Genetics in Context: Social Factors Modify Polygenic Risk Across Multiple Cardiometabolic Diseases
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.
Teaser: Both genetics and the environment influence chronic disease, but whether and how they interact remains understudied. We find evidence of genetic effect modification of polygenic risk scores (PRS) by social factors for multiple cardiometabolic traits and that jointly modeling social risk improves disease prediction across a range of chronic diseases.
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BibTeX
@misc{hysong2026genetics,
title = {{Genetics in Context: Social Factors Modify Polygenic Risk Across Multiple Cardiometabolic Diseases}},
author = {Micah R. Hysong and Sarah Hsu and Farida Akhtari and Miriam S. Udler and Josep M. Mercader and Alisa K. Manning and Michael D. Green and Iain R. Konigsberg and Luciana B. Vargas and Leslie Lange and Michael I. Love and Megan M. Shuey and LáShauntá M. Glover and Alison A. Motsinger-Reif and Genevieve L. Wojcik and Sara J. Cromer and Laura M. Raffield and Polygenic Risk Methods Development (PRIMED) Consortium},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.27.26366625},
url = {https://www.medrxiv.org/content/10.64898/2026.10.27.26366625v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Genetics in Context: Social Factors Modify Polygenic Risk Across Multiple Cardiometabolic Diseases
AU - Micah R. Hysong
AU - Sarah Hsu
AU - Farida Akhtari
AU - Miriam S. Udler
AU - Josep M. Mercader
AU - Alisa K. Manning
AU - Michael D. Green
AU - Iain R. Konigsberg
AU - Luciana B. Vargas
AU - Leslie Lange
AU - Michael I. Love
AU - Megan M. Shuey
AU - LáShauntá M. Glover
AU - Alison A. Motsinger-Reif
AU - Genevieve L. Wojcik
AU - Sara J. Cromer
AU - Laura M. Raffield
AU - Polygenic Risk Methods Development (PRIMED) Consortium
PY - 2026
DA - 2026/10/27
PB - medRxiv
DO - 10.64898/2026.10.27.26366625
UR - https://www.medrxiv.org/content/10.64898/2026.10.27.26366625v1
AB - 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. Teaser: Both genetics and the environment influence chronic disease, but whether and how they interact remains understudied. We find evidence of genetic effect modification of polygenic risk scores (PRS) by social factors for multiple cardiometabolic traits and that jointly modeling social risk improves disease prediction across a range of chronic diseases.
ER -