Genetics in Context: Social Factors Modify Polygenic Risk Across Multiple Cardiometabolic Diseases

Polygenic risk scores (PRS) are increasingly used for disease prediction, but whether their associations vary across social contexts remains poorly understood. Here, we leveraged the large and diverse All of Us cohort to examine interactions between PRS and social determinants of health (SDoH) across eight prevalent chronic diseases and developed disease-specific polysocial risk scores (PsRS). We found consistent evidence of SDoH modification of PRS-disease associations for cardiometabolic diseases, including type 2 diabetes, coronary heart disease and chronic kidney disease, with PRS associations attenuated among more socially vulnerable populations. Joint modeling of PsRS and PRS significantly improved case status prediction across all eight diseases and PsRS were predictive for most diseases in external cohorts. These findings demonstrate that social context modifies associations between genetic risk and disease and suggest that integrating genetic and social information can refine disease prediction across diverse populations.

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Publication Details

Journal
medRxiv
Published
2026-10-29
DOI
https://doi.org/10.64898/2026.10.27.26366625
Primary Topic
Genetic Associations and Epidemiology
Type
preprint

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preprint

Genetics in Context: Social Factors Modify Polygenic Risk Across Multiple Cardiometabolic Diseases

Genevieve L. Wojcik, Alisa Knodle Manning, LáShauntá M. Glover, Laura M. Raffield et al.
4 links
medRxiv
Genetic Associations and Epidemiology
preprint

Genetics in Context: Social Factors Modify Polygenic Risk Across Multiple Cardiometabolic Diseases

Genevieve L. Wojcik, Alisa Knodle Manning, LáShauntá M. Glover, Laura M. Raffield, Sarah C Hsu, Luciana B. Vargas, Farida S. Akhtari, Megan M. Shuey, Miriam S. Udler, Iain R. Konigsberg, SARA JANE CROMER, Leslie A. Lange, Josep Maria Mercader, Michael D. Green, Micah R. Hysong, Michael I Love, Alison Motsinger-Reif
preprint en

Abstract

Polygenic risk scores (PRS) are increasingly used for disease prediction, but whether their associations vary across social contexts remains poorly understood. Here, we leveraged the large and diverse All of Us cohort to examine interactions between PRS and social determinants of health (SDoH) across eight prevalent chronic diseases and developed disease-specific polysocial risk scores (PsRS). We found consistent evidence of SDoH modification of PRS-disease associations for cardiometabolic diseases, including type 2 diabetes, coronary heart disease and chronic kidney disease, with PRS associations attenuated among more socially vulnerable populations. Joint modeling of PsRS and PRS significantly improved case status prediction across all eight diseases and PsRS were predictive for most diseases in external cohorts. These findings demonstrate that social context modifies associations between genetic risk and disease and suggest that integrating genetic and social information can refine disease prediction across diverse populations.

medRxiv
Broad Institute (US), University of North Carolina at Chapel Hill (US), Johns Hopkins University (US), Duke University (US), University of Colorado System (US), Massachusetts General Hospital (US), National Institute of Environmental Health Sciences (US), Duke Medical Center (US), University of Colorado Anschutz Medical Campus (US), Vanderbilt University Medical Center (US)
National Science Foundation, American Diabetes Association, National Institutes of Health, National Institute on Aging, National Human Genome Research Institute
Good health and well-being
Genetic Associations and Epidemiology
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