PROJECT SUMMARY/ABSTRACT Cardiovascular diseases are not only the leading cause of mortality in the world, but they also lead to almost twice as many deaths in Black adults compared to White adults in the United States. Similar drastic health disparities exist for many other heart, lung, blood, and sleep (HLBS) disorders. Acknowledging that social determinants of health play a significant role in these disparities, we cannot overlook the biases that have perturbed existing genomic studies on which we base the development of precision medicine technologies. Of the ~6,300 studies currently compiled in the genome-wide association studies (GWAS) catalog as of March 2023, ~95% of all GWAS participants are of European (EUR) ancestry with less than 1% of participants being of African American (AFR) ancestry. Attempts to translate genetic research findings into clinical practice may be not just incomplete, but dangerously mistaken and misapplied. With the advent of large and diverse patient biobanks, there needs to be a targeted focus on studying genetic variations specific to ancestries that are too often understudied. The Penn Medicine BioBank serves as an ideal discovery vehicle for this purpose with a repository of genotype, whole-exome sequencing, and electronic health record (EHR) data as well as one of the largest AFR populations at any single-institutional medical biobank in the US, to our knowledge. We hypothesize that by using a genome-first AFR-specific approach, we will identify significant genetic associations for HLBS disorders that would have otherwise been missed by previous EUR-dominant studies and future multi-ancestry approaches. We will curate a list of AFR-specific predicted loss-of-function and missense variants, and then through HLBS-focused phenome-wide association studies, we will characterize the clinical manifestations of the diseases caused by these protein-altering variants. Furthermore, genome-wide association studies will be used to identify genetic loci with significant discrepancies in effect across ancestries and uncover variabilities in HLBS disease prevalence. To investigate these ancestry-specific risk loci, we will employ local ancestry inference to study ancestry-dosage effects and construct haplotypes to uncover drivers of the observed phenotype associations. This proposed project has the potential to broaden our understanding of genetic risk for HLBS disorders and significantly impact the development of diagnostic and therapeutic methods. Beyond that, our work embodies the vision that the advancement of personalized medicine should benefit every patient of every ancestry and socioeconomic background.