ArticleCommunications medicine2026
Differentiating genetic and gene-environment interaction profiles of physical and mental health disorders.
Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
6 authors.
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Abstract
backgroundPredisposition to adult chronic physical and mental health disorders is shaped by genetics, environmental exposures, and their interactions. However, it remains unclear whether the same genetic variants associated with disease risk also influence how individuals respond to environmental exposures, such as early life adversity. This study examined whether genetic variants associated with disease risk overlap with those that modulate the effects of early life adversity.
methodsAnalyses were conducted in the UK Biobank, a population-based cohort of adults aged 37-73 years at recruitment. A total of 180,129 participants were included, comprising 22,372 individuals reporting early adversity and 157,757 who did not. Genome-wide association studies (GWAS) and genome-wide environment interaction studies (GWEIS) were performed for four health outcomes based on hospital diagnoses: cardiovascular disorders, type 2 diabetes, mood disorders, and neurotic disorders. Early life adversity was assessed using a validated questionnaire item capturing feelings of being hated or unloved during childhood.
resultsWe demonstrate that genetic variants identified in GWAS do not overlap with those detected in GWEIS, even at a nominal significance level (p < 5×10⁻⁵). SNP-based heritability was detectable for the main effect analyses but not for the interaction analyses, being greater for physical diseases than for mental health conditions.
conclusionsGenetic variants associated with disease risk do not capture context-dependent genetic effects. This has important implications for approaches relying on GWAS-derived signals, including risk prediction and biological interpretation. Integrating environmental context into genetic analyses is critical for understanding chronic disease etiology and identifying context-specific genetic risk factors.
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Registered trials
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