Identification of genes and variants associated with quantitative traits using Bayesian factor screening

We propose a factor-screening method based on a Bayesian model selection framework and apply it to Genetic Analysis Workshop 17 simulated data with unrelated individuals to identify genes and SNP variants associated with the quantitative trait Q1. A Metropolis-Hasting algorithm is implemented to generate a posterior distribution in a restricted model space and thus the marginal posterior distribution of each variant. Our framework provides flexibility to make inferences on either individual variants or genes. We obtained results for 10 simulated data sets. Our methods are able to identify FTP1 and KDR, two genes that are associated with Q1 in a majority of replicates.