An approach to identify gene-environment interactions and reveal new biological insight in complex traits

Xiaofeng Zhu (), Yihe Yang, Noah Lorincz-Comi, Gen Li, Amy R. Bentley, Paul S. de Vries, Michael Brown, Alanna C. Morrison, Charles N. Rotimi, W. James Gauderman, Dabeeru C. Rao and Hugues Aschard
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Xiaofeng Zhu: Case Western Reserve University
Yihe Yang: Case Western Reserve University
Noah Lorincz-Comi: Case Western Reserve University
Gen Li: Case Western Reserve University
Amy R. Bentley: National Institutes of Health
Paul S. de Vries: The University of Texas Health Science Center at Houston
Michael Brown: The University of Texas Health Science Center at Houston
Alanna C. Morrison: The University of Texas Health Science Center at Houston
Charles N. Rotimi: National Institutes of Health
W. James Gauderman: University of Southern California
Dabeeru C. Rao: Washington University School of Medicine
Hugues Aschard: Department of Computational Biology

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the Gene-Lifestyle Interactions Working Group within the Cohorts for Heart and Aging Research in Genetic Epidemiology Consortium has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identify and confirm 5 loci (6 independent signals) interacted with either cigarette smoking or alcohol consumption for serum lipids, and empirically demonstrate that interaction and mediation are the major contributors to genetic effect size heterogeneity across populations. The estimated lower bound of the interaction and environmentally mediated heritability is significant (P

Date: 2024
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DOI: 10.1038/s41467-024-47806-3

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