ABSTRACTGenetic variation in immune-related genes as in the human leukocyte antigen (HLA) locus plays a pervasiverole across organ systems. HLA variation called HLA alleles is used to match organ donors and has beenassociated with adverse drug reactions (ADRs) cancer infections and cardiovascular and neurologic diseases.However most studies focus on the impact of HLA variation on specific immune-mediated diseases; the broaderinfluence of HLA variation across all human disease has not been investigated in depth. The proposed researchprogram will address the challenge of identifying immunogenomic influence on a broad spectrum of diseasesand ADRs. Previous studies of HLA influence have almost exclusively focused on populations of Europeandescent thus differences across ancestral groups are not well understood. The availability of the All of UsResearch Program (AoU) a large diverse DNA biobank coupled to electronic health records (EHR) enablesinvestigation of how HLA alleles influence many diseases across multiple diverse populations simultaneously.We propose to perform systematic investigation of the association of HLA alleles with disease using a twopronged approach based on the phenome-wide association study (PheWAS). PheWAS is a disease-neutralapproach that identifies the association between genetic variation across a broad set of diseases. In SpecificAim 1 HLA alleles will be determined using whole genome sequence data and PheWAS will be deployed inAllofUs to determine the influences of HLA alleles across organ systems and to explore ancestral differences inHLA associations. We will determine association of HLA-A -B -C -DR and -DQ alleles with a comprehensiveset of diseases within and across major ancestry groups in AoU. Despite its power PheWAS analysis is limitedto identifying single-allele connections to phenotypes of interest so influences that result from HLA interactions(either combinations of HLA alleles or between an HLA gene and some other genomic context) may be missed.Specific Aim 2 will address this shortcoming we will develop Machine Learning strategies to explore the effectof HLA allele interactions on disease and explore the potential for recognizing pleiotropic influences of HLAalleles. This innovative PheWAS-based approach has the potential to discover novel mechanisms of manydiseases identify biomarkers that may predict disease and create a roadmap by which future researchersinvestigate the impact of HLA variation in human disease. As indicated by our previous work PheWAS has thepotential to condense decades of immunogenomic discoveries into a single analysis. When applied to under-studied diverse populations this work has the potential to accelerate this field of research. This approach canbe applied to many other genomic loci differential associations by other characteristics such as sex and/orgender and identification of pleiotropic effects across disease systems creating a number of potentially fruitfulavenues of future research.