A pathway analysis applied to Genetic Analysis Workshop 16 genome-wide rheumatoid arthritis data.
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.
Ballard
David H
DH
Program in Computational Biology and Bioinformatics, Yale University, 300 George Street, Suite 503, New Haven, Connecticut 06511, USA. david.ballard@yale.edu.
Aporntewan
Chatchawit
C
Lee
Ji Young
JY
Lee
Joon Sang
JS
Wu
Zheyang
Z
Zhao
Hongyu
H