Applications of MOSS for Discrete Multi-Way Data

Abstract We present a Bayesian variable selection procedure that is applicable to genomewide studies involving a combination of clinical, gene expression and genotype information. We use the Mode Oriented Stochastic Search (MOSS) algorithm of Dobra and Massam (2010) to explore regions of high posterior probability for regression models involving discrete covariates and to perform hierarchical log-linear model search to identify the most relevant associations among the resulting subsets of regressors. We illustrate our methodology with simulated data, expression data and SNP data.