Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics

Parallel Monte Carlo Markov chain algorithms and strategies are described in the context of animal breeding and genetics. Parallel Monte Carlo algorithms are introduced as a starting point including their applications to computing single-parameter and certain multiple-parameter models. Then, two basic approaches for parallel Markov chain Monte Carlo are described: one aims at parallelization within a single chain; the other is based on running multiple chains, yet some variants are discussed as well. Features and strategies of the parallel Markov chain Monte Carlo are illustrated using real data, including a large beef cattle dataset with 50K SNP genotypes.Parallel Markov chain Monte Carlo algorithms are useful for computing complex Bayesian models, which does not only lead to a dramatic speedup in computing but can also be used to optimize model parameters in complex Bayesian models. Hence, we anticipate that use of parallel Markov chain Monte Carlo will have a profound impact on revolutionizing the computational tools for genomic selection programs.In recent decades, Bayesian inference has been increasingly used for analysis of complex statistical models, in part because of increased availability and performance of personal computers and workstations. However, such models are generally not analytically tractable and, hence, computationally demanding numerical techniques are inevitably required. This is especially true of Bayesian computation for genome-enabled prediction and selection, which aims at using whole-genome molecular data to predict the genetic merit of candidate animals for breeding purposes [1]. Typically, implementation of a high-dimensional model based on Markov chain Monte Carlo (MCMC) techniques is notoriously intensive in computing and often requires days, weeks, or even months of CPU (Central Processing Unit) time on personal computers and workstations [2]. Therefore, in order to overcome such computational burden, parallel computing becomes app