Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids. Shin, S., Petersen, A., Wahl, S., Zhai, G., Römisch-Margl, W., Small, K. S, Döring, A., Kato, B. S, Peters, A., Grundberg, E., Prehn, C., Wang-Sattler, R., Wichmann, H., de Angelis, M., Illig, T., Adamski, J., Deloukas, P., Spector, T. D, Suhre, K., Gieger, C., & Soranzo, N. Genome Medicine, 6(3):25, 2014. ![Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Background: Emerging technologies based on mass spectrometry or nuclear magnetic resonance enable the monitoring of hundreds of small metabolites from tissues or body fluids. Profiling of metabolites can help elucidate causal pathways linking established genetic variants to known disease risk factors such as blood lipid traits. Methods: We applied statistical methodology to dissect causal relationships between single nucleotide polymorphisms, metabolite concentrations, and serum lipid traits, focusing on 95 genetic loci reproducibly associated with the four main serum lipids (total-, low-density lipoprotein-, and high-density lipoprotein- cholesterol and triglycerides). The dataset used included 2,973 individuals from two independent population-based cohorts with data for 151 small molecule metabolites and four main serum lipids. Three statistical approaches, namely conditional analysis, Mendelian randomization, and structural equation modeling, were compared to investigate causal relationship at sets of a single nucleotide polymorphism, a metabolite, and a lipid trait associated with one another. Results: A subset of three lipid-associated loci (FADS1, GCKR, and LPA) have a statistically significant association with at least one main lipid and one metabolite concentration in our data, defining a total of 38 cross-associated sets of a single nucleotide polymorphism, a metabolite and a lipid trait. Structural equation modeling provided sufficient discrimination to indicate that the association of a single nucleotide polymorphism with a lipid trait was mediated through a metabolite at 15 of the 38 sets, and involving variants at the FADS1 and GCKR loci. Conclusions: These data provide a framework for evaluating the causal role of components of the metabolome (or other intermediate factors) in mediating the association between established genetic variants and diseases or traits.
@article{shin_interrogating_2014-1,
title = {Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids},
volume = {6},
issn = {1756-994X},
url = {http://genomemedicine.biomedcentral.com/articles/10.1186/gm542},
doi = {10.1186/gm542},
abstract = {Background: Emerging technologies based on mass spectrometry or nuclear magnetic resonance enable the monitoring of hundreds of small metabolites from tissues or body fluids. Profiling of metabolites can help elucidate causal pathways linking established genetic variants to known disease risk factors such as blood lipid traits. Methods: We applied statistical methodology to dissect causal relationships between single nucleotide polymorphisms, metabolite concentrations, and serum lipid traits, focusing on 95 genetic loci reproducibly associated with the four main serum lipids (total-, low-density lipoprotein-, and high-density lipoprotein- cholesterol and triglycerides). The dataset used included 2,973 individuals from two independent population-based cohorts with data for 151 small molecule metabolites and four main serum lipids. Three statistical approaches, namely conditional analysis, Mendelian randomization, and structural equation modeling, were compared to investigate causal relationship at sets of a single nucleotide polymorphism, a metabolite, and a lipid trait associated with one another. Results: A subset of three lipid-associated loci (FADS1, GCKR, and LPA) have a statistically significant association with at least one main lipid and one metabolite concentration in our data, defining a total of 38 cross-associated sets of a single nucleotide polymorphism, a metabolite and a lipid trait. Structural equation modeling provided sufficient discrimination to indicate that the association of a single nucleotide polymorphism with a lipid trait was mediated through a metabolite at 15 of the 38 sets, and involving variants at the FADS1 and GCKR loci. Conclusions: These data provide a framework for evaluating the causal role of components of the metabolome (or other intermediate factors) in mediating the association between established genetic variants and diseases or traits.},
language = {en},
number = {3},
urldate = {2019-05-02},
journal = {Genome Medicine},
author = {Shin, So-Youn and Petersen, Ann-Kristin and Wahl, Simone and Zhai, Guangju and Römisch-Margl, Werner and Small, Kerrin S and Döring, Angela and Kato, Bernet S and Peters, Annette and Grundberg, Elin and Prehn, Cornelia and Wang-Sattler, Rui and Wichmann, H-Erich and de Angelis, Martin and Illig, Thomas and Adamski, Jerzy and Deloukas, Panos and Spector, Tim D and Suhre, Karsten and Gieger, Christian and Soranzo, Nicole},
year = {2014},
pages = {25},
file = {Shin et al. - 2014 - Interrogating causal pathways linking genetic vari.pdf:/Users/neil.hawkins/Zotero/storage/64WCWJLB/Shin et al. - 2014 - Interrogating causal pathways linking genetic vari.pdf:application/pdf},
}
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Profiling of metabolites can help elucidate causal pathways linking established genetic variants to known disease risk factors such as blood lipid traits. Methods: We applied statistical methodology to dissect causal relationships between single nucleotide polymorphisms, metabolite concentrations, and serum lipid traits, focusing on 95 genetic loci reproducibly associated with the four main serum lipids (total-, low-density lipoprotein-, and high-density lipoprotein- cholesterol and triglycerides). The dataset used included 2,973 individuals from two independent population-based cohorts with data for 151 small molecule metabolites and four main serum lipids. Three statistical approaches, namely conditional analysis, Mendelian randomization, and structural equation modeling, were compared to investigate causal relationship at sets of a single nucleotide polymorphism, a metabolite, and a lipid trait associated with one another. Results: A subset of three lipid-associated loci (FADS1, GCKR, and LPA) have a statistically significant association with at least one main lipid and one metabolite concentration in our data, defining a total of 38 cross-associated sets of a single nucleotide polymorphism, a metabolite and a lipid trait. Structural equation modeling provided sufficient discrimination to indicate that the association of a single nucleotide polymorphism with a lipid trait was mediated through a metabolite at 15 of the 38 sets, and involving variants at the FADS1 and GCKR loci. Conclusions: These data provide a framework for evaluating the causal role of components of the metabolome (or other intermediate factors) in mediating the association between established genetic variants and diseases or traits.","language":"en","number":"3","urldate":"2019-05-02","journal":"Genome Medicine","author":[{"propositions":[],"lastnames":["Shin"],"firstnames":["So-Youn"],"suffixes":[]},{"propositions":[],"lastnames":["Petersen"],"firstnames":["Ann-Kristin"],"suffixes":[]},{"propositions":[],"lastnames":["Wahl"],"firstnames":["Simone"],"suffixes":[]},{"propositions":[],"lastnames":["Zhai"],"firstnames":["Guangju"],"suffixes":[]},{"propositions":[],"lastnames":["Römisch-Margl"],"firstnames":["Werner"],"suffixes":[]},{"propositions":[],"lastnames":["Small"],"firstnames":["Kerrin","S"],"suffixes":[]},{"propositions":[],"lastnames":["Döring"],"firstnames":["Angela"],"suffixes":[]},{"propositions":[],"lastnames":["Kato"],"firstnames":["Bernet","S"],"suffixes":[]},{"propositions":[],"lastnames":["Peters"],"firstnames":["Annette"],"suffixes":[]},{"propositions":[],"lastnames":["Grundberg"],"firstnames":["Elin"],"suffixes":[]},{"propositions":[],"lastnames":["Prehn"],"firstnames":["Cornelia"],"suffixes":[]},{"propositions":[],"lastnames":["Wang-Sattler"],"firstnames":["Rui"],"suffixes":[]},{"propositions":[],"lastnames":["Wichmann"],"firstnames":["H-Erich"],"suffixes":[]},{"propositions":["de"],"lastnames":["Angelis"],"firstnames":["Martin"],"suffixes":[]},{"propositions":[],"lastnames":["Illig"],"firstnames":["Thomas"],"suffixes":[]},{"propositions":[],"lastnames":["Adamski"],"firstnames":["Jerzy"],"suffixes":[]},{"propositions":[],"lastnames":["Deloukas"],"firstnames":["Panos"],"suffixes":[]},{"propositions":[],"lastnames":["Spector"],"firstnames":["Tim","D"],"suffixes":[]},{"propositions":[],"lastnames":["Suhre"],"firstnames":["Karsten"],"suffixes":[]},{"propositions":[],"lastnames":["Gieger"],"firstnames":["Christian"],"suffixes":[]},{"propositions":[],"lastnames":["Soranzo"],"firstnames":["Nicole"],"suffixes":[]}],"year":"2014","pages":"25","file":"Shin et al. - 2014 - Interrogating causal pathways linking genetic vari.pdf:/Users/neil.hawkins/Zotero/storage/64WCWJLB/Shin et al. - 2014 - Interrogating causal pathways linking genetic vari.pdf:application/pdf","bibtex":"@article{shin_interrogating_2014-1,\n\ttitle = {Interrogating causal pathways linking genetic variants, small molecule metabolites, and circulating lipids},\n\tvolume = {6},\n\tissn = {1756-994X},\n\turl = {http://genomemedicine.biomedcentral.com/articles/10.1186/gm542},\n\tdoi = {10.1186/gm542},\n\tabstract = {Background: Emerging technologies based on mass spectrometry or nuclear magnetic resonance enable the monitoring of hundreds of small metabolites from tissues or body fluids. Profiling of metabolites can help elucidate causal pathways linking established genetic variants to known disease risk factors such as blood lipid traits. Methods: We applied statistical methodology to dissect causal relationships between single nucleotide polymorphisms, metabolite concentrations, and serum lipid traits, focusing on 95 genetic loci reproducibly associated with the four main serum lipids (total-, low-density lipoprotein-, and high-density lipoprotein- cholesterol and triglycerides). The dataset used included 2,973 individuals from two independent population-based cohorts with data for 151 small molecule metabolites and four main serum lipids. Three statistical approaches, namely conditional analysis, Mendelian randomization, and structural equation modeling, were compared to investigate causal relationship at sets of a single nucleotide polymorphism, a metabolite, and a lipid trait associated with one another. Results: A subset of three lipid-associated loci (FADS1, GCKR, and LPA) have a statistically significant association with at least one main lipid and one metabolite concentration in our data, defining a total of 38 cross-associated sets of a single nucleotide polymorphism, a metabolite and a lipid trait. Structural equation modeling provided sufficient discrimination to indicate that the association of a single nucleotide polymorphism with a lipid trait was mediated through a metabolite at 15 of the 38 sets, and involving variants at the FADS1 and GCKR loci. Conclusions: These data provide a framework for evaluating the causal role of components of the metabolome (or other intermediate factors) in mediating the association between established genetic variants and diseases or traits.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2019-05-02},\n\tjournal = {Genome Medicine},\n\tauthor = {Shin, So-Youn and Petersen, Ann-Kristin and Wahl, Simone and Zhai, Guangju and Römisch-Margl, Werner and Small, Kerrin S and Döring, Angela and Kato, Bernet S and Peters, Annette and Grundberg, Elin and Prehn, Cornelia and Wang-Sattler, Rui and Wichmann, H-Erich and de Angelis, Martin and Illig, Thomas and Adamski, Jerzy and Deloukas, Panos and Spector, Tim D and Suhre, Karsten and Gieger, Christian and Soranzo, Nicole},\n\tyear = {2014},\n\tpages = {25},\n\tfile = {Shin et al. - 2014 - Interrogating causal pathways linking genetic vari.pdf:/Users/neil.hawkins/Zotero/storage/64WCWJLB/Shin et al. - 2014 - Interrogating causal pathways linking genetic vari.pdf:application/pdf},\n}\n\n","author_short":["Shin, S.","Petersen, A.","Wahl, S.","Zhai, G.","Römisch-Margl, W.","Small, K. 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