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

Jonathan Sulc,Zoltán Kutalik,Felix Günther,Matthew R Robinson,Andrew R Wood,Iris M Heid,Ninon Mounier,Timothy M Frayling,Thomas Winkler

doi:10.1038/s41467-020-15107-0

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).

Highlights

The growing sample size of genome-wide association studies has facilitated the discovery of gene-environment interactions (GxE)
Many environmental factors have been shown to interact with the genetic risk score for body mass index (BMI)[12], such as physical activity[13], alcohol consumption[14], socio-economic status[15], sugary drink consumption[16], certain types of diet[17], etc
We have proposed a maximum likelihood-based method to infer the extent of the total G × E interaction between a genetic risk score and the combination of all possible continuous environmental variables

Summary

Introduction

The growing sample size of genome-wide association studies has facilitated the discovery of gene-environment interactions (GxE). Many environmental factors have been shown to interact with the genetic risk score for body mass index (BMI)[12], such as physical activity[13], alcohol consumption[14], socio-economic status (as measured by the Townsend deprivation index)[15], sugary drink consumption[16], certain types of diet[17], etc Many of these environmental variables are correlated with one another, and little is known about how these interactions relate to each other[14]. These methods were not designed to assess the extent of the interaction strength, and are mostly restricted to single SNP analysis These studies do not seek to account for general scale effects that are not specific to the genetic markers. The code implementing the algorithm is available in R and Matlab (https://github.com/zkutalik/GRSxE_software)

Objectives

Methods

Results

Conclusion