Gene-lifestyle interactions in the genomics of human complex traits.

Autor: Laville V; Department of Computational Biology, Institut Pasteur, Université de Paris, F-75015, Paris, France. vincent.laville@pasteur.fr., Majarian T; Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA., Sung YJ; Division of Biostatistics, Washington University, St. Louis, MO, 63110, USA., Schwander K; Division of Biostatistics, Washington University, St. Louis, MO, 63110, USA., Feitosa MF; Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63108-221, USA., Chasman DI; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02215, USA., Bentley AR; Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA., Rotimi CN; Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA., Cupples LA; Department of Biostatistics, Boston University School of Public Health, Boston, MA, 2118, USA.; Framingham Heart Study, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20982, USA., de Vries PS; Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA., Brown MR; Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA., Morrison AC; Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA., Kraja AT; Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63108-221, USA., Province M; Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, 63108-221, USA., Gu CC; Division of Biostatistics, Washington University, St. Louis, MO, 63110, USA., Gauderman WJ; Division of Biostatistics, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, 90032, USA., Rao DC; Division of Biostatistics, Washington University, St. Louis, MO, 63110, USA., Manning AK; Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, 02114, USA.; Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA., Aschard H; Department of Computational Biology, Institut Pasteur, Université de Paris, F-75015, Paris, France. hugues.aschard@pasteur.fr.; Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA. hugues.aschard@pasteur.fr.
Jazyk: angličtina
Zdroj: European journal of human genetics : EJHG [Eur J Hum Genet] 2022 Jun; Vol. 30 (6), pp. 730-739. Date of Electronic Publication: 2022 Mar 22.
DOI: 10.1038/s41431-022-01045-6
Abstrakt: 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.
(© 2022. The Author(s).)
Databáze: MEDLINE
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