Rheumatoid arthritis is a disabling disease that affects up to 1% of the adult population. With the heritability of RA estimated at about 60%, the major genetic association has been linked to the HLA-DRB1 gene within the major histocompatibility complex (MHC) region on chromosome 6. More than 20 years ago, the "shared epitope" hypothesis implicated a short stretch of amino acids (residues 70-74) in the HLA-DRB1 protein subunit, providing a solid foundation for subsequent RA studies. Yet uncertainty remains as to the causal variants within DRB1 and the existence of associations outside DRB1. In this project, we develop an imputation procedure to re-analyze existing GWAS data sets of RA in order to fine-map association signals in the MHC. Besides imputing classical HLA alleles, we also focus on the role of specific amino acid polymorphisms within the classical HLA proteins. We will analyze data sets from multiple ethnicities (European, African American, Han Chinese and Korean) in order to increase power to identify causal variants. Ultimately, insights from our project can help identify the (self-)antigens targeted in RA pathogenesis. Our analytic strategy is broadly applicable beyond RA, and could help interpret MHC associations for many other immune-related diseases based on existing GWAS data. We will share our software tools with the human genetics community.
Rheumatoid arthritis is a disabling inflammatory polyarthritis disease that affects up to 1% of the adult population. While the importance of the major histocompatability complex was highlighted in studies since the 1970s, the specific HLA genes and their variants that play a critical role in the disease have been much harder to unambiguosly determine. In this proposal, we aim to clarify the role of HLA genes and their amino acid residue variants by analyzing existing data sets in up to 30,000 individuals of multiple ethnicities.
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