Genetic analyses of diverse populations improves discovery for complex traits. 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., & Carlson, C. S. Nature, 570:514–518, June, 2019.
Genetic analyses of diverse populations improves discovery for complex traits. [link]Paper  doi  abstract   bibtex   8 downloads  
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.
@article{WojcikGraffNishimuraEtAl2019,
	abstract = {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.},
	author = {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.},
	citation-subset = {IM},
	completed = {2020-03-04},
	country = {England},
	doi = {10.1038/s41586-019-1310-4},
	issn = {1476-4687},
	issn-linking = {0028-0836},
	issue = {7762},
	journal = {Nature},
	keywords = {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},
	mid = {NIHMS1050617},
	month = jun,
	nlm-id = {0410462},
	owner = {NLM},
	pages = {514--518},
	pii = {10.1038/s41586-019-1310-4},
	pmc = {PMC6785182},
	pmid = {31217584},
	url = {https://pubmed.ncbi.nlm.nih.gov/31217584/},

	pubmodel = {Print-Electronic},
	pubstate = {ppublish},
	revised = {2020-09-29},
	title = {Genetic analyses of diverse populations improves discovery for complex traits.},
	volume = {570},
	year = {2019},
	bdsk-url-1 = {https://pubmed.ncbi.nlm.nih.gov/31217584/},
	bdsk-url-2 = {https://doi.org/10.1038/s41586-019-1310-4}}
Downloads: 8
About
Documentation
Keywords
Search
Users
Terms and Conditions of Use
Privacy Policy
Sitemap
© BibBase.org 2021