Stochastic search variable selection based on two mixture components and continuous-scale weighting.

Abstract

Stochastic search variable selection (SSVS) is a Bayesian variable selection method that employs covariate-specific discrete indicator variables to select which covariates (e.g., molecular markers) are included in or excluded from the model. We present a new variant of SSVS where, instead of discrete indicator variables, we use continuous-scale weighting variables (which take also values between zero and one) to select covariates into the model. The improved model performance is shown and compared to standard SSVS using simulated and real quantitative trait locus mapping datasets. The decision making to decide phenotype-genotype associations in our SSVS variant is based on median of posterior distribution or using Bayes factors. We also show here that by using continuous-scale weighting variables it is possible to improve mixing properties of Markov chain Monte Carlo sampling substantially compared to standard SSVS. Also, the separation of association signals and nonsignals (control of noise level) seems to be more efficient compared to the standard SSVS. Thus, the novel method provides efficient new framework for SSVS analysis that additionally provides whole posterior distribution for pseudo-indicators which means more information and may help in decision making.