Variable selection for large p small n regression models with incomplete data: Mapping QTL with epistases

Background: Identifying quantitative trait loci (QTL) for both additive and epistatic effects raises the statistical issue of selecting variables from a large number of candidates using a small number of observations. Missing trait and/or marker values prevent one from directly applying the classical model selection criteria such as Akaike's information criterion (AIC) and Bayesian information criterion (BIC). Results: We propose a two-step Bayesian variable selection method which deals with the sparse parameter space and the small sample size issues. The regression coefficient priors are flexible enough to incorporate the characteristic of "large p small n" data. Specifically, sparseness and possible asymmetry of the significant coefficients are dealt with by developing a Gibbs sampling algorithm to stochastically search through low-dimensional subspaces for significant variables. The superior performance of the approach is demonstrated via simulation study. We also ap...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Min Zhang, Dabao Zhang, Martin T. Wells
• Keywords: BMCBI 2008, Parameter Space, QTL Mapping, Sample Size