| Title: | Identifying genome-by-childhood trauma interactions for depression using a forest-based approach in the UK Biobank and Adolescent Brain Cognitive Development Study |
| Journal: | Proceedings of the National Academy of Sciences of the United States of America |
| Published: | 6 Feb 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41650220/ |
| DOI: | https://doi.org/10.1073/pnas.2527955123 |
| URL: | https://pmc.ncbi.nlm.nih.gov/articles/PMC12890833/ |
Publication 18354
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
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.</p>
20 Keywords
- Adolescent
- Adult
- Adverse Childhood Experiences
- Biological Specimen Banks
- Brain
- Child
- Cognition
- Depression
- Female
- Gene-Environment Interaction
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Humans
- Machine Learning
- Male
- Middle Aged
- Multifactorial Inheritance
- Polymorphism, Single Nucleotide
- UK Biobank
- United Kingdom
3 Authors
- Yue Hu
- Jeffrey R. Gruen
- Heping Zhang
1 Application
| Application ID | Title |
|---|---|
| 42009 | Genetic study of complex phenotypes |
Enabling scientific discoveries that improve human health