Can I read EM algorithm for Bayesian estimation of genomic breeding values on EtoBox?

EM algorithm for Bayesian estimation of genomic breeding values by Takeshi Hayashi; Hiroyoshi Iwata is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is EM algorithm for Bayesian estimation of genomic breeding values about?

Background: In genomic selection, a model for prediction of genome-wide breeding value (GBV) is constructed by estimating a large number of SNP effects that are included in a model. Two Bayesian methods based on MCMC algorithm, Bayesian shrinkage regression (BSR) method and stochastic search variable selection (SSVS) method, (which are called BayesA and BayesB, respectively, in some literatures), have been so far proposed for the estimation of SNP effects. However, much computational burden is imposed on the MCMC-based Bayesian methods. A method with both high computing efficiency and prediction accuracy is desired to be developed for practical use of genomic selection. Results: EM algorithm applicable for BSR is described. Subsequently, we propose a new EM-based Bayesian method, called wBSR (weighted BSR), which is a modification of BSR incorporating a weight for each SNP according to the strength of its association to a trait. Simulation experiments show that the computational time is much reduced with wBSR based on EM algorithm and the accuracy in predicting GBV is improved by wBSR in comparison with BSR based on MCMC algorithm. However, the accuracy of predicted GBV with wBSR i

Who reads EM algorithm for Bayesian estimation of genomic breeding values?

Author

Takeshi Hayashi; Hiroyoshi Iwata

Publisher

BioMed Central; Springer (Biomed Central Ltd.); London: BioMed Central, 2000-; Springer Science and Business Media LLC (ISSN 1471-2156)

Published

2010

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)