Discovery of Immunogenomic Associations with Disease and Differential Risk Across Diverse Populations

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ABSTRACT Genetic variation in immune-related genes, as in the human leukocyte antigen (HLA) locus, plays a pervasive role across organ systems. HLA variation, called HLA alleles, is used to match organ donors, and has been associated 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 broader influence of HLA variation across all human disease has not been investigated in depth. The proposed research program will address the challenge of identifying immunogenomic influence on a broad spectrum of diseases and ADRs. Previous studies of HLA influence have almost exclusively focused on populations of European descent, thus differences across ancestral groups are not well understood. The availability of the All of Us Research Program (AoU), a large, diverse DNA biobank coupled to electronic health records (EHR) enables investigation 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 two pronged approach based on the phenome-wide association study (PheWAS). PheWAS is a disease-neutral approach that identifies the association between genetic variation across a broad set of diseases. In Specific Aim 1, HLA alleles will be determined using whole genome sequence data, and PheWAS will be deployed in AllofUs to determine the influences of HLA alleles across organ systems, and to explore ancestral differences in HLA associations. We will determine association of HLA-A, -B, -C, -DR, and -DQ alleles with a comprehensive set of diseases within and across major ancestry groups in AoU. Despite its power, PheWAS analysis is limited to 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 effect of HLA allele interactions on disease, and explore the potential for recognizing pleiotropic influences of HLA alleles. This innovative PheWAS-based approach has the potential to discover novel mechanisms of many diseases, identify biomarkers that may predict disease, and create a roadmap by which future researchers investigate the impact of HLA variation in human disease. As indicated by our previous work, PheWAS has the potential 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 can be applied to many other genomic loci, differential associations by other characteristics such as sex and/or gender, and identification of pleiotropic effects across disease systems, creating a number of potentially fruitful avenues of future research.