Archives of suicide research : official journal of the International Academy for Suicide Research · 2026
Gene-Environment Interactions in Predicting Self-Harm: A Machine Learning Approach Using Explainable Artificial Intelligence.
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Jeon Hyori, Lee Dohyeon, Kim Hyejin, Yu Hyeona, Lee Jakyung, Jang Junwoo, Ha Tae Hyon, Won Hong-Hee, Myung Woojae, Kang Hyo Shin, Park Jungkyu
Аннотация
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.
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.
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.
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.
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.
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.
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.