Phenome-wide scanning identifies multiple diseases and disease severity phenotypes associated with HLA variants

Numerous associations were discovered between human leukocyte antigen (HLA) variation and a comprehensive set of phenotypes derived from electronic health records. Hints on health and disease from HLA Each of us expresses a mix of different human leukocyte antigens (HLAs), which present self- and foreign peptides to T cells. Because slightly different peptides are presented by each HLA type, HLA expression can influence an individual’s susceptibility to disease. Karnes et al. scrutinized electronic health record information from tens of thousands of people in two distinct cohorts to compare their phenotypes to the HLA alleles they express. This study confirmed previously identified HLA associations and also identified new ones; most associations were related to autoimmune diseases. The researchers have made the catalog freely available so that other groups can mine the data for future discoveries about how HLAs drive different phenotypes. Although many phenotypes have been associated with variants in human leukocyte antigen (HLA) genes, the full phenotypic impact of HLA variants across all diseases is unknown. We imputed HLA genomic variation from two populations of 28,839 and 8431 European ancestry individuals and tested association of HLA variation with 1368 phenotypes. A total of 104 four-digit and 92 two-digit HLA allele phenotype associations were significant in both discovery and replication cohorts, the strongest being HLA-DQB1*03:02 and type 1 diabetes. Four previously unidentified associations were identified across the spectrum of disease with two- and four-digit HLA alleles and 10 with nonsynonymous variants. Some conditions associated with multiple HLA variants and stronger associations with more severe disease manifestations were identified. A comprehensive, publicly available catalog of clinical phenotypes associated with HLA variation is provided. Examining HLA variant disease associations in this large data set allows comprehensive definition of disease associations to drive further mechanistic insights.

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