Genome-by-Trauma Exposure Interactions in Adults With Depression in the UK Biobank

IMPORTANCE: 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 gene-environment interactions with trauma exposure using genome-wide variants (single-nucleotide variations [SNVs]) or polygenic scores, both typically capturing less than 3% of phenotypic risk variance. OBJECTIVE: To reexamine genome-by-trauma interaction associations using genetic measures using all available genotyped data and thus, maximizing accounted variance. DESIGN, SETTING, AND PARTICIPANTS: The UK Biobank study was conducted from April 2007 to May 1, 2016 (follow-up mental health questionnaire). The current study used available cross-sectional genomic and trauma exposure data from UK Biobank. Participants who completed the mental health questionnaire and had available genetic, trauma experience, depressive symptoms, and/or neuroticism information were included. Data were analyzed from April 1 to August 30, 2021. EXPOSURES: Trauma and genome-by-trauma exposure interactions. MAIN OUTCOMES AND MEASURES: Measures of self-reported depression, neuroticism, and trauma exposure with whole-genome SNV data are available from the UK Biobank study. Here, a mixed-model statistical approach using genetic, trauma exposure, and genome-by-trauma exposure interaction similarity matrices was used to explore sources of variation in depression and neuroticism. RESULTS: Analyses were conducted on 148 129 participants (mean [SD] age, 56 [7] years) of which 76 995 were female (52.0%). The study approach estimated the heritability (SE) of MDD to be approximately 0.160 (0.016). Subtypes of self-reported trauma exposure (catastrophic, adult, childhood, and full trauma) accounted for a significant proportion of the variance of MDD, with heritability (SE) ranging from 0.056 (0.013) to 0.176 (0.025). The proportion of MDD risk variance accounted for by significant genome-by-trauma interaction revealed estimates (SD) ranging from 0.074 (0.006) to 0.201 (0.009). Results from sex-specific analyses found genome-by-trauma interaction variance estimates approximately 5-fold greater for MDD in male participants (0.441 [0.018]) than in female participants (0.086 [0.009]). CONCLUSIONS AND RELEVANCE: This cross-sectional study used an approach combining all genome-wide SNV data when exploring genome-by-trauma interactions in individuals with MDD; findings suggest that such interactions were associated with depression manifestation. Genome-by-trauma interaction accounts for greater trait variance in male individuals, which points to potential differences in depression etiology between the sexes. The methodology used in this study can be extrapolated to other environmental factors to identify modifiable risk environments and at-risk groups to target with interventions

Gene environment interplay in depression

Major depressive disorder (MDD) is a common psychiatric disorder and one of the leading causes of disability worldwide. MDD is moderately heritable (~30­40%), suggesting both genetic and environmental effects are influential. Several strands of evidence point to the possible presence of gene­environment correlations and gene­environment interaction effects in MDD, although findings to date have been relatively inconsistent. Recently, research using available genetic and environmental data on biological parents and offspring (trios), have shown that parental genetic nurturing effects are detectable using polygenic scores (PGSs) in more heritable traits such as educational attainment. Research findings also point to potential gene­by­trauma exposure interaction effects involved in MDD. It is evident that data limitations may result in power issues, confounding effects and biases, which impact reliable and accurate quantification of these effects, highlighting the need for methods that maximise statistical power and minimise bias and confounding when exploring these effects. This thesis aims to adapt existing statistical frameworks and use of data to explore gene­environment interplay effects, which are robust to the limitations of the available data. Here, two large population­scale datasets, the UK Biobank (N~150,000) and Generation Scotland: Scottish Family Health Study (N~2680 trios), were utilised to explore genome­by­trauma exposure interaction effects in depression, as well as parental genetic nurturing effects in a range of traits including MDD. The research aims of this thesis included (1) implementing models exploring parental genetic nurturing effects using available trio PGSs; (2) expanding these models to explore mechanisms of genetic nurturing effects with available parental phenotypic data, both addressed in chapter 2. Here, the quantification of parental genetic nurturing effects using trio PGSs was found to be reliable and robust to data limitations. However, expanding these models by including parental phenotypes resulted in confounded effects. Simulation analyses demonstrated that the confounding was induced by power issues associated with PGSs, highlighting the need for improved measures of genetic variance. The final research aim (3) was to explore genome­by­trauma exposure interaction effects using relationship matrices capturing genetic, trauma exposure and genome­by­trauma exposure interaction similarity between participants. Genomic relationship matrices utilised all available genetic data, and thus, served as an improved representation of genetic variance. Environmental relationship matrices utilised principal components, capturing underlying dimensions of trauma exposure. A substantial proportion of MDD variance was found to be attributed to genetic, trauma exposure and genome-­by-trauma interaction effects. However, little insight was inferred regarding the specificity and direction of trauma exposure involved in MDD manifestation, due to the difficulty in interpreting the underlying dimensions of trauma exposure. The two studies provide a strong rationale for the use of improved measures of genetic and environmental components of MDD. Specifically, findings from chapter 2 highlight the need for measures that capture a substantial proportion of genetic variance to explore these complex gene­environment interplay effects. Chapter 3 results demonstrated how leveraging all available genetic data can uncover substantial effects that were previously missed. Evaluations of study designs highlight how future work can incorporate omics (e.g. methylation) data to improve measures of environmental factors, which would aid translational interpretation of results obtained from these models. Methylation data could help identify additional trait associated genetic loci as well as their mode of action, and hence, specific drug targets; which may not have been previously identified by traditional analyses such as genome­wide association studies (GWAS) not containing the relevant genetic variants