Abstract
BackgroundMost Bayesian models for the analysis of complex traits are not analytically tractable and inferences are based on computationally intensive techniques. This is true of Bayesian models for genome-enabled selection, which uses whole-genome molecular data to predict the genetic merit of candidate animals for breeding purposes. In this regard, parallel computing can overcome the bottlenecks that can arise from series computing. Hence, a major goal of the present study is to bridge the gap to high-performance Bayesian computation in the context of animal breeding and genetics.ResultsParallel 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.ConclusionsParallel 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.
Highlights
Most Bayesian models for the analysis of complex traits are not analytically tractable and inferences are based on computationally intensive techniques
Such models are generally not analytically tractable and, 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]
We present a technical description of parallel Markov chain Monte Carlo (MCMC) methods in the context of animal breeding and genetics
Summary
Most Bayesian models for the analysis of complex traits are not analytically tractable and inferences are based on computationally intensive techniques This is true of Bayesian models for genome-enabled selection, which uses whole-genome molecular data to predict the genetic merit of candidate animals for breeding purposes. 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 Such models are generally not analytically tractable and, 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]. Parallel computing has become a dominant paradigm in current computer architectures, mainly in the form of multi-core processors [8]
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