The phenome-wide association scan (PheWAS) has been proposed to examine specific variants against large databases of diseases and phenotypes, such as those contained in electronic medical records (EMR). These methods scan EMR data for genetic associations using International Classification of Disease (ICD) billing codes commonly used in EMR systems in the U.S. PheWAS studies may help elucidate new biology through the identification of phenotypic associations with a shared etiology. This may lead to discoveries of causative associations between diseases and variants of interest. In this study, we anticipate enrolling approximately 100,000 individuals across diverse ancestral backgrounds, leveraging existing high-resolution HLA genotyping data collected by the NMDP (National Marrow Donor Program) and collecting, through self-report, wide-ranging phenotypic data specifically targeting common medical conditions not generally noted in the EMR to execute the largest, most exhaustive PheWAS conducted for the examination of HLA in human health. We aim to identify associations between HLA alleles, identify phenotypes with shared HLA associations, and create to a network map to map associations between phenotypes and HLA genes. The results potentially will offer new insights into the genetic basis of various health conditions and their implications for personalized medicine.