A fast and efficient Gibbs sampler for BayesB in whole-genome analyses

Hao Cheng, Long Qu, Dorian J. Garrick, Rohan L. Fernando

In whole-genome analyses, the number p of marker covariates is often much larger than the number n of observations. Bayesian multiple regression models are widely used in genomic selection to address this problem of [Formula: see text] The primary difference between these models is the prior assumed for the effects of the covariates. Usually in the BayesB method, a Metropolis-Hastings (MH) algorithm is used to jointly sample the marker effect and the locus-specific variance, which may make BayesB computationally intensive. In this paper, we show how the Gibbs sampler without the MH algorithm can be used for the BayesB method. We consider three different versions of the Gibbs sampler to sample the marker effect and locus-specific variance for each locus. Among the Gibbs samplers that were considered, the most efficient sampler is about 2.1 times as efficient as the MH algorithm proposed by Meuwissen et al. and 1.7 times as efficient as that proposed by Habier et al. The three Gibbs samplers presented here were twice as efficient as Metropolis-Hastings samplers and gave virtually the same results.