Bayesian regularized regression for genome-wide SNP data
The goal of genome-wide association studies (GWAS) is to identify the best subset of single-nucleotide polymorphisms (SNPs) that strongly influence a certain trait. State of the art GWAS comprise several thousand or even millions of SNPs, scored on a substantially lower number of individuals. Hence, the number of variables greatly exceeds the number of observations. Analysis of GWAS data has been tackled by using Bayesian regularized regression methods. However, these methods are computationally very demanding and it is therefore of great importance to have access to good computing resources. The purpose of this study is via simulation studies to evaluate how different patterns of correlation (also referred to as linkage disequilibrium) between predictor variables influence the outcome of several Bayesian regularized regression methods.