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

Alexandra C. Gillett*, Bradley S. Jermy, Sang Hong Lee, Oliver Pain, David M. Howard, Saskia P. Hagenaars, Ken B. Hanscombe, Jonathan R.I. Coleman, Cathryn M. Lewis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
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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 variance components of depressive symptoms are modulated by 17 a priori selected covariate traits—12 environmental variables and 5 biomarkers. MRNMs, a mixed-effects modelling approach, provide unbiased polygenic–covariate interaction estimates for a quantitative trait by controlling for outcome-covariate correlations and residual–covariate interactions. A continuous depressive symptom variable was the outcome in 17 MRNMs—one for each covariate trait. Each MRNM had a fixed-effects model (fixed effects included the covariate trait, demographic variables, and principal components) and a random effects model (where polygenic–covariate and residual–covariate interactions are modelled). Of the 17 selected covariates, 11 significantly modulate deviations in depressive symptoms through the modelled interactions, 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 component, to influence depressive symptoms. Only average sleep duration has a polygenic–covariate interaction explaining a demonstrably nonzero proportion of the variability in depressive symptoms. This effect is small, accounting for only 1.22% (95% confidence interval: [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.

Original languageEnglish
Pages (from-to)219-233
Number of pages15
JournalGenetic Epidemiology
Issue number5-6
Early online date19 Apr 2022
Publication statusPublished - 1 Jul 2022


  • depressive symptoms
  • genotype–environment interaction
  • multivariate reaction norm model
  • residual–environment interaction


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