Leveraging functional data to predict disease risk in multi-ethnic populations

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Project Summary Genome-wide association studies (GWAS) have been broadly successful in identifying genetic variants associated to common disease risk, leading to successes in predicting disease risk in European populations using polygenic risk scores. Unfortunately, there is a large gap in the accuracy of polygenic risk scores between European and non-European populations, such that clinical efforts to improve biomedical outcomes via precision medicine may exacerbate health disparities. The increasing availability of multi-ethnic data in larger sample sizes provides opportunities to improve the accuracy of polygenic risk scores, by improving localization of causal variants and aiding identification of variants with population-specific effects. Notably, functional genomics data has great potential to improve all of these efforts, but has yet to be adequately integrated into multi-ethnic approaches. Here, we propose to reap the advantages of integrative analyses of multi-ethnic and functional data, building on the extensive progress of our research program on disease mapping in multi-ethnic populations over the past 8 years; the focus of our current application is on adapting existing statistical methods to a new setting, integrating multi-ethnic and functional data, which currently suffers a large gap in available methods. Our research will be driven by empirical data from >2,500,000 multi-ethnic samples (>1,500,000 with genotype/phenotype data and >1,000,000 with summary association statistics), including African American, Latino, East Asian and South Asian samples spanning a wide range of diseases and quantitative phenotypes. We will analyze both individual-level data and summary-level data and incorporate functional data sets, including genome-wide functional annotations and gene expression data.