Exploring polygenic-environment and residual-environment interactions for depressive symptoms within the UK Biobank

Abstract

Substantial advances have been made in identifying genetic contributions to depression, but little is known about how the effect of genes can be modulated by the environment, creating a gene-environment interaction. Using multivariate reaction norm models (MRNMs) within the UK Biobank (N=61294-91644), we investigate whether the polygenic and residual variation of depressive symptoms are modulated by 25 a-priori selected covariate traits: 12 environmental variables, 5 biomarkers and polygenic risk scores for 8 mental health disorders. MRNMs provide unbiased polygenic-covariate interaction estimates for a quantitative trait by controlling for outcome-covariate correlations and residual-covariate interactions. Of the 25 selected covariates, 11 significantly modulate depressive symptoms, but no single interaction explains a large proportion of phenotypic variation. Results are dominated by residual-covariate interactions, suggesting that covariate traits (including neuroticism, childhood trauma and BMI) typically interact with unmodelled variables, rather than a genome-wide polygenic score, to influence depressive symptoms. Only average sleep duration has a polygenic-covariate interaction explaining a demonstrably non-zero proportion of the variability in depressive symptoms. This effect is small, accounting for only 1.22% (95% CI [0.54,1.89]) of variation. The presence of an interaction highlights a specific focus for intervention, but the negative results here indicate a limited contribution from polygenic-environment interactions.