Implementation and accuracy of genomic selection

Genomic selection is emerging as a powerful tool for the estimation of breeding values in plant and animal breeding. While many analytical approaches have been proposed for the joint estimation of high-density single nucleotide polymorphism (SNP) effects, within the framework of best linear unbiased estimation, genomic selection is equivalent to the prediction of breeding values for individuals with no phenotypes, for which the theoretical solution was first published in 1974. Genomic selection simply replaces the pedigree-derived numerator relationship matrix with the marker-derived realized genomic relationship matrix, an approach first proposed in 1997. The advance facilitated by the availability of high-density SNP genotypes is the ability to precisely estimate realized relationship coefficients among individuals regardless of the availability of pedigree information or the history of selection that has been applied to the population. However, genomic relationship coefficients are usually estimated assuming the independence of SNP genotypes, thus ignoring the effects of linkage disequilibrium, and the utilized SNPs are invariably ascertained to be common variants within the specie's genome which leads to the overestimation of relationship coefficients. The accuracy of the produced genomic estimated breeding values (GEBV) is often evaluated using variously formed validation populations incorporating individuals with genotypes and phenotypes that were not used for the estimation of SNP effects in the training population. However, GEBV accuracies are shown here to be a function of the accuracy of training population GEBV and the magnitudes of genomic relationships between individuals in the training and validation populations. Consequently, genomic selection is ideally suited to populations in which highly accurate GEBV are available for training population individuals and whose marker-selected progeny go on to produce phenotypes and reenter the training population which then becomes dynamic. Conversely, genomic selection is not well suited to the identification of elite individuals within families that have not historically contributed to breeding programs, to static training populations, or to training and implementation in distantly related populations. Thus, the implementation of genomic selection for costly or difficult to measure phenotypes such as feed efficiency or disease resistance will require the periodic regeneration of phenotyped populations for the retraining of GEBV prediction equations or the identification of the causal variants which underlie variation in these traits. The exponentially reducing cost of whole genome resequencing may soon allow the identification of at least the large effect variants. (C) 2013 Elsevier B.V. All rights reserved.