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Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics.
Genet Sel Evol. 2012 Sep 25; 44:29.GS

Abstract

BACKGROUND

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. 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.

RESULTS

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.

CONCLUSIONS

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.

Authors+Show Affiliations

Department of Dairy Science, University of Wisconsin, Madison, WI, USA. nick.wu@ansci.wisc.eduNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

23009363

Citation

Wu, Xiao-Lin, et al. "Parallel Markov Chain Monte Carlo - Bridging the Gap to High-performance Bayesian Computation in Animal Breeding and Genetics." Genetics, Selection, Evolution : GSE, vol. 44, 2012, p. 29.
Wu XL, Sun C, Beissinger TM, et al. Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics. Genet Sel Evol. 2012;44:29.
Wu, X. L., Sun, C., Beissinger, T. M., Rosa, G. J., Weigel, K. A., Gatti, N. d. e. . L., & Gianola, D. (2012). Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics. Genetics, Selection, Evolution : GSE, 44, 29. https://doi.org/10.1186/1297-9686-44-29
Wu XL, et al. Parallel Markov Chain Monte Carlo - Bridging the Gap to High-performance Bayesian Computation in Animal Breeding and Genetics. Genet Sel Evol. 2012 Sep 25;44:29. PubMed PMID: 23009363.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics. AU - Wu,Xiao-Lin, AU - Sun,Chuanyu, AU - Beissinger,Timothy M, AU - Rosa,Guilherme Jm, AU - Weigel,Kent A, AU - Gatti,Natalia de Leon, AU - Gianola,Daniel, Y1 - 2012/09/25/ PY - 2012/02/07/received PY - 2012/08/28/accepted PY - 2012/9/27/entrez PY - 2012/9/27/pubmed PY - 2013/4/12/medline SP - 29 EP - 29 JF - Genetics, selection, evolution : GSE JO - Genet Sel Evol VL - 44 N2 - BACKGROUND: 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. 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. RESULTS: 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. CONCLUSIONS: 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. SN - 1297-9686 UR - https://www.unboundmedicine.com/medline/citation/23009363/Parallel_Markov_chain_Monte_Carlo___bridging_the_gap_to_high_performance_Bayesian_computation_in_animal_breeding_and_genetics_ L2 - https://gsejournal.biomedcentral.com/articles/10.1186/1297-9686-44-29 DB - PRIME DP - Unbound Medicine ER -