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Polygenic risk-by-socio-economic status interaction effects on specific and aggregated outcomes of depression, anxiety, body mass index, waist-hip-ratio, smoking and alcohol use

Wang, R.; Lifelines Cohort Study, ; Snieder, H.; Hartman, C. A.

2025-09-24 epidemiology
10.1101/2025.09.22.25336352 medRxiv
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BackgroundPsychiatric and somatic problems are highly prevalent, often co-occur and share part of their genetic background. Low socio-economic status (SES) is a risk factor for both. We examined whether low SES amplifies the effects of genetic susceptibility on depression, anxiety, body mass index (BMI), waist-hip-ratio (WHR), smoking and alcohol use, investigating and aggregated outcomes. MethodsData came from the population-based Lifelines Cohort Study (n=50,761). Anxiety, depression, body mass index, waist-hip-ratio, smoking, and alcohol use were analyzed individually and collectively using confirmatory factor analysis to model shared variance among outcomes. Polygenic risk scores (PRSs) were calculated based on recent genome-wide association studies (GWASs) of specific conditions. We performed GWASs and derived PRS of aggregated outcomes using genomic structural equation modelling (SEM). A latent SES factor was generated by educational attainment, occupational status, and disposable household income. Interaction effects of SES indices with PRSs were estimated using linear mixed regression. ResultsEight of ten PRSs-by-SES interactions were significant (ps<0.05) for depression, anxiety, BMI, smoking, and aggregated outcome level, but not for WHR (p=0.07) and alcohol use (p=0.67). Lower SES amplified the effects of PRSs on depression, anxiety, BMI, and smoking. At the aggregated outcome level, interaction effects were mostly smaller than effects for individual outcomes. ConclusionsOur study demonstrates polygenic risk-by-SES interaction effects on depression, anxiety, BMI, and smoking. However, the effects were attenuated for PRSs derived from genomic SEM and aggregated outcomes, indicating limited additional etiological insights from modelling genetic and phenotypic overlap.

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