| Title: | Gene-Environment Interactions in Predicting Self-Harm: A Machine Learning Approach Using Explainable Artificial Intelligence |
| Journal: | Archives of Suicide Research |
| Published: | 27 May 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42201893/ |
| DOI: | https://doi.org/10.1080/13811118.2026.2657528 |
Publication 19890
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Abstract
BACKGROUND: Self-harm is a critical public health concern; nevertheless, the complex interplay between genetic predispositions and environmental factors in self-harm remains poorly understood.</p>
METHODS: This study employed data from 156,873 participants in the UK Biobank to investigate how polygenic risk scores (PRSs) for 15 psychiatric disorders/traits interact with environmental risk factors in predicting lifetime self-harm. Automated machine learning identified the optimal predictive model, while explainable artificial intelligence techniques were applied to assess feature importance and interactions.</p>
RESULTS: Environmental factors, particularly interpersonal trauma, such as partner belittlement and sexual assault, demonstrated stronger predictive value than genetic factors. However, gene-environment interactions accounted for approximately 12% of the variance in predicted self-harm risk, with major depression, cannabis use disorder, and anorexia nervosa PRSs exhibiting the strongest interaction effects.</p>
CONCLUSIONS: This study's findings suggest that individuals with genetic vulnerabilities may be particularly susceptible to interpersonal trauma, highlighting the need for personalized prevention strategies addressing combined genetic and environmental risks.</p>
11 Authors
- Hyori Jeon
- Dohyeon Lee
- Hyejin Kim
- Hyeona Yu
- Jakyung Lee
- Junwoo Jang
- Tae Hyon Ha
- Hong-Hee Won
- Woojae Myung
- Hyo Shin Kang
- Jungkyu Park
1 Application
| Application ID | Title |
|---|---|
| 33002 | Development of a computational method to predict disease risk |
Enabling scientific discoveries that improve human health