Genome-by-Trauma Exposure Interaction Effects in Depression

Abstract

Background Self-reported trauma exposure has consistently been found to be a risk factor for Major Depressive Disorder (MDD) and several studies have reported interactions with genetic liability. To date, most studies have examined interaction effects with trauma exposure using genome-wide variants (single nucleotide polymorphisms SNPs) or polygenic scores, both typically capturing less than 3% of phenotypic risk variance. We sought to re-examine genome-by-trauma interaction effects using genetic measures utilising all available genotyped data and thus, maximising accounted variance. Methods Measures of self-reported depression, neuroticism and trauma exposure for 148 129 participants with whole genome SNP data are available from the UK Biobank study. Here, we used a mixed-model statistical approach utilising genetic, trauma exposure and genome-by-trauma exposure interaction similarity matrices to explore sources of variation in depression and neuroticism. Findings Our approach estimated the heritability of MDD to be approximately 0.160 [SE 0.016]. Subtypes of self-reported trauma exposure (catastrophic, adult, childhood and full trauma) accounted for a significant proportion of the variance of each trait, ranging from 0.056 [SE 0.013] to 0.176 [SE 0.025]. The proportion of MDD risk variance accounted for by significant genome-by-trauma interaction ranged from 0.074 [SE 0.006] to 0.201 [SE 0.009]. Results from sex-specific analyses found genome-by-trauma interaction variance estimates approximately 5-fold greater for MDD in males than in females. Interpretation This is the first study to utilise an approach combining all genome-wide SNP data when exploring genome-by-trauma interaction effects in MDD and present evidence that interaction effects are influential in depression manifestation. This effect accounts for greater trait variance within males which points to potential differences in depression aetiology between the sexes. The methodology utilised in this study can be extrapolated to other environmental factors to identify modifiable risk environments and at-risk groups to target with interventions.