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A pathway analysis applied to Genetic Analysis Workshop 16 genome-wide rheumatoid arthritis data

Author(s): Ballard David H | Aporntewan Chatchawit | Lee Ji | Lee Joon | Wu Zheyang | Zhao Hongyu

Journal: BMC Proceedings
ISSN 1753-6561

Volume: 3;
Issue: Suppl 7;
Start page: S91;
Date: 2009;
Original page

Abstract The identification of several hundred genomic regions affecting disease risk has proven the ability of genome-wide association studies have proven their ability to identify genetic contributors to disease. Currently, single-nucleotide polymorphism (SNP) association analysis is the most widely used method of genome-wide association data, but recent research shows that multi-marker tests of association may provide greater power, especially when more than one mutation is present within a gene and the mutations are in low linkage disequilibrium with each other. Here we use a multi-marker association test based on regression to SNPs located within known genes to obtain a gene-level score of association. We then perform pathway analysis using this score as a measure of gene importance. We use two tests of pathway enrichment - a binomial test and a random set method. By utilizing publicly available gene and pathway information, we identify B cell, cytokine and inflammation response, and antigen presentation pathways as being associated with rheumatoid arthritis. These results confirm known biological mechanisms for auto-immunity disorders, of which rheumatoid arthritis is one.
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