Gene-lifestyle interactions in the genomics of human complex traits
The role and biological significance of gene-environment interactions in human traits and diseases remain poorly understood. To address these questions, the CHARGE Gene-Lifestyle Interactions Working Group conducted series of genome-wide interaction studies (GWIS) involving up to 610,475 individuals...
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creator | Laville, Vincent Majarian, Timothy Sung, Yun J Schwander, Karen Feitosa, Mary F Chasman, Daniel I Bentley, Amy R Rotimi, Charles N Cupples, L Adrienne de Vries, Paul S Brown, Michael R Morrison, Alanna C Kraja, Aldi T Province, Mike Gu, C Charles Gauderman, W James Rao, D C Manning, Alisa K Aschard, Hugues |
description | The role and biological significance of gene-environment interactions in human traits and diseases remain poorly understood. To address these questions, the CHARGE Gene-Lifestyle Interactions Working Group conducted series of genome-wide interaction studies (GWIS) involving up to 610,475 individuals across four ancestries for three lipids and four blood pressure traits, while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and G×E interactions across phenotype-exposure-ancestry combinations, and to derive insights on the potential mechanistic underlying G×E through in-silico functional analyses. Our analyses show first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted modest correlation between marginal and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell types. In these analyses, we found multiple instances of potential heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work, we identified potential biases in methods used to jointly meta-analyze genetic and interaction effects. We performed simulations to characterize these limitations and to provide the community with guidelines for future G×E studies. |
doi_str_mv | 10.1038/s41431-022-01045-6 |
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To address these questions, the CHARGE Gene-Lifestyle Interactions Working Group conducted series of genome-wide interaction studies (GWIS) involving up to 610,475 individuals across four ancestries for three lipids and four blood pressure traits, while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and G×E interactions across phenotype-exposure-ancestry combinations, and to derive insights on the potential mechanistic underlying G×E through in-silico functional analyses. Our analyses show first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted modest correlation between marginal and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell types. In these analyses, we found multiple instances of potential heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work, we identified potential biases in methods used to jointly meta-analyze genetic and interaction effects. We performed simulations to characterize these limitations and to provide the community with guidelines for future G×E studies.</description><identifier>ISSN: 1018-4813</identifier><identifier>ISSN: 1476-5438</identifier><identifier>EISSN: 1476-5438</identifier><identifier>DOI: 10.1038/s41431-022-01045-6</identifier><identifier>PMID: 35314805</identifier><language>eng</language><publisher>England: Nature Publishing Group</publisher><subject>Alcohol ; Blood pressure ; Consortia ; Epidemiology ; Epistasis, Genetic ; Gene-Environment Interaction ; Genetics ; Genome-Wide Association Study ; Genomes ; Genomics ; Genotype & phenotype ; Health care ; Heritability ; High density lipoprotein ; Human genetics ; Humans ; Life Sciences ; Life Style ; Lifestyles ; Lipids ; Multifactorial Inheritance ; Phenotype ; Phenotypes ; Phenotypic variations ; Preventive medicine ; Public health ; Quantitative Methods ; Smoking ; Working groups</subject><ispartof>European journal of human genetics : EJHG, 2022-06, Vol.30 (6), p.730-739</ispartof><rights>2022. The Author(s).</rights><rights>The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). 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It also highlighted modest correlation between marginal and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell types. In these analyses, we found multiple instances of potential heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work, we identified potential biases in methods used to jointly meta-analyze genetic and interaction effects. 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interactions in the genomics of human complex traits</title><author>Laville, Vincent ; Majarian, Timothy ; Sung, Yun J ; Schwander, Karen ; Feitosa, Mary F ; Chasman, Daniel I ; Bentley, Amy R ; Rotimi, Charles N ; Cupples, L Adrienne ; de Vries, Paul S ; Brown, Michael R ; Morrison, Alanna C ; Kraja, Aldi T ; Province, Mike ; Gu, C Charles ; Gauderman, W James ; Rao, D C ; Manning, Alisa K ; Aschard, Hugues</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c468t-14d09885f7452c9017c6149558d710f3d662a7b8e56758ba4b9e6875d628f6b63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Alcohol</topic><topic>Blood pressure</topic><topic>Consortia</topic><topic>Epidemiology</topic><topic>Epistasis, Genetic</topic><topic>Gene-Environment Interaction</topic><topic>Genetics</topic><topic>Genome-Wide Association Study</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Genotype & 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To address these questions, the CHARGE Gene-Lifestyle Interactions Working Group conducted series of genome-wide interaction studies (GWIS) involving up to 610,475 individuals across four ancestries for three lipids and four blood pressure traits, while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and G×E interactions across phenotype-exposure-ancestry combinations, and to derive insights on the potential mechanistic underlying G×E through in-silico functional analyses. Our analyses show first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted modest correlation between marginal and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell types. In these analyses, we found multiple instances of potential heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work, we identified potential biases in methods used to jointly meta-analyze genetic and interaction effects. 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subjects | Alcohol Blood pressure Consortia Epidemiology Epistasis, Genetic Gene-Environment Interaction Genetics Genome-Wide Association Study Genomes Genomics Genotype & phenotype Health care Heritability High density lipoprotein Human genetics Humans Life Sciences Life Style Lifestyles Lipids Multifactorial Inheritance Phenotype Phenotypes Phenotypic variations Preventive medicine Public health Quantitative Methods Smoking Working groups |
title | Gene-lifestyle interactions in the genomics of human complex traits |
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