A Fast EM Algorithm for BayesA-Like Prediction of Genomic Breeding Values

Prediction accuracies of estimated breeding values for economically important traits are expected to benefit from genomic information. Single nucleotide polymorphism (SNP) panels used in genomic prediction are increasing in density, but the Markov Chain Monte Carlo (MCMC) estimation of SNP effects c...

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Main Authors:	Sun, Xiaochen, Qu, Long, Garrick, Dorian J., Dekkers, Jack C. M., Fernando, Rohan L.
Format:	Online
Language:	English
Published:	Public Library of Science 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3494698/

id	pubmed-3494698
recordtype	oai_dc
spelling	pubmed-34946982012-11-14 A Fast EM Algorithm for BayesA-Like Prediction of Genomic Breeding Values Sun, Xiaochen Qu, Long Garrick, Dorian J. Dekkers, Jack C. M. Fernando, Rohan L. Research Article Prediction accuracies of estimated breeding values for economically important traits are expected to benefit from genomic information. Single nucleotide polymorphism (SNP) panels used in genomic prediction are increasing in density, but the Markov Chain Monte Carlo (MCMC) estimation of SNP effects can be quite time consuming or slow to converge when a large number of SNPs are fitted simultaneously in a linear mixed model. Here we present an EM algorithm (termed “fastBayesA”) without MCMC. This fastBayesA approach treats the variances of SNP effects as missing data and uses a joint posterior mode of effects compared to the commonly used BayesA which bases predictions on posterior means of effects. In each EM iteration, SNP effects are predicted as a linear combination of best linear unbiased predictions of breeding values from a mixed linear animal model that incorporates a weighted marker-based realized relationship matrix. Method fastBayesA converges after a few iterations to a joint posterior mode of SNP effects under the BayesA model. When applied to simulated quantitative traits with a range of genetic architectures, fastBayesA is shown to predict GEBV as accurately as BayesA but with less computing effort per SNP than BayesA. Method fastBayesA can be used as a computationally efficient substitute for BayesA, especially when an increasing number of markers bring unreasonable computational burden or slow convergence to MCMC approaches. Public Library of Science 2012-11-09 /pmc/articles/PMC3494698/ /pubmed/23152868 http://dx.doi.org/10.1371/journal.pone.0049157 Text en © 2012 Sun et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Sun, Xiaochen Qu, Long Garrick, Dorian J. Dekkers, Jack C. M. Fernando, Rohan L.
spellingShingle	Sun, Xiaochen Qu, Long Garrick, Dorian J. Dekkers, Jack C. M. Fernando, Rohan L. A Fast EM Algorithm for BayesA-Like Prediction of Genomic Breeding Values
author_facet	Sun, Xiaochen Qu, Long Garrick, Dorian J. Dekkers, Jack C. M. Fernando, Rohan L.
author_sort	Sun, Xiaochen
title	A Fast EM Algorithm for BayesA-Like Prediction of Genomic Breeding Values
title_short	A Fast EM Algorithm for BayesA-Like Prediction of Genomic Breeding Values
title_full	A Fast EM Algorithm for BayesA-Like Prediction of Genomic Breeding Values
title_fullStr	A Fast EM Algorithm for BayesA-Like Prediction of Genomic Breeding Values
title_full_unstemmed	A Fast EM Algorithm for BayesA-Like Prediction of Genomic Breeding Values
title_sort	fast em algorithm for bayesa-like prediction of genomic breeding values
description	Prediction accuracies of estimated breeding values for economically important traits are expected to benefit from genomic information. Single nucleotide polymorphism (SNP) panels used in genomic prediction are increasing in density, but the Markov Chain Monte Carlo (MCMC) estimation of SNP effects can be quite time consuming or slow to converge when a large number of SNPs are fitted simultaneously in a linear mixed model. Here we present an EM algorithm (termed “fastBayesA”) without MCMC. This fastBayesA approach treats the variances of SNP effects as missing data and uses a joint posterior mode of effects compared to the commonly used BayesA which bases predictions on posterior means of effects. In each EM iteration, SNP effects are predicted as a linear combination of best linear unbiased predictions of breeding values from a mixed linear animal model that incorporates a weighted marker-based realized relationship matrix. Method fastBayesA converges after a few iterations to a joint posterior mode of SNP effects under the BayesA model. When applied to simulated quantitative traits with a range of genetic architectures, fastBayesA is shown to predict GEBV as accurately as BayesA but with less computing effort per SNP than BayesA. Method fastBayesA can be used as a computationally efficient substitute for BayesA, especially when an increasing number of markers bring unreasonable computational burden or slow convergence to MCMC approaches.
publisher	Public Library of Science
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3494698/
_version_	1611923235039870976

A Fast EM Algorithm for BayesA-Like Prediction of Genomic Breeding Values

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