Genes and trauma combine to shape self-harm risk and cannabis vulnerability

Gene-Environment Interactions in Predicting Self-Harm: A Machine Learning Approach Using Explainable Artificial Intelligence.

Archives of suicide research : official journal of the International Academy for Suicide Research • • Moderately Relevant
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AI Summary

This large-scale study examined how genetic predispositions and environmental factors work together to influence self-harm risk in over 156,000 UK Biobank participants. Using advanced machine learning and artificial intelligence analysis, researchers identified which factors most strongly predict self-harm behaviors. The findings reveal a complex picture: environmental factors—particularly interpersonal trauma including partner abuse and sexual assault—were significantly more predictive than genetic factors alone. However, the interaction between genes and environment matters, with certain genetic vulnerabilities making individuals more susceptible to the harmful effects of trauma.

The research has particular significance for cannabis-related research because cannabis use disorder showed one of the strongest gene-environment interaction effects alongside major depression and anorexia nervosa. This suggests that individuals with genetic vulnerabilities may be at heightened risk for both cannabis use disorder and self-harm when exposed to traumatic environmental factors. The study accounts for approximately 12% of predicted self-harm risk through gene-environment interactions, highlighting that personalized prevention and treatment strategies must consider both inherited genetic factors and lived experiences.

For cannabis users and medical professionals, these findings underscore the importance of trauma-informed care and personalized risk assessment. Individuals with genetic predispositions to psychiatric conditions or substance use disorders may benefit from enhanced mental health support, particularly if they've experienced interpersonal trauma. This research supports the growing understanding that cannabis use patterns and mental health outcomes cannot be separated from the broader context of a person's genetic makeup and life experiences, emphasizing the need for holistic, individualized approaches to prevention and treatment.

📄 Original Abstract

Self-harm is a critical public health concern; nevertheless, the complex interplay between genetic predispositions and environmental factors in self-harm remains poorly understood. 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. 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. 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.

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