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

Hyori Jeon

doi:10.1080/13811118.2026.2657528

2026-05-27

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DOI：10.1080/13811118.2026.2657528

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自伤是一个重要的公共卫生问题；然而，自伤中遗传易位与环境因素的复杂相互作用仍然知之甚少。本研究利用英国生物银行中156,873名参与者的数据，探讨了15种精神障碍/特征的 polygenic risk scores (PRSs) 如何与环境风险因素在预测终生自伤方面的相互作用。自动化机器学习识别了最佳预测模型，同时可解释人工智能技术被应用于评估特征重要性和相互作用。环境因素，特别是人际创伤（如伴侣贬低和性侵犯），显示出比遗传因素更强的预测价值。然而，基因-环境相互作用解释了预测自伤风险中约12%的变异，其中重度抑郁、大麻使用障碍和神经性厌食症的 PRSs 表现出最强的相互作用效应。本研究结果表明，具有遗传易感性的个体可能特别易受人际创伤的影响，强调了需要针对遗传和环境风险相结合的个性化预防策略。

Keyword:

Machine learning

self-harm

polygenic risk score

gene–environment interactions

explainable artificial intelligence

期刊

Archives of Suicide Research

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