title: 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 t n are as comparable as those of stepwise variable selection implemented to when n t p. When n > p, the iterative Bayesian variable selection performs better than stepwise variable selection does.
authors.affiliation: Medical Genetics Institute, Cedars-Sinai Medical Center, 8635 West Third Street, Suite 665, Los Angeles, California 90048, USA. soonil.kwon@cshs.org
authors.forename: Soonil
authors.initials: S
authors.lastname: Kwon
authors.forename: Dai
authors.initials: D
authors.lastname: Wang
authors.forename: Xiuqing
authors.initials: X
authors.lastname: Guo