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Knee pain is one of the most common musculoskeletal complaints that brings people to medical attention. Approximately 50% of individuals over the age of 50 report an experience of knee pain within the past 12 months. We sought to identify the genetic variants associated with knee pain in 171,516 subjects from the UK Biobank cohort and seek supporting evidence in cohorts from 23andMe, the Osteoarthritis Initiative, and the Johnston County Osteoarthritis Project. We identified two loci that reached genome-wide significance in the UK Biobank: rs143384, located in GDF5 (P = 1.32 × 10−12), a gene previously implicated in osteoarthritis; and rs2808772, located near COL27A1 (P = 1.49 × 10−8). These findings were supported in cohorts with self-reported osteoarthritis/radiographic knee osteoarthritis without pain information. In this report on genome-wide association of knee pain, we identified two loci in or near GDF5 and COL27A1 that are associated with knee pain. Weihua Meng, Mark Adams et al. report a genome-wide association study of knee pain in the UK Biobank, identifying two loci near GDF5 and COL27A1 as significantly associated. These findings are supported by association data in additional cohorts, using self-reported osteoarthritis or radiographic knee osteoarthritis as a proxy for knee pain. [ABSTRACT FROM AUTHOR]

Understanding population health disparities is an essential component of equitable precision health efforts. Epidemiology research often relies on definitions of race and ethnicity, but these population labels may not adequately capture disease burdens and environmental factors impacting specific sub-populations. Here, we propose a framework for repurposing data from electronic health records (EHRs) in concert with genomic data to explore the demographic ties that can impact disease burdens. Using data from a diverse biobank in New York City, we identified 17 communities sharing recent genetic ancestry. We observed 1,177 health outcomes that were statistically associated with a specific group and demonstrated significant differences in the segregation of genetic variants contributing to Mendelian diseases. We also demonstrated that fine-scale population structure can impact the prediction of complex disease risk within groups. This work reinforces the utility of linking genomic data to EHRs and provides a framework toward fine-scale monitoring of population health. [Display omitted] • Genomic data linked to health records capture demography in health systems • Genetic networks reveal recent common ancestry in diverse populations • Evidence of many founder populations in New York City • Fine-scale population structure impacts genetic risk predictions Taking a quantitative approach to genetic ancestry in health systems furthers understanding of disease burdens specific to fine-scale populations and the environmental and demographic ties that can impact disease. [ABSTRACT FROM AUTHOR]