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 populations with some genetic ancestries using polygenic risk scores. Unfortunately, there is a large gap in the accuracy of polygenic risk scores in genetically diverse populations, such that clinical efforts to improve biomedical outcomes via precision medicine may have limited success. The increasing availability of data from genetically diverse populations 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 genetic ancestry-specific effects. Notably, functional genomics data has great potential to improve all of these efforts, but has yet to be adequately included in approaches for analyzing data from genetically diverse populations. Here, we propose to reap the advantages of joint analyses of data from genetically diverse populations and functional data, building on the extensive progress of our research program on disease mapping in genetically diverse populations over the past 8 years; the focus of our current application is on adapting existing statistical methods to a new setting, joint analyses of data from genetically diverse populations and functional data, which currently suffers a large gap in available methods. Our research will be driven by empirical data from >2,500,000 samples from genetically diverse populations (>1,500,000 with genotype/phenotype data and >1,000,000 with summary association statistics) 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.