Joint effects of childhood adversity and genetic risk for psychosis on psychopathology in the UK Biobank
Clinical Snapshot
PICO Framework
| P — Population | 163,704 individuals from the UK Biobank cohort |
| I — Intervention | Exposure to adverse childhood experiences (ACEs) and genetic risk (polygenic risk scores for schizophrenia and bipolar disorder) |
| C — Comparator | Individuals without ACEs and/or lower genetic risk scores |
| O — Outcomes | 55 psychopathological symptoms across seven domains (depression, anxiety, help-seeking, cognition, psychotic, mania, self-harm/suicide) |
Bottom Line
This large UK Biobank study demonstrates that genetic risk for psychosis and childhood adversity interact differently across various mental health symptom domains. While the findings suggest additive effects for depression and anxiety symptoms, multiplicative interactions were observed for psychotic and manic symptoms. The study's strength lies in its substantial sample size and novel approach to gene-environment interactions. However, significant limitations include cross-sectional design preventing causal inference, reliance on self-reported measures for both exposures and outcomes, and UK Biobank's inherent selection bias. The clinical utility remains limited as polygenic risk scores are not routinely available in practice. For Australian clinicians, these findings support trauma-informed approaches to mental healthcare but should not influence immediate clinical decision-making given the research-stage nature of polygenic risk assessment.
Key Findings
P Value: Not reported in abstract
Effect Size: RERI values 0.20-0.26 for additive interactions; beta coefficients -0.543 to -0.118 for multiplicative interactions
Primary Outcome: Differential interaction patterns between genetic risk and ACEs across symptom domains
Nnt Or Sensitivity: RERI (Relative Excess Risk due to Interaction) suggests 20-26% excess risk from additive interactions
Confidence Interval: Not reported in abstract
Clinical Application
Limited clinical utility due to complexity of polygenic risk scoring and lack of validated screening tools Relevant for Australian mental health services; aligns with RACGP guidelines on trauma-informed care but PRS not routinely available through PBS Individuals with history of childhood adversity and family history of psychosis
Abstract
BACKGROUND: The individual effects of genetic factors and adverse childhood experiences (ACEs) on risk of psychosis, including schizophrenia (SCZ) and bipolar disorder (BIP), have been widely acknowledged, but their interaction effects on individual psychopathological symptoms remain unclear. METHODS: Based on data from 163,704 individuals in the UK Biobank, we investigated the joint effects of polygenic risk scores (PRSs) of SCZ and BIP and ACEs on psychopathology. ACEs status and 55 psychopathological symptoms from seven domains were measured retrospectively using an online mental health questionnaire in 2016. Recent genome-wide association studies for SCZ and BIP were combined with genotype data to generate PRSs. Logistic regression analyses were then conducted to explore univariate and joint main effects of PRSs and ACEs on psychopathological symptoms, as well as their additive and multiplicative interaction effects. RESULTS: The interaction mechanisms for PRSs and ACEs varied across symptom domains: additive interactions were observed on the depression (RERIBIP-ACEs = 0.20-0.25), anxiety (RERISCZ-ACEs = 0.20; RERIBIP-ACEs = 0.22-0.26), help-seeking (RERISCZ-ACEs = 0.24; RERIBIP-ACEs = 0.23), and cognition domains (RERISCZ-ACEs = -0.23 to -0.17), whereas multiplicative interactions were only detected on the psychotic (betaSCZ-ACEs = -0.543; betaBIP-ACEs = -0.181), mania (betaBIP-ACEs = -0.195), self-harm or suicide (betaSCZ-ACEs = -0.118), and cognitive domains (betaSCZ-ACEs = -0.204 to -0.157). CONCLUSIONS: The interplay mechanisms for genetic liability to SCZ and BIP and ACEs vary across symptom domains. This study reveals heterogeneity in gene-ACEs interaction mechanisms underlying psychosis and may provide personalized guidance for psychological care after ACEs.
Related Research
Journal of psychiatric research
Systematic review of machine learning and deep learning models for EEG-based detection of depression.
2 Aug 2026
Sleep medicine reviews
Artificial intelligence in sleep medicine I: Diagnosis, treatment, care, and research
2 Aug 2026
International journal of yoga therapy
Randomized Controlled Trial on the Effect of Yoga on Anxiety Severity, Quality of Life, and Biomarkers in Individuals with Anxiety Disorder
31 July 2026
This content is for educational purposes for healthcare professionals only and does not constitute clinical advice. Clinical decisions should be based on individual patient assessment, current guidelines, and appropriate specialist consultation. Editorial Standards · Privacy Policy · Terms of Service