Abstract
Genomic selection uses genome-wide dense SNP marker genotyping for the prediction of genetic values, and consists of two steps: (1) estimation of SNP effects, and (2) prediction of genetic value based on SNP genotypes and estimates of their effects. For the former step, BayesB type of estimators have been proposed, which assume a priori that many markers have no effects, and some have an effect coming from a gamma or exponential distribution, i.e. a fat-tailed distribution. Whilst such estimators have been developed using Monte Carlo Markov chain (MCMC), here we derive a much faster non-MCMC based estimator by analytically performing the required integrations. The accuracy of the genome-wide breeding value estimates was 0.011 (s.e. 0.005) lower than that of the MCMC based BayesB predictor, which may be because the integrations were performed one-by-one instead of for all SNPs simultaneously. The bias of the new method was opposite to that of the MCMC based BayesB, in that the new method underestimates the breeding values of the best selection candidates, whereas MCMC-BayesB overestimated their breeding values. The new method was computationally several orders of magnitude faster than MCMC based BayesB, which will mainly be advantageous in computer simulations of entire breeding schemes, in cross-validation testing, and practical schemes with frequent re-estimation of breeding values.
Highlights
The recent detection of thousands to millions of SNP markers and the dramatic improvements in high-throughput, cost effective genotyping technology have made it possible to apply marker assisted selection at a genome wide scale, which is termed genomic selection [1]
Genomic selection uses genome-wide dense SNP marker genotyping for the prediction of genetic values, and consists of two steps: (1) estimation of SNP effects, and (2) prediction of genetic value based on SNP genotypes and estimates of their effects
Since an equal variance for each of the marker effects seems unrealistic, the BayesA method extended the GS-Best Linear Unbiased Prediction (BLUP) method by estimating the variance of every marker separately, and an inverse chi-square prior was used for the estimation of these variances
Summary
The recent detection of thousands to millions of SNP markers and the dramatic improvements in high-throughput, cost effective genotyping technology have made it possible to apply marker assisted selection at a genome wide scale, which is termed genomic selection [1]. These authors suggested three methods for the estimation of genetic value from dense SNP marker data, namely GSBLUP, BayesA, and BayesB. Since an equal variance for each of the marker effects seems unrealistic, the BayesA method extended the GS-BLUP method by estimating the variance of every marker separately, and an inverse chi-square prior was used for the estimation of these variances. In a simulation study where the genetic model included a finite number of loci with exponentially distributed effects, BayesB provided more accurate prediction of genetic value than BayesA, which in turn was more accurate than GS-BLUP
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