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<p>Psychotic disorders are complex, multifactorial conditions influenced by both genetic liability and early environmental adversity. Polygenic risk scores (PRSs) derived from genome-wide association studies have shown utility in capturing genetic predisposition, but their biological interpretability remains limited. In this study, we evaluated whether biologically informed pathway-specific polygenic scores (pPGSs) for psychosis, restricted to neurotransmitter-related pathways, could help clarify gene-environment interplay. Using data from 1 192 individuals in the EU-GEI multi-site case-control study, we constructed pPGSs for dopamine, glutamate, GABA, and serotonin systems. We investigated associations between pPGSs and childhood trauma (rGE), their interactions on psychosis risk (GxE), and the influence of the genome-wide psychosis PRS on these relationships. Serotonin, dopamine, and glutamate pPGSs were positively associated with a composite trauma exposure (i.e., abuse and neglect), suggesting shared genetic factors contributing to both psychosis liability and early adversity. Significant negative GxE effects were observed for both dopamine and serotonin pPGSs, indicating that higher trauma exposure diminished the relative influence of genetic liability on psychosis risk. Adjustment for the genome-wide psychosis PRS attenuated most effects, but serotonergic and dopaminergic associations remained robust, supporting pathway-specific contributions beyond general polygenic risk. These findings provide proof-of-concept for the utility of pPGSs in psychiatric research, suggesting both genetic contributions to trauma exposure and GxE effects on psychosis risk. Further research incorporating epigenetic data and longitudinal designs may enhance mechanistic insight and translational potential.</p>

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Integrating biological pathway polygenic scores and trauma in psychosis: findings from the EU-GEI study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月5日

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  • Giulia Trotta,
  • Isabelle Austin-Zimmerman,
  • Edoardo Spinazzola,
  • Lucia Sideli,
  • Monica Aas,
  • Victoria Rodriguez,
  • Senta Haussler,
  • Zhikun Li,
  • Perry BM Leung,
  • Qiang Li,
  • Diego Quattrone,
  • Teya Petrova,
  • Craig Morgan,
  • Michael O’Donovan,
  • Pak C. Sham,
  • Evangelos Vassos,
  • Chloe CY Wong,
  • Richard Bentall,
  • Robin M. Murray,
  • Luis Alameda,
  • Marta Di Forti

摘要

Psychotic disorders are complex, multifactorial conditions influenced by both genetic liability and early environmental adversity. Polygenic risk scores (PRSs) derived from genome-wide association studies have shown utility in capturing genetic predisposition, but their biological interpretability remains limited. In this study, we evaluated whether biologically informed pathway-specific polygenic scores (pPGSs) for psychosis, restricted to neurotransmitter-related pathways, could help clarify gene-environment interplay. Using data from 1 192 individuals in the EU-GEI multi-site case-control study, we constructed pPGSs for dopamine, glutamate, GABA, and serotonin systems. We investigated associations between pPGSs and childhood trauma (rGE), their interactions on psychosis risk (GxE), and the influence of the genome-wide psychosis PRS on these relationships. Serotonin, dopamine, and glutamate pPGSs were positively associated with a composite trauma exposure (i.e., abuse and neglect), suggesting shared genetic factors contributing to both psychosis liability and early adversity. Significant negative GxE effects were observed for both dopamine and serotonin pPGSs, indicating that higher trauma exposure diminished the relative influence of genetic liability on psychosis risk. Adjustment for the genome-wide psychosis PRS attenuated most effects, but serotonergic and dopaminergic associations remained robust, supporting pathway-specific contributions beyond general polygenic risk. These findings provide proof-of-concept for the utility of pPGSs in psychiatric research, suggesting both genetic contributions to trauma exposure and GxE effects on psychosis risk. Further research incorporating epigenetic data and longitudinal designs may enhance mechanistic insight and translational potential.