Quantification of the overall contribution of gene-environment interaction for obesity-related traits

Jonathan Sulc, Ninon Mounier, Felix Günther, Thomas Winkler, Andrew R. Wood, Timothy M. Frayling, Iris M. Heid, Matthew R. Robinson and Zoltán Kutalik ()
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Jonathan Sulc: University Center for Primary Care and Public Health, University of Lausanne
Ninon Mounier: University Center for Primary Care and Public Health, University of Lausanne
Felix Günther: University of Regensburg
Thomas Winkler: University of Regensburg
Andrew R. Wood: University of Exeter Medical School, University of Exeter
Timothy M. Frayling: University of Exeter Medical School, University of Exeter
Iris M. Heid: University of Regensburg
Matthew R. Robinson: University of Lausanne
Zoltán Kutalik: University Center for Primary Care and Public Health, University of Lausanne

Nature Communications, 2020, vol. 11, issue 1, 1-13

Abstract: Abstract The growing sample size of genome-wide association studies has facilitated the discovery of gene-environment interactions (GxE). Here we propose a maximum likelihood method to estimate the contribution of GxE to continuous traits taking into account all interacting environmental variables, without the need to measure any. Extensive simulations demonstrate that our method provides unbiased interaction estimates and excellent coverage. We also offer strategies to distinguish specific GxE from general scale effects. Applying our method to 32 traits in the UK Biobank reveals that while the genetic risk score (GRS) of 376 variants explains 5.2% of body mass index (BMI) variance, GRSxE explains an additional 1.9%. Nevertheless, this interaction holds for any variable with identical correlation to BMI as the GRS, hence may not be GRS-specific. Still, we observe that the global contribution of specific GRSxE to complex traits is substantial for nine obesity-related measures (including leg impedance and trunk fat-free mass).

Date: 2020
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DOI: 10.1038/s41467-020-15107-0

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