Distinct explanations underlie gene-environment interactions in the UK Biobank

The role of gene-environment (GxE) interaction in disease and complex trait architectures is widely hypothesized, but currently unknown. Here, we apply three statistical approaches to quantify and distinguish three different types of GxE interaction for a given disease/trait and E variable. First, we detect locus-specific GxE interaction by testing for genetic correlation ( rg ) < 1 across E bins. Second, we detect genome-wide effects of the E variable on genetic variance by leveraging polygenic risk scores (PRS) to test for significant PRSxE in a regression of phenotypes on PRS, E, and PRSxE, together with differences in SNP-heritability across E bins. Third, we detect genome-wide proportional amplification of genetic and environmental effects as a function of the E variable by testing for significant PRSxE with no differences in SNP-heritability across E bins. Simulations show that these approaches achieve high sensitivity and specificity in distinguishing these three GxE scenarios. We applied our framework to 33 UK Biobank diseases/traits (average N =325K) and 10 E variables spanning lifestyle, diet, and other environmental exposures. First, we identified 19 trait-E pairs with rg significantly < 1 (FDR<5%) (average rg =0.95); for example, white blood cell count had rg =0.95 (s.e. 0.01) between smokers and non-smokers. Second, we identified 28 trait-E pairs with significant PRSxE and significant SNP-heritability differences across E bins; for example, type 2 diabetes had a significant PRSxE for alcohol consumption (P=1e-13) with 4.2x larger SNP-heritability in the largest versus smallest quintiles of alcohol consumption (P<1e-16). Third, we identified 15 trait-E pairs with significant PRSxE with no SNP-heritability differences across E bins; for example, triglyceride levels had a significant PRSxE effect for composite diet score (P=4e-5) with no SNP-heritability differences. Analyses using biological sex as the E variable produced additional significant findings in each of the three scenarios. Overall, we infer a substantial contribution of GxE and GxSex effects to disease and complex trait variance. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was conducted using the UK Biobank resource under application no. 16549 and funded by National Institutes of Health (NIH) grants R01 MH101244, R37 MH107649 and R01 HG006399. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in the manuscript/