Identifying genome-by-childhood trauma interactions for depression using a forest-based approach in the UK Biobank...

Depression is shaped by both genetic and environmental factors, but genome-wide interaction studies (GWIS) often lack power to detect complex gene-environment (G × E) interactions. We applied a forest-based machine learning approach to 38,018 UK Biobank (UKB) participants, examining interactions between 285,677 single-nucleotide polymorphisms (SNPs) and three trauma types (childhood, adult, and catastrophic trauma). While GWIS detected no significant interactions, we identified 8,225 potentially important SNP-environment pairs across 1,732 genes, with childhood trauma contributing most prominently. Stratified heritability was higher among childhood trauma-exposed individuals (13.3%) versus those unexposed (6.0%). Many identified genes overlapped with known psychiatric risk loci and accounted for most of the SNP-based heritability. Thirteen top genes were replicated in the Adolescent Brain Cognitive Development Study. Our findings highlight the polygenic G × E nature of depression and the critical role of childhood trauma in modulating genetic risk, demonstrating the value of forest-based methods in detecting complex gene-environment interactions.