Identity-by-descent captures shared environmental factors at biobank scale

Genealogical ties leave traces beyond shared genetic material: relatives tend to share environmental exposures and cultural practices that persist across generations. Here, using genomic and electronic health record data from 13,143 individuals in the Biorepository and Integrative Genomics, we apply a hierarchical community detection algorithm that groups individuals into four major communities and 17 subcommunities based on the proportion of their genome shared identity-by-descent (IBD). This approach captures a fine-scale demographic structure beyond traditional ancestry classifications. By integrating neighborhood-level geographic data with census-derived environmental metrics, we reveal unequal exposure to environmental stressors between subcommunities, which correlates with differential rates of health conditions, including elevated respiratory condition prevalence in subcommunities under greater pollution burden. Through an interventional mediation analysis we find that a substantial part of the excess subcommunity-level risk remains unexplained by measured environmental factors alone, consistent with a contribution of unmeasured shared exposures captured by IBD-based grouping. Subcommunity-level disparities persist in sensitivity analyses adjusting for self-reported race, indicating that IBD-based clustering captures health-relevant information beyond demographic categories commonly used as proxies for social determinants of health. We implement an open-source dashboard for interactive exploration of IBD-defined communities, disease prevalence, and environmental exposures. Together, we demonstrate that IBD-based clustering jointly captures genetic and environmental determinants of health, offering a scalable framework for precision health and population genetics that translates biobank data into actionable insights.