comprehensively explore these loci in diverse populations.},\n\tauthor = {Fesinmeyer, Megan D. and North, Kari E. and Ritchie, Marylyn D. and Lim, Unhee and Franceschini, Nora and Wilkens, Lynne R. and Gross, Myron D. and B{\\r u}{\\v z}kov{\\'a}, Petra and Glenn, Kimberly and Quibrera, P. Miguel and Fern{\\'a}ndez-Rhodes, Lindsay and Li, Qiong and Fowke, Jay H. and Li, Rongling and Carlson, Christopher S. and Prentice, Ross L. and Kuller, Lewis H. and Manson, Joann E. and Matise, Tara C. and Cole, Shelley A. and Chen, Christina T. L. and Howard, Barbara V. and Kolonel, Laurence N. and Henderson, Brian E. and Monroe, Kristine R. and Crawford, Dana C. and Hindorff, Lucia A. and Buyske, Steven and Haiman, Christopher A. and Le Marchand, Loic and Peters, Ulrike},\n\tcitation-subset = {IM},\n\tcompleted = {2014-01-17},\n\tcountry = {United States},\n\tdoi = {10.1002/oby.20268},\n\tissn = {1930-739X},\n\tissn-linking = {1930-7381},\n\tissue = {4},\n\tjournal = {Obesity (Silver Spring, Md.)},\n\tkeywords = {Alleles; Body Mass Index; Ethnic Groups, genetics; Gene Frequency; Genetic Loci; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Linkage Disequilibrium; Metagenomics, methods; Obesity, epidemiology, genetics; Phenotype; Polymorphism, Single Nucleotide; Risk Factors},\n\tmid = {NIHMS384727},\n\tmonth = apr,\n\tnlm-id = {101264860},\n\towner = {NLM},\n\tpages = {835--846},\n\tpmc = {PMC3482415},\n\tpmid = {23712987},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/23712987/},\n\n\tpubmodel = {Print},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Genetic risk factors for {BMI} and obesity in an ethnically diverse population: results from the {Population Architecture using Genomics and Epidemiology} ({PAGE}) study.},\n\tvolume = {21},\n\tyear = {2013},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/23712987/},\n\tbdsk-url-2 = {https://doi.org/10.1002/oby.20268}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Association of functional polymorphism rs2231142 (Q141K) in the ABCG2 gene with serum uric acid and gout in 4 US populations: the PAGE Study.\n \n \n \n \n\n\n \n Zhang, L.; Spencer, K. L.; Voruganti, V. S.; Jorgensen, N. W.; Fornage, M.; Best, L. G.; Brown-Gentry, K. D.; Cole, S. A.; Crawford, D. C.; Deelman, E.; Franceschini, N.; Gaffo, A. L.; Glenn, K. R.; Heiss, G.; Jenny, N. S.; Kottgen, A.; Li, Q.; Liu, K.; Matise, T. C.; North, K. E.; Umans, J. G.; and Kao, W. H. L.\n\n\n \n\n\n\n American journal of epidemiology, 177: 923–932. May 2013.\n \n\n\n\n
\n\n\n\n \n \n $\"Association$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{ZhangSpencerVorugantiEtAl2013,\n\tabstract = {A loss-of-function mutation (Q141K, rs2231142) in the ATP-binding cassette, subfamily G, member 2 gene (ABCG2) has been shown to be associated with serum uric acid levels and gout in Asians, Europeans, and European and African Americans; however, less is known about these associations in other populations. Rs2231142 was genotyped in 22,734 European Americans, 9,720 African Americans, 3,849 Mexican Americans, and 3,550 American Indians in the {Population Architecture using Genomics and Epidemiology} (PAGE) Study (2008-2012). Rs2231142 was significantly associated with serum uric acid levels (P = 2.37 × 10(-67), P = 3.98 × 10(-5), P = 6.97 × 10(-9), and P = 5.33 × 10(-4) in European Americans, African Americans, Mexican Americans, and American Indians, respectively) and gout (P = 2.83 × 10(-10), P = 0.01, and P = 0.01 in European Americans, African Americans, and Mexican Americans, respectively). Overall, the T allele was associated with a 0.24-mg/dL increase in serum uric acid level (P = 1.37 × 10(-80)) and a 1.75-fold increase in the odds of gout (P = 1.09 × 10(-12)). The association between rs2231142 and serum uric acid was significantly stronger in men, postmenopausal women, and hormone therapy users compared with their counterparts. The association with gout was also significantly stronger in men than in women. These results highlight a possible role of sex hormones in the regulation of ABCG2 urate transporter and its potential implications for the prevention, diagnosis, and treatment of hyperuricemia and gout.},\n\tauthor = {Zhang, Lili and Spencer, Kylee L. and Voruganti, V. Saroja and Jorgensen, Neal W. and Fornage, Myriam and Best, Lyle G. and Brown-Gentry, Kristin D. and Cole, Shelley A. and Crawford, Dana C. and Deelman, Ewa and Franceschini, Nora and Gaffo, Angelo L. and Glenn, Kimberly R. and Heiss, Gerardo and Jenny, Nancy S. and Kottgen, Anna and Li, Qiong and Liu, Kiang and Matise, Tara C. and North, Kari E. and Umans, Jason G. and Kao, W. H. Linda},\n\tchemicals = {ABCG2 protein, human, ATP Binding Cassette Transporter, Subfamily G, Member 2, ATP-Binding Cassette Transporters, Neoplasm Proteins, Uric Acid},\n\tcitation-subset = {IM},\n\tcompleted = {2014-05-05},\n\tcountry = {United States},\n\tdoi = {10.1093/aje/kws330},\n\tissn = {1476-6256},\n\tissn-linking = {0002-9262},\n\tissue = {9},\n\tjournal = {American journal of epidemiology},\n\tkeywords = {ATP Binding Cassette Transporter, Subfamily G, Member 2; ATP-Binding Cassette Transporters, genetics; Adult; African Americans, genetics; Age Distribution; Comorbidity; European Continental Ancestry Group, genetics; Female; Genetic Predisposition to Disease; Genetics, Population; Genome-Wide Association Study; Gout, blood, ethnology, genetics; Hormone Replacement Therapy, statistics & numerical data; Humans; Indians, North American, genetics; Male; Mexican Americans, genetics; Middle Aged; Neoplasm Proteins, genetics; Polymorphism, Genetic; Postmenopause; Sex Distribution; United States; Uric Acid, blood; ABCG2 protein, human; genetic association studies; gout; meta-analysis; polymorphism, single nucleotide; urate transporter; uric acid},\n\tmonth = may,\n\tnlm-id = {7910653},\n\towner = {NLM},\n\tpages = {923--932},\n\tpii = {kws330},\n\tpmc = {PMC4023295},\n\tpmid = {23552988},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/23552988/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-12-02},\n\ttitle = {Association of functional polymorphism rs2231142 ({Q141K}) in the {ABCG2} gene with serum uric acid and gout in 4 {US} populations: the {PAGE} Study.},\n\tvolume = {177},\n\tyear = {2013},\n\tbdsk-url-1 = {https://doi.org/10.1093/aje/kws330}}\n\n

\n\n\n\n\n\n

\n\n

\n \n 2012\n \n \n (10)\n \n \n

\n \n \n

\n \n\n \n \n \n \n \n \n Fine-mapping and initial characterization of QT interval loci in African Americans.\n \n \n \n \n\n\n \n Avery, C. L.; Sethupathy, P.; Buyske, S.; He, Q.; Lin, D.; Arking, D. E.; Carty, C. L.; Duggan, D.; Fesinmeyer, M. D.; Hindorff, L. A.; Jeff, J. M.; Klein, L.; Patton, K. K.; Peters, U.; Shohet, R. V.; Sotoodehnia, N.; Young, A. M.; Kooperberg, C.; Haiman, C. A.; Mohlke, K. L.; Whitsel, E. A.; and North, K. E.\n\n\n \n\n\n\n PLoS genetics, 8: e1002870. 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Fine-mapping$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{AverySethupathyBuyskeEtAl2012,\n\tabstract = {The {QT} interval ({QT}) is heritable and its prolongation is a risk factor for ventricular tachyarrhythmias and sudden death. Most genetic studies of {QT} have examined European ancestral populations; however, the increased genetic diversity in African Americans provides opportunities to narrow association signals and identify population-specific variants. We therefore evaluated 6,670 SNPs spanning eleven previously identified {QT} loci in 8,644 African American participants from two {Population Architecture using Genomics and Epidemiology} (PAGE) studies: the Atherosclerosis Risk in Communities study and Women's Health Initiative Clinical Trial. Of the fifteen known independent {QT} variants at the eleven previously identified loci, six were significantly associated with {QT} in African American populations (P≤1.20×10(-4)): ATP1B1, PLN1, KCNQ1, NDRG4, and two NOS1AP independent signals. We also identified three population-specific signals significantly associated with {QT} in African Americans (P≤1.37×10(-5)): one at NOS1AP and two at ATP1B1. Linkage disequilibrium (LD) patterns in African Americans assisted in narrowing the region likely to contain the functional variants for several loci. For example, African American LD patterns showed that 0 SNPs were in LD with NOS1AP signal rs12143842, compared with European LD patterns that indicated 87 SNPs, which spanned 114.2 Kb, were in LD with rs12143842. Finally, bioinformatic-based characterization of the nine African American signals pointed to functional candidates located exclusively within non-coding regions, including predicted binding sites for transcription factors such as TBX5, which has been implicated in cardiac structure and conductance. In this detailed evaluation of {QT} loci, we identified several African Americans SNPs that better define the association with {QT} and successfully narrowed intervals surrounding established loci. These results demonstrate that the same loci influence variation in {QT} across multiple populations, that novel signals exist in African Americans, and that the SNPs identified as strong candidates for functional evaluation implicate gene regulatory dysfunction in {QT} prolongation.},\n\tauthor = {Avery, Christy L. and Sethupathy, Praveen and Buyske, Steven and He, Qianchuan and Lin, Dan-Yu and Arking, Dan E. and Carty, Cara L. and Duggan, David and Fesinmeyer, Megan D. and Hindorff, Lucia A. and Jeff, Janina M. and Klein, Liviu and Patton, Kristen K. and Peters, Ulrike and Shohet, Ralph V. and Sotoodehnia, Nona and Young, Alicia M. and Kooperberg, Charles and Haiman, Christopher A. and Mohlke, Karen L. and Whitsel, Eric A. and North, Kari E.},\n\tcitation-subset = {IM},\n\tcompleted = {2012-12-17},\n\tcountry = {United States},\n\tdoi = {10.1371/journal.pgen.1002870},\n\tissn = {1553-7404},\n\tissn-linking = {1553-7390},\n\tissue = {8},\n\tjournal = {PLoS genetics},\n\tkeywords = {African Americans; Aged; Computational Biology; Electrocardiography; European Continental Ancestry Group; Female; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Linkage Disequilibrium; Male; Metagenomics; Middle Aged; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Quantitative Trait, Heritable; Risk Factors; Tachycardia, ethnology, genetics; United States, epidemiology},\n\tnlm-id = {101239074},\n\towner = {NLM},\n\tpages = {e1002870},\n\tpii = {PGENETICS-D-12-00989},\n\tpmc = {PMC3415454},\n\tpmid = {22912591},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/22912591/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Fine-mapping and initial characterization of {{QT}} interval loci in {African Americans}.},\n\tvolume = {8},\n\tyear = {2012},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/22912591/},\n\tbdsk-url-2 = {https://doi.org/10.1371/journal.pgen.1002870}}\n\n

\n\n\n

\n The QT interval (QT) is heritable and its prolongation is a risk factor for ventricular tachyarrhythmias and sudden death. Most genetic studies of QT have examined European ancestral populations; however, the increased genetic diversity in African Americans provides opportunities to narrow association signals and identify population-specific variants. We therefore evaluated 6,670 SNPs spanning eleven previously identified QT loci in 8,644 African American participants from two Population Architecture using Genomics and Epidemiology (PAGE) studies: the Atherosclerosis Risk in Communities study and Women's Health Initiative Clinical Trial. Of the fifteen known independent QT variants at the eleven previously identified loci, six were significantly associated with QT in African American populations (P≤1.20×10(-4)): ATP1B1, PLN1, KCNQ1, NDRG4, and two NOS1AP independent signals. We also identified three population-specific signals significantly associated with QT in African Americans (P≤1.37×10(-5)): one at NOS1AP and two at ATP1B1. Linkage disequilibrium (LD) patterns in African Americans assisted in narrowing the region likely to contain the functional variants for several loci. For example, African American LD patterns showed that 0 SNPs were in LD with NOS1AP signal rs12143842, compared with European LD patterns that indicated 87 SNPs, which spanned 114.2 Kb, were in LD with rs12143842. Finally, bioinformatic-based characterization of the nine African American signals pointed to functional candidates located exclusively within non-coding regions, including predicted binding sites for transcription factors such as TBX5, which has been implicated in cardiac structure and conductance. In this detailed evaluation of QT loci, we identified several African Americans SNPs that better define the association with QT and successfully narrowed intervals surrounding established loci. These results demonstrate that the same loci influence variation in QT across multiple populations, that novel signals exist in African Americans, and that the SNPs identified as strong candidates for functional evaluation implicate gene regulatory dysfunction in QT prolongation.\n

\n\n\n

\n \n\n \n \n \n \n \n \n Evaluation of the Metabochip genotyping array in African Americans and implications for fine mapping of GWAS-identified loci: the PAGE study.\n \n \n \n \n\n\n \n Buyske, S.; Wu, Y.; Carty, C. L.; Cheng, I.; Assimes, T. L.; Dumitrescu, L.; Hindorff, L. A.; Mitchell, S.; Ambite, J. L.; Boerwinkle, E.; Buzkova, P.; Carlson, C. S.; Cochran, B.; Duggan, D.; Eaton, C. B.; Fesinmeyer, M. D.; Franceschini, N.; Haessler, J.; Jenny, N.; Kang, H. M.; Kooperberg, C.; Lin, Y.; Le Marchand, L.; Matise, T. C.; Robinson, J. G.; Rodriguez, C.; Schumacher, F. R.; Voight, B. F.; Young, A.; Manolio, T. A.; Mohlke, K. L.; Haiman, C. A.; Peters, U.; Crawford, D. C.; and North, K. E.\n\n\n \n\n\n\n PloS one, 7: e35651. 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Evaluation$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{BuyskeWuCartyEtAl2012,\n\tabstract = {The Metabochip is a custom genotyping array designed for replication and fine mapping of metabolic, cardiovascular, and anthropometric trait loci and includes low frequency variation content identified from the 1000 Genomes Project. It has 196,725 SNPs concentrated in 257 genomic regions. We evaluated the Metabochip in 5,863 African Americans; 89% of all SNPs passed rigorous quality control with a call rate of 99.9%. Two examples illustrate the value of fine mapping with the Metabochip in African-ancestry populations. At CELSR2/PSRC1/SORT1, we found the strongest associated {SNP} for LDL-C to be rs12740374 (p = 3.5 × 10(-11)), a {SNP} indistinguishable from multiple SNPs in European ancestry samples due to high correlation. Its distinct signal supports functional studies elsewhere suggesting a causal role in LDL-C. At CETP we found rs17231520, with risk allele frequency 0.07 in African Americans, to be associated with HDL-C (p = 7.2 × 10(-36)). This variant is very rare in Europeans and not tagged in common {GWAS} arrays, but was identified as associated with HDL-C in African Americans in a single-gene study. Our results, one narrowing the risk interval and the other revealing an associated variant not found in Europeans, demonstrate the advantages of high-density genotyping of common and rare variation for fine mapping of trait loci in African American samples.},\n\tauthor = {Buyske, Steven and Wu, Ying and Carty, Cara L. and Cheng, Iona and Assimes, Themistocles L. and Dumitrescu, Logan and Hindorff, Lucia A. and Mitchell, Sabrina and Ambite, Jose Luis and Boerwinkle, Eric and Buzkova, Petra and Carlson, Chris S. and Cochran, Barbara and Duggan, David and Eaton, Charles B. and Fesinmeyer, Megan D. and Franceschini, Nora and Haessler, Jeffrey and Jenny, Nancy and Kang, Hyun Min and Kooperberg, Charles and Lin, Yi and Le Marchand, Loic and Matise, Tara C. and Robinson, Jennifer G. and Rodriguez, Carlos and Schumacher, Fredrick R. and Voight, Benjamin F. and Young, Alicia and Manolio, Teri A. and Mohlke, Karen L. and Haiman, Christopher A. and Peters, Ulrike and Crawford, Dana C. and North, Kari E.},\n\tchemicals = {CETP protein, human, Cholesterol Ester Transfer Proteins, Cholesterol, HDL, Cholesterol, LDL},\n\tcitation-subset = {IM},\n\tcompleted = {2012-09-10},\n\tcountry = {United States},\n\tdoi = {10.1371/journal.pone.0035651},\n\tissn = {1932-6203},\n\tissn-linking = {1932-6203},\n\tissue = {4},\n\tjournal = {PloS one},\n\tkeywords = {African Americans, genetics; Cardiovascular Diseases, ethnology, genetics; Cholesterol Ester Transfer Proteins, genetics; Cholesterol, HDL, blood; Cholesterol, LDL, blood; Chromosomes, Human, genetics; Cohort Studies; Gene Frequency; Genome-Wide Association Study; Genotype; Humans; Metabolic Diseases, ethnology, genetics; Polymorphism, Single Nucleotide; Quantitative Trait Loci},\n\tnlm-id = {101285081},\n\towner = {NLM},\n\tpages = {e35651},\n\tpii = {PONE-D-12-01108},\n\tpmc = {PMC3335090},\n\tpmid = {22539988},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/22539988/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2019-12-10},\n\ttitle = {Evaluation of the {Metabochip} genotyping array in {African Americans} and implications for fine mapping of {GWAS}-identified loci: the {PAGE} study.},\n\tvolume = {7},\n\tyear = {2012},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/22539988/},\n\tbdsk-url-2 = {https://doi.org/10.1371/journal.pone.0035651}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Serum vitamins A and E as modifiers of lipid trait genetics in the National Health and Nutrition Examination Surveys as part of the Population Architecture using Genomics and Epidemiology (PAGE) study.\n \n \n \n \n\n\n \n Dumitrescu, L.; Goodloe, R.; Brown-Gentry, K.; Mayo, P.; Allen, M.; Jin, H.; Gillani, N. B.; Schnetz-Boutaud, N.; Dilks, H. H.; and Crawford, D. C.\n\n\n \n\n\n\n Human genetics, 131: 1699–1708. November 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Serum$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{DumitrescuGoodloeBrownGentryEtAl2012,\n\tabstract = {Both environmental and genetic factors impact lipid traits. Environmental modifiers of known genotype-phenotype associations may account for some of the "missing heritability" of these traits. To identify such modifiers, we genotyped 23 lipid-associated variants identified previously through genome-wide association studies ({GWAS}) in 2,435 non-Hispanic white, 1,407 non-Hispanic black, and 1,734 Mexican-American samples collected for the National Health and Nutrition Examination Surveys (NHANES). Along with lipid levels, NHANES collected environmental variables, including fat-soluble macronutrient serum levels of vitamin A and E levels. As part of the {Population Architecture using Genomics and Epidemiology} (PAGE) study, we modeled gene-environment interactions between vitamin A or vitamin E and 23 variants previously associated with high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) levels. We identified three {SNP} × vitamin A and six {SNP} × vitamin E interactions at a significance threshold of p < 2.2 × 10(-3). The most significant interaction was APOB rs693 × vitamin E (p = 8.9 × 10(-7)) for LDL-C levels among Mexican-Americans. The nine significant interaction models individually explained 0.35-1.61% of the variation in any one of the lipid traits. Our results suggest that vitamins A and E may modify known genotype-phenotype associations; however, these interactions account for only a fraction of the overall variability observed for HDL-C, LDL-C, and TG levels in the general population.},\n\tauthor = {Dumitrescu, Logan and Goodloe, Robert and Brown-Gentry, Kristin and Mayo, Ping and Allen, Melissa and Jin, Hailing and Gillani, Niloufar B. and Schnetz-Boutaud, Nathalie and Dilks, Holli H. and Crawford, Dana C.},\n\tchemicals = {Cholesterol, HDL, Cholesterol, LDL, Genetic Markers, Triglycerides, Vitamin A, Vitamin E},\n\tcitation-subset = {IM},\n\tcompleted = {2013-01-08},\n\tcountry = {Germany},\n\tdoi = {10.1007/s00439-012-1186-y},\n\tissn = {1432-1203},\n\tissn-linking = {0340-6717},\n\tissue = {11},\n\tjournal = {Human genetics},\n\tkeywords = {Adult; African Americans, genetics; Cholesterol, HDL, genetics; Cholesterol, LDL, genetics; Cohort Studies; European Continental Ancestry Group, genetics; Female; Fluorescent Antibody Technique; Gene-Environment Interaction; Genetic Association Studies; Genetic Markers; Genome-Wide Association Study; Humans; Mexican Americans, genetics; Molecular Epidemiology; Nutrition Surveys; Polymorphism, Single Nucleotide, genetics; Quantitative Trait Loci; Risk Factors; Triglycerides, genetics; Vitamin A, blood; Vitamin E, blood},\n\tmid = {NIHMS391691},\n\tmonth = nov,\n\tnlm-id = {7613873},\n\towner = {NLM},\n\tpages = {1699--1708},\n\tpmc = {PMC3472117},\n\tpmid = {22688886},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/22688886/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Serum vitamins {A} and {E} as modifiers of lipid trait genetics in the {National Health and Nutrition Examination Surveys} as part of the {Population Architecture using Genomics and Epidemiology} ({PAGE}) study.},\n\tvolume = {131},\n\tyear = {2012},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/22688886/},\n\tbdsk-url-2 = {https://doi.org/10.1007/s00439-012-1186-y}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Associations between incident ischemic stroke events and stroke and cardiovascular disease-related genome-wide association studies single nucleotide polymorphisms in the Population Architecture Using Genomics and Epidemiology study.\n \n \n \n \n\n\n \n Carty, C. L.; Buzková, P.; Fornage, M.; Franceschini, N.; Cole, S.; Heiss, G.; Hindorff, L. A.; Howard, B. V.; Mann, S.; Martin, L. W.; Zhang, Y.; Matise, T. C.; Prentice, R.; Reiner, A. P.; and Kooperberg, C.\n\n\n \n\n\n\n Circulation. Cardiovascular genetics, 5: 210–216. April 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Associations$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{CartyBuzkovaFornageEtAl2012,\n\tabstract = {Genome-wide association studies ({GWAS}) have identified loci associated with ischemic stroke (IS) and cardiovascular disease (CVD) in European-descent individuals, but their replication in different populations has been largely unexplored. Nine single nucleotide polymorphisms (SNPs) selected from {GWAS} and meta-analyses of stroke, and 86 SNPs previously associated with myocardial infarction and CVD risk factors, including blood lipids (high density lipoprotein [HDL], low density lipoprotein [LDL], and triglycerides), type 2 diabetes, and body mass index (BMI), were investigated for associations with incident IS in European Americans (EA) N=26 276, African-Americans (AA) N=8970, and American Indians (AI) N=3570 from the {Population Architecture using Genomics and Epidemiology} Study. Ancestry-specific fixed effects meta-analysis with inverse variance weighting was used to combine study-specific log hazard ratios from Cox proportional hazards models. Two of 9 stroke SNPs (rs783396 and rs1804689) were significantly associated with [corrected] IS hazard in AA; none were significant in this large EA cohort. Of 73 CVD risk factor SNPs tested in EA, 2 (HDL and triglycerides SNPs) were associated with IS. In AA, SNPs associated with LDL, HDL, and BMI were significantly associated with IS (3 of 86 SNPs tested). Out of 58 SNPs tested in AI, 1 LDL {SNP} was significantly associated with IS. Our analyses showing lack of replication in spite of reasonable power for many stroke SNPs and differing results by ancestry highlight the need to follow up on {GWAS} findings and conduct genetic association studies in diverse populations. We found modest IS associations with BMI and lipids SNPs, though these findings require confirmation.},\n\tauthor = {Carty, Cara L. and Buzkov{\\'a}, Petra and Fornage, Myriam and Franceschini, Nora and Cole, Shelley and Heiss, Gerardo and Hindorff, Lucia A. and Howard, Barbara V. and Mann, Sue and Martin, Lisa W. and Zhang, Ying and Matise, Tara C. and Prentice, Ross and Reiner, Alexander P. and Kooperberg, Charles},\n\tchemicals = {Cholesterol, HDL, Cholesterol, LDL, Triglycerides},\n\tcitation-subset = {IM},\n\tcompleted = {2012-08-27},\n\tcountry = {United States},\n\tdoi = {10.1161/CIRCGENETICS.111.962191},\n\tissn = {1942-3268},\n\tissn-linking = {1942-3268},\n\tissue = {2},\n\tjournal = {Circulation. Cardiovascular genetics},\n\tkeywords = {Aged; Aged, 80 and over; Cardiovascular Diseases, epidemiology, ethnology, genetics, metabolism; Cholesterol, HDL, metabolism; Cholesterol, LDL, metabolism; European Continental Ancestry Group, ethnology, genetics; Female; Genetics, Population; Genome-Wide Association Study; Genomics; Humans; Male; Middle Aged; Polymorphism, Single Nucleotide; Risk Factors; Stroke, epidemiology, ethnology, genetics, metabolism; Triglycerides, metabolism},\n\tmid = {NIHMS369440},\n\tmonth = apr,\n\tnlm-id = {101489144},\n\towner = {NLM},\n\tpages = {210--216},\n\tpii = {CIRCGENETICS.111.962191},\n\tpmc = {PMC3402178},\n\tpmid = {22403240},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/22403240/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Associations between incident ischemic stroke events and stroke and cardiovascular disease-related genome-wide association studies single nucleotide polymorphisms in the {Population Architecture Using Genomics and Epidemiology} study.},\n\tvolume = {5},\n\tyear = {2012},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/22403240/},\n\tbdsk-url-2 = {https://doi.org/10.1161/CIRCGENETICS.111.962191}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n The association of genetic variants of type 2 diabetes with kidney function.\n \n \n \n \n\n\n \n Franceschini, N.; Shara, N. M.; Wang, H.; Voruganti, V. S.; Laston, S.; Haack, K.; Lee, E. T.; Best, L. G.; Maccluer, J. W.; Cochran, B. J.; Dyer, T. D.; Howard, B. V.; Cole, S. A.; North, K. E.; and Umans, J. G.\n\n\n \n\n\n\n Kidney international, 82: 220–225. July 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{FranceschiniSharaWangEtAl2012,\n\tabstract = {Type 2 diabetes is highly prevalent and is the major cause of progressive chronic kidney disease in American Indians. Genome-wide association studies identified several loci associated with diabetes but their impact on susceptibility to diabetic complications is unknown. We studied the association of 18 type 2 diabetes genome-wide association single-nucleotide polymorphisms (SNPs) with estimated glomerular filtration rate (eGFR; MDRD equation) and urine albumin-to-creatinine ratio in 6958 Strong Heart Study family and cohort participants. Center-specific residuals of eGFR and log urine albumin-to-creatinine ratio, obtained from linear regression models adjusted for age, sex, and body mass index, were regressed onto {SNP} dosage using variance component models in family data and linear regression in unrelated individuals. Estimates were then combined across centers. Four diabetic loci were associated with eGFR and one locus with urine albumin-to-creatinine ratio. A {SNP} in the WFS1 gene (rs10010131) was associated with higher eGFR in younger individuals and with increased albuminuria. SNPs in the FTO, KCNJ11, and TCF7L2 genes were associated with lower eGFR, but not albuminuria, and were not significant in prospective analyses. Our findings suggest a shared genetic risk for type 2 diabetes and its kidney complications, and a potential role for WFS1 in early-onset diabetic nephropathy in American Indian populations.},\n\tauthor = {Franceschini, Nora and Shara, Nawar M. and Wang, Hong and Voruganti, V. Saroja and Laston, Sandy and Haack, Karin and Lee, Elisa T. and Best, Lyle G. and Maccluer, Jean W. and Cochran, Barbara J. and Dyer, Thomas D. and Howard, Barbara V. and Cole, Shelley A. and North, Kari E. and Umans, Jason G.},\n\tchemicals = {Biomarkers, Membrane Proteins, wolframin protein, Creatinine},\n\tcitation-subset = {IM},\n\tcompleted = {2012-11-06},\n\tcountry = {United States},\n\tdoi = {10.1038/ki.2012.107},\n\tissn = {1523-1755},\n\tissn-linking = {0085-2538},\n\tissue = {2},\n\tjournal = {Kidney international},\n\tkeywords = {Age of Onset; Aged; Albuminuria, genetics, physiopathology; Biomarkers, urine; Creatinine, urine; Cross-Sectional Studies; Diabetes Mellitus, Type 2, ethnology, genetics; Diabetic Nephropathies, ethnology, genetics, physiopathology; Female; Gene Frequency; Genetic Predisposition to Disease; Genome-Wide Association Study; Glomerular Filtration Rate, genetics; Humans; Indians, North American, genetics; Kidney, metabolism, physiopathology; Linear Models; Linkage Disequilibrium; Longitudinal Studies; Male; Membrane Proteins, genetics; Middle Aged; Phenotype; Polymorphism, Single Nucleotide; Prospective Studies; Risk Assessment; Risk Factors; United States, epidemiology},\n\tmid = {NIHMS363695},\n\tmonth = jul,\n\tnlm-id = {0323470},\n\towner = {NLM},\n\tpages = {220--225},\n\tpii = {S0085-2538(15)55508-7},\n\tpmc = {PMC3664521},\n\tpmid = {22513821},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/22513821/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {The association of genetic variants of type 2 diabetes with kidney function.},\n\tvolume = {82},\n\tyear = {2012},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/22513821/},\n\tbdsk-url-2 = {https://doi.org/10.1038/ki.2012.107}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Consistent directions of effect for established type 2 diabetes risk variants across populations: the Population Architecture using Genomics and Epidemiology (PAGE) Consortium.\n \n \n \n \n\n\n \n Haiman, C. A.; Fesinmeyer, M. D.; Spencer, K. L.; Buzková, P.; Voruganti, V. S.; Wan, P.; Haessler, J.; Franceschini, N.; Monroe, K. R.; Howard, B. V.; Jackson, R. D.; Florez, J. C.; Kolonel, L. N.; Buyske, S.; Goodloe, R. J.; Liu, S.; Manson, J. E.; Meigs, J. B.; Waters, K.; Mukamal, K. J.; Pendergrass, S. A.; Shrader, P.; Wilkens, L. R.; Hindorff, L. A.; Ambite, J. L.; North, K. E.; Peters, U.; Crawford, D. C.; Le Marchand, L.; and Pankow, J. S.\n\n\n \n\n\n\n Diabetes, 61: 1642–1647. June 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Consistent$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{HaimanFesinmeyerSpencerEtAl2012,\n\tabstract = {Common genetic risk variants for type 2 diabetes (T2D) have primarily been identified in populations of European and Asian ancestry. We tested whether the direction of association with 20 T2D risk variants generalizes across six major racial/ethnic groups in the U.S. as part of the {Population Architecture using Genomics and Epidemiology} Consortium (16,235 diabetes case and 46,122 control subjects of European American, African American, Hispanic, East Asian, American Indian, and Native Hawaiian ancestry). The percentage of positive (odds ratio [OR] >1 for putative risk allele) associations ranged from 69% in American Indians to 100% in European Americans. Of the nine variants where we observed significant heterogeneity of effect by racial/ethnic group (P(heterogeneity) < 0.05), eight were positively associated with risk (OR >1) in at least five groups. The marked directional consistency of association observed for most genetic variants across populations implies a shared functional common variant in each region. Fine-mapping of all loci will be required to reveal markers of risk that are important within and across populations.},\n\tauthor = {Haiman, Christopher A. and Fesinmeyer, Megan D. and Spencer, Kylee L. and Buzkov{\\'a}, Petra and Voruganti, V. Saroja and Wan, Peggy and Haessler, Jeff and Franceschini, Nora and Monroe, Kristine R. and Howard, Barbara V. and Jackson, Rebecca D. and Florez, Jose C. and Kolonel, Laurence N. and Buyske, Steven and Goodloe, Robert J. and Liu, Simin and Manson, Joann E. and Meigs, James B. and Waters, Kevin and Mukamal, Kenneth J. and Pendergrass, Sarah A. and Shrader, Peter and Wilkens, Lynne R. and Hindorff, Lucia A. and Ambite, Jose Luis and North, Kari E. and Peters, Ulrike and Crawford, Dana C. and Le Marchand, Loic and Pankow, James S.},\n\tcitation-subset = {AIM, IM},\n\tcompleted = {2012-07-25},\n\tcountry = {United States},\n\tdoi = {10.2337/db11-1296},\n\tissn = {1939-327X},\n\tissn-linking = {0012-1797},\n\tissue = {6},\n\tjournal = {Diabetes},\n\tkeywords = {Adult; Aged; Aged, 80 and over; Alleles; Diabetes Mellitus, Type 2, ethnology, genetics; Female; Genetic Predisposition to Disease; Genome-Wide Association Study; Genotype; Humans; Male; Metagenomics; Middle Aged; Population Groups, genetics; Risk; Risk Factors},\n\tmonth = jun,\n\tnlm-id = {0372763},\n\towner = {NLM},\n\tpages = {1642--1647},\n\tpii = {db11-1296},\n\tpmc = {PMC3357304},\n\tpmid = {22474029},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/22474029/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Consistent directions of effect for established type 2 diabetes risk variants across populations: the {Population Architecture using Genomics and Epidemiology }({PAGE}) Consortium.},\n\tvolume = {61},\n\tyear = {2012},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/22474029/},\n\tbdsk-url-2 = {https://doi.org/10.2337/db11-1296}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Genotype imputation of Metabochip SNPs using a study-specific reference panel of ~ 4,000 haplotypes in African Americans from the Women's Health Initiative.\n \n \n \n \n\n\n \n Liu, E. Y.; Buyske, S.; Aragaki, A. K.; Peters, U.; Boerwinkle, E.; Carlson, C.; Carty, C.; Crawford, D. C.; Haessler, J.; Hindorff, L. A.; Marchand, L. L.; Manolio, T. A.; Matise, T.; Wang, W.; Kooperberg, C.; North, K. E.; and Li, Y.\n\n\n \n\n\n\n Genetic epidemiology, 36: 107–117. February 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Genotype$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{LiuBuyskeAragakiEtAl2012,\n\tabstract = {Genetic imputation has become standard practice in modern genetic studies. However, several important issues have not been adequately addressed including the utility of study-specific reference, performance in admixed populations, and quality for less common (minor allele frequency [MAF] 0.005-0.05) and rare (MAF < 0.005) variants. These issues only recently became addressable with genome-wide association studies ({GWAS}) follow-up studies using dense genotyping or sequencing in large samples of non-European individuals. In this work, we constructed a study-specific reference panel of 3,924 haplotypes using African Americans in the Women's Health Initiative (WHI) genotyped on both the Metabochip and the Affymetrix 6.0 {GWAS} platform. We used this reference panel to impute into 6,459 WHI {SNP} Health Association Resource (SHARe) study subjects with only {GWAS} genotypes. Our analysis confirmed the imputation quality metric Rsq (estimated r(2) , specific to each {SNP}) as an effective post-imputation filter. We recommend different Rsq thresholds for different MAF categories such that the average (across SNPs) Rsq is above the desired dosage r(2)  (squared Pearson correlation between imputed and experimental genotypes). With a desired dosage r(2)  of 80%, 99.9% (97.5%, 83.6%, 52.0%, 20.5%) of SNPs with MAF > 0.05 (0.03-0.05, 0.01-0.03, 0.005-0.01, and 0.001-0.005) passed the post-imputation filter. The average dosage r(2)  for these SNPs is 94.7%, 92.1%, 89.0%, 83.1%, and 79.7%, respectively. These results suggest that for African Americans imputation of Metabochip SNPs from {GWAS} data, including low frequency SNPs with MAF 0.005-0.05, is feasible and worthwhile for power increase in downstream association analysis provided a sizable reference panel is available.},\n\tauthor = {Liu, Eric Yi and Buyske, Steven and Aragaki, Aaron K. and Peters, Ulrike and Boerwinkle, Eric and Carlson, Chris and Carty, Cara and Crawford, Dana C. and Haessler, Jeff and Hindorff, Lucia A. and Marchand, Loic Le and Manolio, Teri A. and Matise, Tara and Wang, Wei and Kooperberg, Charles and North, Kari E. and Li, Yun},\n\tcitation-subset = {IM},\n\tcompleted = {2013-01-10},\n\tcountry = {United States},\n\tdoi = {10.1002/gepi.21603},\n\tissn = {1098-2272},\n\tissn-linking = {0741-0395},\n\tissue = {2},\n\tjournal = {Genetic epidemiology},\n\tkeywords = {African Americans; Alleles; Female; Gene Frequency; Genome, Human; Genome-Wide Association Study; Genotype; Haplotypes; Humans; Models, Genetic; Oligonucleotide Array Sequence Analysis, methods; Phenotype; Polymorphism, Single Nucleotide; Reproducibility of Results; Software; United States; Women's Health},\n\tmid = {NIHMS359763},\n\tmonth = feb,\n\tnlm-id = {8411723},\n\towner = {NLM},\n\tpages = {107--117},\n\tpmc = {PMC3410659},\n\tpmid = {22851474},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/22851474/},\n\n\tpubmodel = {Print},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Genotype imputation of {Metabochip} {SNP}s using a study-specific reference panel of \\textasciitilde 4,000 haplotypes in {African Americans} from the {Women's Health Initiative}.},\n\tvolume = {36},\n\tyear = {2012},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/22851474/},\n\tbdsk-url-2 = {https://doi.org/10.1002/gepi.21603}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Susceptibility variants for obesity and colorectal cancer risk: the Multiethnic Cohort and PAGE studies.\n \n \n \n \n\n\n \n Lim, U.; Wilkens, L. R.; Monroe, K. R.; Caberto, C.; Tiirikainen, M.; Cheng, I.; Park, S. L.; Stram, D. O.; Henderson, B. E.; Kolonel, L. N.; Haiman, C. A.; and Le Marchand, L.\n\n\n \n\n\n\n International journal of cancer, 131: E1038–E1043. September 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Susceptibility$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{LimWilkensMonroeEtAl2012,\n\tabstract = {Obesity is a leading contributor to colorectal cancer risk. We investigated whether the risk variants identified in genome-wide association studies of body mass index (BMI) and waist size are associated with colorectal cancer risk, independently of the effect of obesity phenotype due to a shared etiology. Twenty-four single nucleotide polymorphisms (SNPs) in 15 loci (BDNF, FAIM2, FTO, GNPDA2, KCTD15, LYPLAL1, MC4R, MSRA, MTCH2, NEGR1, NRXN3, SEC16B, SH2B1, TFAP2B and TMEM18) were genotyped in a case-control study of 2,033 colorectal cancer cases and 9,640 controls nested within the multiethnic cohort study, as part of the population architecture using genomics and epidemiology consortium. Risk alleles for two obesity SNPs were associated with colorectal cancer risk--KCTD15 rs29941 [odds ratio (OR) for C allele = 0.90, 95% confidence interval (CI) 0.83-0.98; p = 0.01] and MC4R rs17782313 (OR for C allele = 1.12, 95% CI 1.02-1.22; p = 0.02). These associations were independent of the effect of BMI. However, none of the results remained significant after adjustment for multiple comparisons. No heterogeneity was observed across race/ethnic groups. Our findings suggest that the obesity risk variants are not likely to affect the risk of colorectal cancer substantially.},\n\tauthor = {Lim, Unhee and Wilkens, Lynne R. and Monroe, Kristine R. and Caberto, Christian and Tiirikainen, Maarit and Cheng, Iona and Park, Sungshim Lani and Stram, Daniel O. and Henderson, Brian E. and Kolonel, Laurence N. and Haiman, Christopher A. and Le Marchand, Lo{\\"\\i}c},\n\tchemicals = {KCTD15 protein, human, Potassium Channels, Proteins, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, FTO protein, human},\n\tcitation-subset = {IM},\n\tcompleted = {2013-03-11},\n\tcountry = {United States},\n\tdoi = {10.1002/ijc.27592},\n\tissn = {1097-0215},\n\tissn-linking = {0020-7136},\n\tissue = {6},\n\tjournal = {International journal of cancer},\n\tkeywords = {Aged; Alpha-Ketoglutarate-Dependent Dioxygenase FTO; Body Mass Index; Cohort Studies; Colorectal Neoplasms, ethnology, etiology, genetics; Female; Humans; Male; Middle Aged; Obesity, ethnology, etiology, genetics; Polymorphism, Single Nucleotide; Potassium Channels, genetics; Proteins, genetics; Risk},\n\tmid = {NIHMS371827},\n\tmonth = sep,\n\tnlm-id = {0042124},\n\towner = {NLM},\n\tpages = {E1038--E1043},\n\tpmc = {PMC3402643},\n\tpmid = {22511254},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/22511254/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Susceptibility variants for obesity and colorectal cancer risk: the {Multiethnic Cohort} and {PAGE} studies.},\n\tvolume = {131},\n\tyear = {2012},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/22511254/},\n\tbdsk-url-2 = {https://doi.org/10.1002/ijc.27592}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n HNF1B and endometrial cancer risk: results from the PAGE study.\n \n \n \n \n\n\n \n Setiawan, V. W.; Haessler, J.; Schumacher, F.; Cote, M. L.; Deelman, E.; Fesinmeyer, M. D.; Henderson, B. E.; Jackson, R. D.; Vöckler, J.; Wilkens, L. R.; Yasmeen, S.; Haiman, C. A.; Peters, U.; Le Marchand, L.; and Kooperberg, C.\n\n\n \n\n\n\n PloS one, 7: e30390. 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"HNF1B$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{SetiawanHaesslerSchumacherEtAl2012,\n\tabstract = {We examined the association between HNF1B variants identified in a recent genome-wide association study and endometrial cancer in two large case-control studies nested in prospective cohorts: the Multiethnic Cohort Study (MEC) and the Women's Health Initiative (WHI) as part of the {Population Architecture using Genomics and Epidemiology} (PAGE) study. A total of 1,357 incident cases of invasive endometrial cancer and 7,609 controls were included in the analysis (MEC: 426 cases/3,854 controls; WHI: 931 cases/3,755 controls). The majority of women in the WHI were European American, while the MEC included sizable numbers of African Americans, Japanese and Latinos. We estimated the odds ratios (ORs) per allele and 95% confidence intervals (CIs) of each {SNP} using unconditional logistic regression adjusting for age, body mass index, and four principal components of ancestry informative markers. The combined ORs were estimated using fixed effect models. Rs4430796 and rs7501939 were associated with endometrial cancer risk in MEC and WHI with no heterogeneity observed across racial/ethnic groups (P ≥ 0.21) or between studies (P ≥ 0.70). The OR(per allele) was 0.82 (95% CI: 0.75, 0.89; P = 5.63 × 10(-6)) for rs4430796 (G allele) and 0.79 (95% CI: 0.73, 0.87; P = 3.77 × 10(-7)) for rs7501939 (A allele). The associations with the risk of Type I and Type II tumors were similar (P ≥ 0.19). Adjustment for additional endometrial cancer risk factors such as parity, oral contraceptive use, menopausal hormone use, and smoking status had little effect on the results. In conclusion, HNF1B SNPs are associated with risk of endometrial cancer and that the associated relative risks are similar for Type I and Type II tumors.},\n\tauthor = {Setiawan, Veronica Wendy and Haessler, Jeffrey and Schumacher, Fredrick and Cote, Michele L. and Deelman, Ewa and Fesinmeyer, Megan D. and Henderson, Brian E. and Jackson, Rebecca D. and V{\\"o}ckler, Jens-S. and Wilkens, Lynne R. and Yasmeen, Shagufta and Haiman, Christopher A. and Peters, Ulrike and Le Marchand, Lo{\\"\\i}c and Kooperberg, Charles},\n\tchemicals = {HNF1B protein, human, Hepatocyte Nuclear Factor 1-beta},\n\tcitation-subset = {IM},\n\tcompleted = {2012-07-05},\n\tcountry = {United States},\n\tdoi = {10.1371/journal.pone.0030390},\n\tissn = {1932-6203},\n\tissn-linking = {1932-6203},\n\tissue = {1},\n\tjournal = {PloS one},\n\tkeywords = {Aged; Carcinoma, Endometrioid, ethnology, genetics, pathology; Case-Control Studies; Cohort Studies; Endometrial Neoplasms, ethnology, genetics, pathology; Ethnic Groups, statistics & numerical data; Female; Genetics, Population; Hepatocyte Nuclear Factor 1-beta, genetics, physiology; Humans; Male; Middle Aged; Polymorphism, Single Nucleotide, physiology},\n\tnlm-id = {101285081},\n\towner = {NLM},\n\tpages = {e30390},\n\tpii = {PONE-D-11-22303},\n\tpmc = {PMC3267708},\n\tpmid = {22299039},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/22299039/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {{HNF1B} and endometrial cancer risk: results from the {PAGE} study.},\n\tvolume = {7},\n\tyear = {2012},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/22299039/},\n\tbdsk-url-2 = {https://doi.org/10.1371/journal.pone.0030390}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Population differences in genetic risk for age-related macular degeneration and implications for genetic testing.\n \n \n \n \n\n\n \n Spencer, K. L.; Glenn, K.; Brown-Gentry, K.; Haines, J. L.; and Crawford, D. C.\n\n\n \n\n\n\n Archives of ophthalmology (Chicago, Ill. : 1960), 130: 116–117. January 2012.\n \n\n\n\n
\n\n\n\n \n \n $\"Population$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{SpencerGlennBrownGentryEtAl2012,\n\tauthor = {Spencer, Kylee L. and Glenn, Kimberly and Brown-Gentry, Kristin and Haines, Jonathan L. and Crawford, Dana C.},\n\tchemicals = {ARMS2 protein, human, Proteins},\n\tcitation-subset = {AIM, IM},\n\tcompleted = {2012-02-23},\n\tcountry = {United States},\n\tdoi = {10.1001/archopthalmol.2011.1370},\n\tissn = {1538-3601},\n\tissn-linking = {0003-9950},\n\tissue = {1},\n\tjournal = {Archives of ophthalmology (Chicago, Ill. : 1960)},\n\tkeywords = {African Americans, genetics; Cross-Sectional Studies; European Continental Ancestry Group, genetics; Genetic Predisposition to Disease; Genetic Testing; Genetics, Population; Genotype; Humans; Macular Degeneration, genetics; Mexican Americans, genetics; Middle Aged; Polymorphism, Genetic; Proteins, genetics},\n\tmid = {NIHMS368228},\n\tmonth = jan,\n\tnlm-id = {7706534},\n\towner = {NLM},\n\tpages = {116--117},\n\tpii = {130/1/116},\n\tpmc = {PMC3326353},\n\tpmid = {22232482},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/22232482/},\n\n\tpubmodel = {Print},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Population differences in genetic risk for age-related macular degeneration and implications for genetic testing.},\n\tvolume = {130},\n\tyear = {2012},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/22232482/},\n\tbdsk-url-2 = {https://doi.org/10.1001/archopthalmol.2011.1370}}\n\n

\n\n\n\n

\n\n\n\n\n\n

\n\n

\n \n 2011\n \n \n (9)\n \n \n

\n \n \n

\n \n\n \n \n \n \n \n \n A phenomics-based strategy identifies loci on APOC1, BRAP, and PLCG1 associated with metabolic syndrome phenotype domains.\n \n \n \n \n\n\n \n Avery, C. L.; He, Q.; North, K. E.; Ambite, J. L.; Boerwinkle, E.; Fornage, M.; Hindorff, L. A.; Kooperberg, C.; Meigs, J. B.; Pankow, J. S.; Pendergrass, S. A.; Psaty, B. M.; Ritchie, M. D.; Rotter, J. I.; Taylor, K. D.; Wilkens, L. R.; Heiss, G.; and Lin, D. Y.\n\n\n \n\n\n\n PLoS genetics, 7: e1002322. October 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"A$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{AveryHeNorthEtAl2011,\n\tabstract = {Despite evidence of the clustering of metabolic syndrome components, current approaches for identifying unifying genetic mechanisms typically evaluate clinical categories that do not provide adequate etiological information. Here, we used data from 19,486 European American and 6,287 African American Candidate Gene Association Resource Consortium participants to identify loci associated with the clustering of metabolic phenotypes. Six phenotype domains (atherogenic dyslipidemia, vascular dysfunction, vascular inflammation, pro-thrombotic state, central obesity, and elevated plasma glucose) encompassing 19 quantitative traits were examined. Principal components analysis was used to reduce the dimension of each domain such that >55% of the trait variance was represented within each domain. We then applied a statistically efficient and computational feasible multivariate approach that related eight principal components from the six domains to 250,000 imputed SNPs using an additive genetic model and including demographic covariates. In European Americans, we identified 606 genome-wide significant SNPs representing 19 loci. Many of these loci were associated with only one trait domain, were consistent with results in African Americans, and overlapped with published findings, for instance central obesity and FTO. However, our approach, which is applicable to any set of interval scale traits that is heritable and exhibits evidence of phenotypic clustering, identified three new loci in or near APOC1, BRAP, and PLCG1, which were associated with multiple phenotype domains. These pleiotropic loci may help characterize metabolic dysregulation and identify targets for intervention.},\n\tauthor = {Avery, Christy L. and He, Qianchuan and North, Kari E. and Ambite, Jose L. and Boerwinkle, Eric and Fornage, Myriam and Hindorff, Lucia A. and Kooperberg, Charles and Meigs, James B. and Pankow, James S. and Pendergrass, Sarah A. and Psaty, Bruce M. and Ritchie, Marylyn D. and Rotter, Jerome I. and Taylor, Kent D. and Wilkens, Lynne R. and Heiss, Gerardo and Lin, Dan Yu},\n\tchemicals = {Apolipoprotein C-I, Blood Glucose, apolipoprotein C-I, human, BRAP protein, human, Ubiquitin-Protein Ligases, PLCG1 protein, human, Phospholipase C gamma},\n\tcitation-subset = {IM},\n\tcompleted = {2012-02-10},\n\tcountry = {United States},\n\tdoi = {10.1371/journal.pgen.1002322},\n\tissn = {1553-7404},\n\tissn-linking = {1553-7390},\n\tissue = {10},\n\tjournal = {PLoS genetics},\n\tkeywords = {African Americans, genetics; Apolipoprotein C-I, genetics, metabolism; Blood Glucose, genetics, metabolism; Dyslipidemias, genetics, metabolism; European Continental Ancestry Group, genetics; Genetic Association Studies; Genetic Predisposition to Disease; Genome, Human; Humans; Metabolic Syndrome, genetics; Obesity, Abdominal, genetics, metabolism; Phenotype; Phospholipase C gamma, genetics, metabolism; Polymorphism, Single Nucleotide; Quantitative Trait, Heritable; Ubiquitin-Protein Ligases, genetics, metabolism; Vascular Diseases, genetics, metabolism},\n\tmonth = oct,\n\tnlm-id = {101239074},\n\towner = {NLM},\n\tpages = {e1002322},\n\tpii = {PGENETICS-D-11-00802},\n\tpmc = {PMC3192835},\n\tpmid = {22022282},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/22022282/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {A phenomics-based strategy identifies loci on {APOC1}, {BRAP}, and {PLCG1} associated with metabolic syndrome phenotype domains.},\n\tvolume = {7},\n\tyear = {2011},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/22022282/},\n\tbdsk-url-2 = {https://doi.org/10.1371/journal.pgen.1002322}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Genetic determinants of lipid traits in diverse populations from the Population Architecture using Genomics and Epidemiology (PAGE) study.\n \n \n \n \n\n\n \n Dumitrescu, L.; Carty, C. L.; Taylor, K.; Schumacher, F. R.; Hindorff, L. A.; Ambite, J. L.; Anderson, G.; Best, L. G.; Brown-Gentry, K.; B ̊u ̌zková, P.; Carlson, C. S.; Cochran, B.; Cole, S. A.; Devereux, R. B.; Duggan, D.; Eaton, C. B.; Fornage, M.; Franceschini, N.; Haessler, J.; Howard, B. V.; Johnson, K. C.; Laston, S.; Kolonel, L. N.; Lee, E. T.; MacCluer, J. W.; Manolio, T. A.; Pendergrass, S. A.; Quibrera, M.; Shohet, R. V.; Wilkens, L. R.; Haiman, C. A.; Le Marchand, L.; Buyske, S.; Kooperberg, C.; North, K. E.; and Crawford, D. C.\n\n\n \n\n\n\n PLoS genetics, 7: e1002138. June 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Genetic$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{DumitrescuCartyTaylorEtAl2011,\n\tabstract = {For the past five years, genome-wide association studies ({GWAS}) have identified hundreds of common variants associated with human diseases and traits, including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) levels. Approximately 95 loci associated with lipid levels have been identified primarily among populations of European ancestry. The {Population Architecture using Genomics and Epidemiology} (PAGE) study was established in 2008 to characterize {GWAS}-identified variants in diverse population-based studies. We genotyped 49 {GWAS}-identified SNPs associated with one or more lipid traits in at least two PAGE studies and across six racial/ethnic groups. We performed a meta-analysis testing for {SNP} associations with fasting HDL-C, LDL-C, and ln(TG) levels in self-identified European American (~20,000), African American (~9,000), American Indian (~6,000), Mexican American/Hispanic (~2,500), Japanese/East Asian (~690), and Pacific Islander/Native Hawaiian (~175) adults, regardless of lipid-lowering medication use. We replicated 55 of 60 (92%) {SNP} associations tested in European Americans at p<0.05. Despite sufficient power, we were unable to replicate ABCA1 rs4149268 and rs1883025, CETP rs1864163, and TTC39B rs471364 previously associated with HDL-C and MAFB rs6102059 previously associated with LDL-C. Based on significance (p<0.05) and consistent direction of effect, a majority of replicated genotype-phentoype associations for HDL-C, LDL-C, and ln(TG) in European Americans generalized to African Americans (48%, 61%, and 57%), American Indians (45%, 64%, and 77%), and Mexican Americans/Hispanics (57%, 56%, and 86%). Overall, 16 associations generalized across all three populations. For the associations that did not generalize, differences in effect sizes, allele frequencies, and linkage disequilibrium offer clues to the next generation of association studies for these traits.},\n\tauthor = {Dumitrescu, Logan and Carty, Cara L. and Taylor, Kira and Schumacher, Fredrick R. and Hindorff, Lucia A. and Ambite, Jos{\\'e} L. and Anderson, Garnet and Best, Lyle G. and Brown-Gentry, Kristin and B{\\r u}{\\v z}kov{\\'a}, Petra and Carlson, Christopher S. and Cochran, Barbara and Cole, Shelley A. and Devereux, Richard B. and Duggan, Dave and Eaton, Charles B. and Fornage, Myriam and Franceschini, Nora and Haessler, Jeff and Howard, Barbara V. and Johnson, Karen C. and Laston, Sandra and Kolonel, Laurence N. and Lee, Elisa T. and MacCluer, Jean W. and Manolio, Teri A. and Pendergrass, Sarah A. and Quibrera, Miguel and Shohet, Ralph V. and Wilkens, Lynne R. and Haiman, Christopher A. and Le Marchand, Lo{\\"\\i}c and Buyske, Steven and Kooperberg, Charles and North, Kari E. and Crawford, Dana C.},\n\tchemicals = {Lipoproteins, HDL, Lipoproteins, LDL, Triglycerides},\n\tcitation-subset = {IM},\n\tcompleted = {2011-11-01},\n\tcountry = {United States},\n\tdoi = {10.1371/journal.pgen.1002138},\n\tissn = {1553-7404},\n\tissn-linking = {1553-7390},\n\tissue = {6},\n\tjournal = {PLoS genetics},\n\tkeywords = {Adolescent; Adult; Aged; Aged, 80 and over; Continental Population Groups, genetics; Female; Gene Frequency, genetics; Genetics, Population; Genome-Wide Association Study; Humans; Linkage Disequilibrium, genetics; Lipid Metabolism, genetics; Lipoproteins, HDL, genetics; Lipoproteins, LDL, genetics; Male; Middle Aged; Molecular Epidemiology; Polymorphism, Single Nucleotide, genetics; Quantitative Trait Loci, genetics; Risk Factors; Triglycerides, genetics; Young Adult},\n\tmonth = jun,\n\tnlm-id = {101239074},\n\towner = {NLM},\n\tpages = {e1002138},\n\tpii = {PGENETICS-D-11-00002},\n\tpmc = {PMC3128106},\n\tpmid = {21738485},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/21738485/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2019-01-15},\n\ttitle = {Genetic determinants of lipid traits in diverse populations from the {Population Architecture using Genomics and Epidemiology} ({PAGE}) study.},\n\tvolume = {7},\n\tyear = {2011},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/21738485/},\n\tbdsk-url-2 = {https://doi.org/10.1371/journal.pgen.1002138}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Type 2 diabetes risk variants and colorectal cancer risk: the Multiethnic Cohort and PAGE studies.\n \n \n \n \n\n\n \n Cheng, I.; Caberto, C. P.; Lum-Jones, A.; Seifried, A.; Wilkens, L. R.; Schumacher, F. R.; Monroe, K. R.; Lim, U.; Tiirikainen, M.; Kolonel, L. N.; Henderson, B. E.; Stram, D. O.; Haiman, C. A.; and Le Marchand, L.\n\n\n \n\n\n\n Gut, 60: 1703–1711. December 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Type$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{ChengCabertoLumJonesEtAl2011,\n\tabstract = {Diabetes has been positively associated with the risk of colorectal cancer. This study investigated whether recently established risk variants for diabetes also have effects on colorectal cancer. 19 single nucleotide repeats (SNPs) associated with type 2 diabetes in genome-wide association studies were tested in a case-control study of 2011 colorectal cancer cases and 6049 controls nested in the Multiethnic Cohort study as part of the {Population Architecture using Genomics and Epidemiology} (PAGE) initiative. ORs and 95% CIs were estimated by unconditional logistic regression to evaluate the association between SNPs and colorectal cancer risk, adjusting for age, sex and race/ethnicity. Permutation testing was conducted to correct for multiple hypothesis testing. Four type 2 diabetes SNPs were associated with colorectal cancer risk: rs7578597 (THADA), rs864745 (JAZF1), rs5219 (KCNJ11) and rs7961581 (TSPAN8, LGR5). The strongest association was for the rs7578597 (THADA) Thr1187Ala missense polymorphism (P(trend)=0.004 adjusted for multiple testing), with the high risk allele for colorectal cancer being the low risk allele for diabetes. Similar patterns of associations were seen with further adjustment for diabetes status and body mass index. The association of diabetes status with colorectal cancer risk was somewhat weakened after adjustment for these SNPs. The findings suggest that diabetes risk variants also influence colorectal cancer susceptibility, possibly through mechanisms different from those for diabetes.},\n\tauthor = {Cheng, Iona and Caberto, Christian P. and Lum-Jones, Annette and Seifried, Ann and Wilkens, Lynne R. and Schumacher, Fredrick R. and Monroe, Kristine R. and Lim, Unhee and Tiirikainen, Maarit and Kolonel, Laurence N. and Henderson, Brian E. and Stram, Daniel O. and Haiman, Christopher A. and Le Marchand, Lo{\\"\\i}c},\n\tcitation-subset = {AIM, IM},\n\tcompleted = {2012-01-03},\n\tcountry = {England},\n\tdoi = {10.1136/gut.2011.237727},\n\tissn = {1468-3288},\n\tissn-linking = {0017-5749},\n\tissue = {12},\n\tjournal = {Gut},\n\tkeywords = {Aged; Body Mass Index; Case-Control Studies; Colorectal Neoplasms, etiology, genetics; Continental Population Groups, statistics & numerical data; Diabetes Mellitus, Type 2, etiology, genetics; Ethnic Groups, statistics & numerical data; Female; Genome-Wide Association Study; Humans; Logistic Models; Male; Polymorphism, Single Nucleotide, genetics; Risk Factors},\n\tmid = {NIHMS630948},\n\tmonth = dec,\n\tnlm-id = {2985108R},\n\towner = {NLM},\n\tpages = {1703--1711},\n\tpii = {gut.2011.237727},\n\tpmc = {PMC4332884},\n\tpmid = {21602532},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/21602532/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Type 2 diabetes risk variants and colorectal cancer risk: the {Multiethnic Cohort} and {PAGE} studies.},\n\tvolume = {60},\n\tyear = {2011},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/21602532/},\n\tbdsk-url-2 = {https://doi.org/10.1136/gut.2011.237727}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n No association of risk variants for diabetes and obesity with breast cancer: the Multiethnic Cohort and PAGE studies.\n \n \n \n \n\n\n \n Chen, F.; Wilkens, L. R.; Monroe, K. R.; Stram, D. O.; Kolonel, L. N.; Henderson, B. E.; Le Marchand, L.; and Haiman, C. A.\n\n\n \n\n\n\n Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 20: 1039–1042. May 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"No$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{ChenWilkensMonroeEtAl2011,\n\tabstract = {Body mass index is an established risk factor for postmenopausal breast cancer. Epidemiologic studies have also reported a positive association between type 2 diabetes (T2D) and breast cancer risk. To investigate a genetic basis linking these common phenotypes with breast cancer, we tested 31 common variants for T2D and obesity in a case-control study of 1,915 breast cancer cases and 2,884 controls nested within the Multiethnic Cohort (MEC) study. Following adjustment for multiple tests, we found no significant association between any variant and breast cancer risk. Summary scores comprising the numbers of risk alleles for T2D and/or obesity were also not found to be significantly associated with breast cancer risk. Our findings provide no evidence for association between established T2D and/or obesity risk variants and breast cancer risk among women of various ethnicities. These results suggest that the potential for a shared biology between T2D/obesity and breast cancer is not due to pleiotropic effects of these risk variants.},\n\tauthor = {Chen, Fang and Wilkens, Lynne R. and Monroe, Kristine R. and Stram, Daniel O. and Kolonel, Laurence N. and Henderson, Brian E. and Le Marchand, Lo{\\"\\i}c and Haiman, Christopher A.},\n\tcitation-subset = {IM},\n\tcompleted = {2011-09-15},\n\tcountry = {United States},\n\tdoi = {10.1158/1055-9965.EPI-11-0135},\n\tissn = {1538-7755},\n\tissn-linking = {1055-9965},\n\tissue = {5},\n\tjournal = {Cancer epidemiology, biomarkers \\& prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology},\n\tkeywords = {Aged; Body Mass Index; Breast Neoplasms, etiology, pathology; Case-Control Studies; Cohort Studies; Diabetes Complications, genetics; Diabetes Mellitus, Type 2, complications, genetics; Ethnic Groups, genetics; Female; Humans; Middle Aged; Obesity, complications, genetics; Postmenopause; Prognosis; Prospective Studies; Risk Factors; SEER Program},\n\tmid = {NIHMS633634},\n\tmonth = may,\n\tnlm-id = {9200608},\n\towner = {NLM},\n\tpages = {1039--1042},\n\tpii = {1055-9965.EPI-11-0135},\n\tpmc = {PMC4201112},\n\tpmid = {21357383},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/21357383/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {No association of risk variants for diabetes and obesity with breast cancer: the {Multiethnic Cohort} and {PAGE} studies.},\n\tvolume = {20},\n\tyear = {2011},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/21357383/},\n\tbdsk-url-2 = {https://doi.org/10.1158/1055-9965.EPI-11-0135}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Association of genetic variants and incident coronary heart disease in multiethnic cohorts: the PAGE study.\n \n \n \n \n\n\n \n Franceschini, N.; Carty, C.; B ̊uzková, P.; Reiner, A. P.; Garrett, T.; Lin, Y.; Vöckler, J.; Hindorff, L. A.; Cole, S. A.; Boerwinkle, E.; Lin, D.; Bookman, E.; Best, L. G.; Bella, J. N.; Eaton, C.; Greenland, P.; Jenny, N.; North, K. E.; Taverna, D.; Young, A. M.; Deelman, E.; Kooperberg, C.; Psaty, B.; and Heiss, G.\n\n\n \n\n\n\n Circulation. Cardiovascular genetics, 4: 661–672. December 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Association$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{FranceschiniCartyBuzkovaEtAl2011,\n\tabstract = {Genome-wide association studies identified several single nucleotide polymorphisms ({SNP}) associated with prevalent coronary heart disease (CHD), but less is known of associations with incident CHD. The association of 13 published CHD SNPs was examined in 5 ancestry groups of 4 large US prospective cohorts. The analyses included incident coronary events over an average 9.1 to 15.7 follow-up person-years in up to 26 617 white individuals (6626 events), 8018 black individuals (914 events), 1903 Hispanic individuals (113 events), 3669 American Indian individuals (595 events), and 885 Asian/Pacific Islander individuals (66 events). We used Cox proportional hazards models (with additive mode of inheritance) adjusted for age, sex, and ancestry (as needed). Nine loci were statistically associated with incident CHD events in white participants: 9p21 (rs10757278; P=4.7 × 10(-41)), 16q23.1 (rs2549513; P=0.0004), 6p24.1 (rs499818; P=0.0002), 2q36.3 (rs2943634; P=6.7 × 10(-6)), MTHFD1L (rs6922269, P=5.1 × 10(-10)), APOE (rs429358; P=2.7×10(-18)), ZNF627 (rs4804611; P=5.0 × 10(-8)), CXCL12 (rs501120; P=1.4 × 10(-6)) and LPL (rs268; P=2.7 × 10(-17)). The 9p21 region showed significant between-study heterogeneity, with larger effects in individuals age 55 years or younger and in women. Inclusion of coronary revascularization procedures among the incident CHD events introduced heterogeneity. The SNPs were not associated with CHD in black participants, and associations varied in other US minorities. Prospective analyses of white participants replicated several reported cross-sectional CHD-{SNP} associations.},\n\tauthor = {Franceschini, Nora and Carty, Cara and B{\\r u}zkov{\\'a}, Petra and Reiner, Alex P. and Garrett, Tiana and Lin, Yi and V{\\"o}ckler, Jens-S. and Hindorff, Lucia A. and Cole, Shelley A. and Boerwinkle, Eric and Lin, Dan-Yu and Bookman, Ebony and Best, Lyle G. and Bella, Jonathan N. and Eaton, Charles and Greenland, Philip and Jenny, Nancy and North, Kari E. and Taverna, Darin and Young, Alicia M. and Deelman, Ewa and Kooperberg, Charles and Psaty, Bruce and Heiss, Gerardo},\n\tcitation-subset = {IM},\n\tcompleted = {2012-04-13},\n\tcountry = {United States},\n\tdoi = {10.1161/CIRCGENETICS.111.960096},\n\tissn = {1942-3268},\n\tissn-linking = {1942-3268},\n\tissue = {6},\n\tjournal = {Circulation. Cardiovascular genetics},\n\tkeywords = {Aged; Aged, 80 and over; Continental Population Groups, ethnology, genetics; Coronary Disease, ethnology, genetics; Female; Genome-Wide Association Study; Humans; Male; Middle Aged; Polymorphism, Single Nucleotide; Prospective Studies},\n\tmid = {NIHMS340747},\n\tmonth = dec,\n\tnlm-id = {101489144},\n\towner = {NLM},\n\tpages = {661--672},\n\tpii = {CIRCGENETICS.111.960096},\n\tpmc = {PMC3293207},\n\tpmid = {22042884},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/22042884/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Association of genetic variants and incident coronary heart disease in multiethnic cohorts: the {PAGE} study.},\n\tvolume = {4},\n\tyear = {2011},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/22042884/},\n\tbdsk-url-2 = {https://doi.org/10.1161/CIRCGENETICS.111.960096}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Recent findings in the genetics of blood pressure and hypertension traits.\n \n \n \n \n\n\n \n Franceschini, N.; Reiner, A. P.; and Heiss, G.\n\n\n \n\n\n\n American journal of hypertension, 24: 392–400. April 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Recent$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{FranceschiniReinerHeiss2011,\n\tabstract = {We provide an overview of ongoing discovery efforts in the genetics of blood pressure (BP) and hypertension (HTN) traits. Two large genome-wide association meta-analyses of individuals of European descent were recently published, revealing ~13 new loci for BP traits. Only two of these loci harbor genes in a pathway known to affect BP (CYP17A1 and NPPA/NPPB). Functional variants in these loci are still unknown. Few genome-wide association studies ({GWAS}) of complex diseases have been published from non-European populations. The study of populations with different evolutionary history and linkage disequilibrium (LD) structure, such as individuals of African ancestry, may provide an opportunity to further narrow these regions to identify the causal gene(s). Several collaborative efforts toward discovery of low-frequency variants and copy number variation for BP traits are currently underway. As evidence for new loci for complex diseases accumulates the assessment of the epidemiologic architecture of these variants in populations assumes higher priority. The impact of public health-relevant contexts such as diet, physical activity, psychosocial factors, and aging has not been examined for most common variants associated with BP.},\n\tauthor = {Franceschini, Nora and Reiner, Alexander P. and Heiss, Gerardo},\n\tcitation-subset = {IM},\n\tcompleted = {2011-07-01},\n\tcountry = {United States},\n\tdoi = {10.1038/ajh.2010.218},\n\tissn = {1941-7225},\n\tissn-linking = {0895-7061},\n\tissue = {4},\n\tjournal = {American journal of hypertension},\n\tkeywords = {Blood Pressure, genetics; European Continental Ancestry Group, genetics; Genome-Wide Association Study; Humans; Hypertension, epidemiology, genetics},\n\tmid = {NIHMS294745},\n\tmonth = apr,\n\tnlm-id = {8803676},\n\towner = {NLM},\n\tpages = {392--400},\n\tpii = {ajh2010218},\n\tpmc = {PMC3110743},\n\tpmid = {20948529},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/20948529/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {Recent findings in the genetics of blood pressure and hypertension traits.},\n\tvolume = {24},\n\tyear = {2011},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/20948529/},\n\tbdsk-url-2 = {https://doi.org/10.1038/ajh.2010.218}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n The Next PAGE in understanding complex traits: design for the analysis of Population Architecture Using Genetics and Epidemiology (PAGE) Study.\n \n \n \n \n\n\n \n Matise, T. C.; Ambite, J. L.; Buyske, S.; Carlson, C. S.; Cole, S. A.; Crawford, D. C.; Haiman, C. A.; Heiss, G.; Kooperberg, C.; Marchand, L. L.; Manolio, T. A.; North, K. E.; Peters, U.; Ritchie, M. D.; Hindorff, L. A.; Haines, J. L.; and Study, P. A. G. E.\n\n\n \n\n\n\n American journal of epidemiology, 174: 849–859. October 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{MatiseAmbiteBuyskeEtAl2011,\n\tabstract = {Genetic studies have identified thousands of variants associated with complex traits. However, most association studies are limited to populations of European descent and a single phenotype. The {Population Architecture using Genomics and Epidemiology} (PAGE) Study was initiated in 2008 by the National Human Genome Research Institute to investigate the epidemiologic architecture of well-replicated genetic variants associated with complex diseases in several large, ethnically diverse population-based studies. Combining DNA samples and hundreds of phenotypes from multiple cohorts, PAGE is well-suited to address generalization of associations and variability of effects in diverse populations; identify genetic and environmental modifiers; evaluate disease subtypes, intermediate phenotypes, and biomarkers; and investigate associations with novel phenotypes. PAGE investigators harmonize phenotypes across studies where possible and perform coordinated cohort-specific analyses and meta-analyses. PAGE researchers are genotyping thousands of genetic variants in up to 121,000 DNA samples from African-American, white, Hispanic/Latino, Asian/Pacific Islander, and American Indian participants. Initial analyses will focus on single nucleotide polymorphisms (SNPs) associated with obesity, lipids, cardiovascular disease, type 2 diabetes, inflammation, various cancers, and related biomarkers. PAGE SNPs are also assessed for pleiotropy using the "phenome-wide association study" approach, testing each {SNP} for associations with hundreds of phenotypes. PAGE data will be deposited into the National Center for Biotechnology Information's Database of Genotypes and Phenotypes and made available via a custom browser.},\n\tauthor = {Matise, Tara C. and Ambite, Jose Luis and Buyske, Steven and Carlson, Christopher S. and Cole, Shelley A. and Crawford, Dana C. and Haiman, Christopher A. and Heiss, Gerardo and Kooperberg, Charles and Marchand, Loic Le and Manolio, Teri A. and North, Kari E. and Peters, Ulrike and Ritchie, Marylyn D. and Hindorff, Lucia A. and Haines, Jonathan L. and Study, P. A. G. E.},\n\tcitation-subset = {IM},\n\tcompleted = {2011-11-08},\n\tcountry = {United States},\n\tdoi = {10.1093/aje/kwr160},\n\tissn = {1476-6256},\n\tissn-linking = {0002-9262},\n\tissue = {7},\n\tjournal = {American journal of epidemiology},\n\tkeywords = {Epidemiologic Methods; Epidemiologic Research Design; Ethnic Groups, genetics; Genetic Association Studies, methods; Genetics, Population; Genome-Wide Association Study; Humans; Interinstitutional Relations; Multifactorial Inheritance; National Human Genome Research Institute (U.S.); Phenotype; Pilot Projects; Research Design; Risk Factors; United States},\n\tmonth = oct,\n\tnlm-id = {7910653},\n\towner = {NLM},\n\tpages = {849--859},\n\tpii = {kwr160},\n\tpmc = {PMC3176830},\n\tpmid = {21836165},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/21836165/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2018-11-13},\n\ttitle = {The Next {PAGE} in understanding complex traits: design for the analysis of {Population Architecture Using Genetics and Epidemiology} ({PAGE}) Study.},\n\tvolume = {174},\n\tyear = {2011},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/21836165/},\n\tbdsk-url-2 = {https://doi.org/10.1093/aje/kwr160}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n The use of phenome-wide association studies (PheWAS) for exploration of novel genotype-phenotype relationships and pleiotropy discovery.\n \n \n \n \n\n\n \n Pendergrass, S. A.; Brown-Gentry, K.; Dudek, S. M.; Torstenson, E. S.; Ambite, J. L.; Avery, C. L.; Buyske, S.; Cai, C.; Fesinmeyer, M. D.; Haiman, C.; Heiss, G.; Hindorff, L. A.; Hsu, C.; Jackson, R. D.; Kooperberg, C.; Le Marchand, L.; Lin, Y.; Matise, T. C.; Moreland, L.; Monroe, K.; Reiner, A. P.; Wallace, R.; Wilkens, L. R.; Crawford, D. C.; and Ritchie, M. D.\n\n\n \n\n\n\n Genetic epidemiology, 35: 410–422. July 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"The$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{PendergrassBrownGentryDudekEtAl2011,\n\tabstract = {The field of phenomics has been investigating network structure among large arrays of phenotypes, and genome-wide association studies ({GWAS}) have been used to investigate the relationship between genetic variation and single diseases/outcomes. A novel approach has emerged combining both the exploration of phenotypic structure and genotypic variation, known as the phenome-wide association study (PheWAS). The {Population Architecture using Genomics and Epidemiology} (PAGE) network is a National Human Genome Research Institute (NHGRI)-supported collaboration of four groups accessing eight extensively characterized epidemiologic studies. The primary focus of PAGE is deep characterization of well-replicated {GWAS} variants and their relationships to various phenotypes and traits in diverse epidemiologic studies that include European Americans, African Americans, Mexican Americans/Hispanics, Asians/Pacific Islanders, and Native Americans. The rich phenotypic resources of PAGE studies provide a unique opportunity for PheWAS as each genotyped variant can be tested for an association with the wide array of phenotypic measurements available within the studies of PAGE, including prevalent and incident status for multiple common clinical conditions and risk factors, as well as clinical parameters and intermediate biomarkers. The results of PheWAS can be used to discover novel relationships between SNPs, phenotypes, and networks of interrelated phenotypes; identify pleiotropy; provide novel mechanistic insights; and foster hypothesis generation. The PAGE network has developed infrastructure to support and perform PheWAS in a high-throughput manner. As implementing the PheWAS approach has presented several challenges, the infrastructure and methodology, as well as insights gained in this project, are presented herein to benefit the larger scientific community.},\n\tauthor = {Pendergrass, S. A. and Brown-Gentry, K. and Dudek, S. M. and Torstenson, E. S. and Ambite, J. L. and Avery, C. L. and Buyske, S. and Cai, C. and Fesinmeyer, M. D. and Haiman, C. and Heiss, G. and Hindorff, L. A. and Hsu, C.-N. and Jackson, R. D. and Kooperberg, C. and Le Marchand, L. and Lin, Y. and Matise, T. C. and Moreland, L. and Monroe, K. and Reiner, A. P. and Wallace, R. and Wilkens, L. R. and Crawford, D. C. and Ritchie, M. D.},\n\tcitation-subset = {IM},\n\tcompleted = {2011-09-29},\n\tcountry = {United States},\n\tdoi = {10.1002/gepi.20589},\n\tissn = {1098-2272},\n\tissn-linking = {0741-0395},\n\tissue = {5},\n\tjournal = {Genetic epidemiology},\n\tkeywords = {Continental Population Groups, genetics; Databases, Genetic; Ethnic Groups, genetics; Genetic Association Studies, statistics & numerical data; Genetic Variation; Genome-Wide Association Study, statistics & numerical data; Humans; Models, Genetic; Models, Statistical; Phenotype; Polymorphism, Single Nucleotide},\n\tmid = {NIHMS293168},\n\tmonth = jul,\n\tnlm-id = {8411723},\n\towner = {NLM},\n\tpages = {410--422},\n\tpmc = {PMC3116446},\n\tpmid = {21594894},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/21594894/},\n\n\tpubmodel = {Print-Electronic},\n\tpubstate = {ppublish},\n\trevised = {2019-01-08},\n\ttitle = {The use of phenome-wide association studies ({PheWAS}) for exploration of novel genotype-phenotype relationships and pleiotropy discovery.},\n\tvolume = {35},\n\tyear = {2011},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/21594894/},\n\tbdsk-url-2 = {https://doi.org/10.1002/gepi.20589}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Visual integration of results from a large DNA biobank (BioVU) using Synthesis-View.\n \n \n \n \n\n\n \n Pendergrass, S.; Dudek, S. M.; Roden, D. M.; Crawford, D. C.; and Ritchie, M. D.\n\n\n \n\n\n\n Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing,265–275. 2011.\n \n\n\n\n
\n\n\n\n \n \n $\"Visual$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{PendergrassDudekRodenEtAl2011,\n\tabstract = {In this paper, we describe using Synthesis-View, a new method of presenting complex genetic data, to revisit results of a study from the BioVU Vanderbilt DNA databank. BioVU is a biorepository of DNA samples coupled with de-identified electronic medical records (EMR). In the Ritchie et al. study ~10,000 BioVU samples were genotyped for 21 SNPs that were previously associated with 5 diseases: atrial fibrillation, Crohn Disease, multiple sclerosis, rheumatoid arthritis, and type 2 diabetes. In the proof-of-concept study, the 21 tests of association replicated previous findings where sample size provided adequate power. The majority of the BioVU results were originally presented in tabular form. Herein we have revisited the results of this study using Synthesis-View. The Synthesis-View software tool visually synthesizes the results of complex, multi-layered studies that aim to characterize associations between small numbers of single-nucleotide polymorphisms (SNPs) and diseases and/or phenotypes, such as the results of replication and meta-analysis studies. Using Synthesis-View with the data of the Ritchie et al. study and presenting these data in this integrated visual format demonstrates new ways to investigate and interpret these kinds of data. Synthesis-View is freely available for non-commercial research institutions, for full details see https://chgr.mc.vanderbilt.edu/synthesisview.},\n\tauthor = {Pendergrass, Sarah and Dudek, Scott M. and Roden, Dan M. and Crawford, Dana C. and Ritchie, Marylyn D.},\n\tcitation-subset = {IM},\n\tcompleted = {2013-11-13},\n\tcountry = {United States},\n\tdoi = {10.1142/9789814335058_0028},\n\tissn = {2335-6936},\n\tissn-linking = {2335-6928},\n\tjournal = {Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing},\n\tkeywords = {Algorithms; Computational Biology; Computer Graphics; Databases, Nucleic Acid, statistics & numerical data; Disease, genetics; Genetic Association Studies, statistics & numerical data; Genome-Wide Association Study, statistics & numerical data; Humans; Polymorphism, Single Nucleotide; Software},\n\tmid = {NIHMS275250},\n\tnlm-id = {9711271},\n\towner = {NLM},\n\tpages = {265--275},\n\tpii = {9789814335058_0028},\n\tpmc = {PMC3065108},\n\tpmid = {21121054},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/21121054/},\n\n\tpubmodel = {Print},\n\tpubstate = {ppublish},\n\trevised = {2019-11-11},\n\ttitle = {Visual integration of results from a large {DNA} biobank ({BioVU}) using {Synthesis-View}.},\n\tyear = {2011},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/21121054/},\n\tbdsk-url-2 = {https://doi.org/10.1142/9789814335058_0028}}\n\n

\n\n\n\n\n\n

\n\n

\n \n 2010\n \n \n (2)\n \n \n

\n \n \n

\n \n\n \n \n \n \n \n \n Synthesis-View: visualization and interpretation of SNP association results for multi-cohort, multi-phenotype data and meta-analysis.\n \n \n \n \n\n\n \n Pendergrass, S. A.; Dudek, S. M.; Crawford, D. C.; and Ritchie, M. D.\n\n\n \n\n\n\n BioData mining, 3: 10. December 2010.\n \n\n\n\n
\n\n\n\n \n \n $\"Synthesis-View:$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

@article{PendergrassDudekCrawfordEtAl2010,\n\tabstract = {Initial genome-wide association study ({GWAS}) discoveries are being further explored through the use of large cohorts across multiple and diverse populations involving meta-analyses within large consortia and networks. Many of the additional studies characterize less than 100 single nucleotide polymorphisms (SNPs), often include multiple and correlated phenotypic measurements, and can include data from multiple-sites, multiple-studies, as well as multiple race/ethnicities. New approaches for visualizing resultant data are necessary in order to fully interpret results and obtain a broad view of the trends between DNA variation and phenotypes, as well as provide information on specific {SNP} and phenotype relationships. The Synthesis-View software tool was designed to visually synthesize the results of the aforementioned types of studies. Presented herein are multiple examples of the ways Synthesis-View can be used to report results from association studies of DNA variation and phenotypes, including the visual integration of p-values or other metrics of significance, allele frequencies, sample sizes, effect size, and direction of effect. To truly allow a user to visually integrate multiple pieces of information typical of a genetic association study, innovative views are needed to integrate multiple pieces of information. As a result, we have created "Synthesis-View" software for the visualization of genotype-phenotype association data in multiple cohorts. Synthesis-View is freely available for non-commercial research institutions, for full details see https://chgr.mc.vanderbilt.edu/synthesisview.},\n\tauthor = {Pendergrass, Sarah A. and Dudek, Scott M. and Crawford, Dana C. and Ritchie, Marylyn D.},\n\tcompleted = {2011-07-14},\n\tcountry = {England},\n\tdoi = {10.1186/1756-0381-3-10},\n\tissn = {1756-0381},\n\tissn-linking = {1756-0381},\n\tjournal = {BioData mining},\n\tmonth = dec,\n\tnlm-id = {101319161},\n\towner = {NLM},\n\tpages = {10},\n\tpii = {1756-0381-3-10},\n\tpmc = {PMC3012023},\n\tpmid = {21162740},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/21162740/},\n\n\tpubmodel = {Electronic},\n\tpubstate = {epublish},\n\trevised = {2018-11-13},\n\ttitle = {{Synthesis-View}: visualization and interpretation of {SNP} association results for multi-cohort, multi-phenotype data and meta-analysis.},\n\tvolume = {3},\n\tyear = {2010},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/21162740/},\n\tbdsk-url-2 = {https://doi.org/10.1186/1756-0381-3-10}}\n\n

\n\n\n\n\n\n

\n\n\n

\n \n\n \n \n \n \n \n \n Consistent association of type 2 diabetes risk variants found in Europeans in diverse racial and ethnic groups.\n \n \n \n \n\n\n \n Waters, K. M.; Stram, D. O.; Hassanein, M. T.; Le Marchand, L.; Wilkens, L. R.; Maskarinec, G.; Monroe, K. R.; Kolonel, L. N.; Altshuler, D.; Henderson, B. E.; and Haiman, C. A.\n\n\n \n\n\n\n PLoS genetics, 6. August 2010.\n \n\n\n\n
\n\n\n\n \n \n $\"Consistent$ Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

@article{WatersStramHassaneinEtAl2010,\n\tabstract = {It has been recently hypothesized that many of the signals detected in genome-wide association studies ({GWAS}) to T2D and other diseases, despite being observed to common variants, might in fact result from causal mutations that are rare. One prediction of this hypothesis is that the allelic associations should be population-specific, as the causal mutations arose after the migrations that established different populations around the world. We selected 19 common variants found to be reproducibly associated to T2D risk in European populations and studied them in a large multiethnic case-control study (6,142 cases and 7,403 controls) among men and women from 5 racial/ethnic groups (European Americans, African Americans, Latinos, Japanese Americans, and Native Hawaiians). In analysis pooled across ethnic groups, the allelic associations were in the same direction as the original report for all 19 variants, and 14 of the 19 were significantly associated with risk. In summing the number of risk alleles for each individual, the per-allele associations were highly statistically significant (P<10(-4)) and similar in all populations (odds ratios 1.09-1.12) except in Japanese Americans the estimated effect per allele was larger than in the other populations (1.20; P(het) = 3.8×10(-4)). We did not observe ethnic differences in the distribution of risk that would explain the increased prevalence of type 2 diabetes in these groups as compared to European Americans. The consistency of allelic associations in diverse racial/ethnic groups is not predicted under the hypothesis of Goldstein regarding "synthetic associations" of rare mutations in T2D.},\n\tauthor = {Waters, Kevin M. and Stram, Daniel O. and Hassanein, Mohamed T. and Le Marchand, Lo{\\"\\i}c and Wilkens, Lynne R. and Maskarinec, Gertraud and Monroe, Kristine R. and Kolonel, Laurence N. and Altshuler, David and Henderson, Brian E. and Haiman, Christopher A.},\n\tcitation-subset = {IM},\n\tcompleted = {2010-12-28},\n\tcountry = {United States},\n\tdoi = {10.1371/journal.pgen.1001078},\n\tissn = {1553-7404},\n\tissn-linking = {1553-7390},\n\tissue = {8},\n\tjournal = {PLoS genetics},\n\tkeywords = {Adult; Aged; Alleles; Case-Control Studies; Diabetes Mellitus, Type 2, ethnology, genetics; Ethnic Groups; Europe, ethnology; Female; Genetic Predisposition to Disease; Genetic Variation; Genome-Wide Association Study; Humans; Male; Middle Aged},\n\tmonth = aug,\n\tnlm-id = {101239074},\n\towner = {NLM},\n\tpii = {e1001078},\n\tpmc = {PMC2928808},\n\tpmid = {20865176},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/20865176/},\n\n\tpubmodel = {Electronic},\n\tpubstate = {epublish},\n\trevised = {2018-11-13},\n\ttitle = {Consistent association of type 2 diabetes risk variants found in {Europeans} in diverse racial and ethnic groups.},\n\tvolume = {6},\n\tyear = {2010},\n\tbdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/20865176/},\n\tbdsk-url-2 = {https://doi.org/10.1371/journal.pgen.1001078}}\n\n

\n\n\n\n\n\n

\n\n\n\n\n

\n\n\n \n\n \n \n \n \n\n