A principal-components-based clustering method to identify multiple variants associated with rheumatoid arthritis and arthritis-related autoantibodies.
作者信息
Black Mary Helen, Watanabe Richard M
机构信息
Department of Preventive Medicine, Keck School of Medicine, University of Southern California, 1540 Alcazar Street, CHP 222-V, Los Angeles, California 90089, USA.
出版信息
BMC Proc. 2009 Dec 15;3 Suppl 7(Suppl 7):S129. doi: 10.1186/1753-6561-3-s7-s129.
Abstract
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.
摘要
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