Application of an iterative Bayesian variable selection method in a genome-wide association study of rheumatoid arthritis

Abstract

Genome-wide association studies usually involve several hundred thousand of single-nucleotide polymorphisms (SNPs). Conventional approaches face challenges when there are enormous number of SNPs but a relatively small number of samples and, in some cases, are not feasible. We introduce here an iterative Bayesian variable selection method that provides a unique tool for association studies with a large number of SNPs (p) but a relatively small sample size (n). We applied this method to the simulated case-control sample provided by the Genetic Analysis Workshop 15 and compared its performance with stepwise variable selection method. We demonstrated that the results of iterative Bayesian variable selection applied to when p » n are as comparable as those of stepwise variable selection implemented to when n » p. When n > p, the iterative Bayesian variable selection performs better than stepwise variable selection does.