Differentiating genetic and gene-environment interaction profiles of physical and mental health disorders
- 1Integrated Program in Neurosciences, McGill University
- 2Douglas Mental Health University Institute, McGill University
Abstract
Background: Predisposition 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. Methods: Analyses 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. Results: We 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. Conclusions: Genetic 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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BibTeX
@article{barth2026differentiating,
title = {{Differentiating genetic and gene-environment interaction profiles of physical and mental health disorders}},
author = {Barbara Barth and Patricia Maidana Miguel and Irina Pokhvisneva and Andre K. Portella and Michael J. Meaney and Patricia Pelufo Silveira},
journal = {Communications Medicine},
year = {2026},
month = jul,
doi = {10.1038/s43856-026-01748-7},
url = {https://doi.org/10.1038/s43856-026-01748-7},
note = {CC BY},
}
RIS
TY - JOUR
TI - Differentiating genetic and gene-environment interaction profiles of physical and mental health disorders
AU - Barbara Barth
AU - Patricia Maidana Miguel
AU - Irina Pokhvisneva
AU - Andre K. Portella
AU - Michael J. Meaney
AU - Patricia Pelufo Silveira
PY - 2026
DA - 2026/07/01
JO - Communications Medicine
DO - 10.1038/s43856-026-01748-7
UR - https://doi.org/10.1038/s43856-026-01748-7
AB - Background: Predisposition 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. Methods: Analyses 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. Results: We 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. Conclusions: Genetic 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.
SN - 2730-664X
ER -