A principal-components-based clustering method to identify multiple variants associated with rheumatoid arthritis and arthritis-related autoantibodies

Multivariate techniques are an important area of investigation for studying contributions of multiple genetic variants to disease onset and pathology. We analyzed the Genetic Analysis Workshop 16 North American Rheumatoid Arthritis Consortium (NARAC) data using a principal-components analysis (PCA) with an orthoblique rotation to identify specific subsets of single-nucleotide polymorphisms (SNP) in the major histocompatibility complex (MHC) region associated with rheumatoid arthritis (RA) and rheumatoid factor IgM (RFUW), and compared this method with a traditional PC approach. Using the orthoblique PC-based clustering method, we identified new clusters of SNPs across the MHC region associated with RA and RFUW, and replicated known SNP cluster associations with RA, such as those in the HLA-DRB region.