Approaches for combining genomic and nongenomic information, optimizing models for populations of diverse genetic ancestry and across age groups, and conveying this information to clinicians and patients have yet to be developed and applied in clinical care. Clinical and environmental data combined with monogenic and polygenic risk measurements can improve risk prediction, as demonstrated in ref. PRSs can be improved if developed and validated using multiancestry cohorts 16. Such scores have been shown to have limited prediction accuracy with increasing genetic distance from European populations 12, 15. PRSs for individual conditions are typically generated from summary statistics derived from genome-wide association studies (GWASs), which are themselves derived from populations that are heavily overrepresented by individuals of European ancestry 12. However, clinical use of Eurocentric PRSs in diverse patient samples risks exacerbating existing health disparities 12, 13, 14. Improved estimation of risk may also enable targeting of preventive or therapeutic interventions to those most likely to benefit from them while avoiding unnecessary testing or overtreatment 10, 11. Clinical use of PRSs may ultimately prevent disease or enable its detection at earlier, more treatable stages 7, 8, 9, 10. Incorporation of genomic risk information has the potential to improve risk estimation and management 4, 6, particularly at younger ages 7. PRSs are being calculated and disseminated at a prodigious rate 1, 2, but their development and application to clinical care, particularly among ancestrally diverse individuals, present substantial challenges 3, 4, 5. Polygenic risk scores (PRSs) aggregate the effects of many genetic risk variants and can be used to predict an individual’s genetic predisposition to a disease or phenotype 1. The initial experience from eMERGE can inform the approach needed to implement PRS-based testing in diverse clinical settings. ![]() Finally, we created a framework for regulatory compliance and developed a PRS clinical report for return to providers and for inclusion in an additional genome-informed risk assessment. We then developed a pipeline for clinical PRS implementation (score transfer to a clinical laboratory, validation and verification of score performance), and used genetic ancestry to calibrate PRS mean and variance, utilizing genetically diverse data from 13,475 participants of the All of Us Research Program cohort to train and test model parameters. Standardized metrics were considered in the selection process, with additional consideration given to strength of evidence in African and Hispanic populations. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. ![]() Nature Medicine volume 30, pages 480–487 ( 2024) Cite this article ![]() Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations
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