Abstract
BackgroundIn 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.ResultsEM 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 is inferior to that with SSVS based on MCMC algorithm which is currently considered to be a method of choice for genomic selection.ConclusionsEM-based wBSR method proposed in this study is much advantageous over MCMC-based Bayesian methods in computational time and can predict GBV more accurately than MCMC-based BSR. Therefore, wBSR is considered a practical method for genomic selection with a large number of SNP markers.
Highlights
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
In stochastic search variable selection (SSVS), we investigated the accuracies of predicted GBVs for p = 0.01, 0.05, 0.1, 0.2 and 0.5 in Data I but for p = 0.01, 0.05 and 0.1 in Data II due to large computational time required for Markov chain Monte Carlo (MCMC) algorithm
The accuracies of the predicted GBVs obtained by several methods for genomic selection were evaluated in 100 simulated data sets of Data I and in 20 data sets of Data II, where we assumed that 1010 SNP markers and 10100 SNP markers were available on a whole genome for Data I and Data II, respectively
Summary
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. Meuwissen et al [1] used block-updating for a SNP effect and a variance to prevent the estimate from being stuck at zero In this simultaneous update, a variance is assigned a zero or sampled from a prior inverted chi-square distribution following a prior mixture probability, which is a prior probability of each SNP to be included in the model, and a SNP effect is obtained from a conditional normal distribution given a variance. A variance is assigned a zero or sampled from a prior inverted chi-square distribution following a prior mixture probability, which is a prior probability of each SNP to be included in the model, and a SNP effect is obtained from a conditional normal distribution given a variance Taking these things into consideration, we use more general statistical terms BSR and SSVS for BayesA and BayesB, respectively, hereafter in this paper for the help of understanding of readers in broad research fields. BayesB can be interpreted as a variant of original SSVS as noted above, we use the term ‘SSVS’ for BayesB, which could cause no confusion
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