a fast em algorithm for bayesa-like prediction of genomic breeding values快速em算法bayesa-like预测基因组育种值.pdfVIP

A Fast EM Algorithm for BayesA-Like Prediction of Genomic Breeding Values 1 1,2 1 1 1 Xiaochen Sun , Long Qu , Dorian J. Garrick , Jack C. M. Dekkers , Rohan L. Fernando * 1 Department of Animal Science and Center for Integrated Animal Genomics, Iowa State University, Ames, Iowa, United States of America, 2 Department of Statistics, Iowa State University, Ames, Iowa, United States of America Abstract 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