Abstract
BackgroundTwo key findings from genomic selection experiments are 1) the reference population used must be very large to subsequently predict accurate genomic estimated breeding values (GEBV), and 2) prediction equations derived in one breed do not predict accurate GEBV when applied to other breeds. Both findings are a problem for breeds where the number of individuals in the reference population is limited. A multi-breed reference population is a potential solution, and here we investigate the accuracies of GEBV in Holstein dairy cattle and Jersey dairy cattle when the reference population is single breed or multi-breed. The accuracies were obtained both as a function of elements of the inverse coefficient matrix and from the realised accuracies of GEBV.MethodsBest linear unbiased prediction with a multi-breed genomic relationship matrix (GBLUP) and two Bayesian methods (BAYESA and BAYES_SSVS) which estimate individual SNP effects were used to predict GEBV for 400 and 77 young Holstein and Jersey bulls respectively, from a reference population of 781 and 287 Holstein and Jersey bulls, respectively. Genotypes of 39,048 SNP markers were used. Phenotypes in the reference population were de-regressed breeding values for production traits. For the GBLUP method, expected accuracies calculated from the diagonal of the inverse of coefficient matrix were compared to realised accuracies.ResultsWhen GBLUP was used, expected accuracies from a function of elements of the inverse coefficient matrix agreed reasonably well with realised accuracies calculated from the correlation between GEBV and EBV in single breed populations, but not in multi-breed populations. When the Bayesian methods were used, realised accuracies of GEBV were up to 13% higher when the multi-breed reference population was used than when a pure breed reference was used. However no consistent increase in accuracy across traits was obtained.ConclusionPredicting genomic breeding values using a genomic relationship matrix is an attractive approach to implement genomic selection as expected accuracies of GEBV can be readily derived. However in multi-breed populations, Bayesian approaches give higher accuracies for some traits. Finally, multi-breed reference populations will be a valuable resource to fine map QTL.
Highlights
Two key findings from genomic selection experiments are 1) the reference population used must be very large to subsequently predict accurate genomic estimated breeding values (GEBV), and 2) prediction equations derived in one breed do not predict accurate GEBV when applied to other breeds
We have investigated the accuracy of GEBV for dairy production traits in Holstein dairy cattle and Jersey dairy cattle when the reference population consists of Holstein bulls only, Jersey bulls only, or bulls of both breeds, with all bulls genotyped for approximately 50,000 markers
In this study we have investigated the agreement of expected accuracies obtained as a function of elements of the inverse coefficient matrix when a genomic relationship matrix is used, with accuracies of GEBV obtained by correlating GEBV and breeding values for bulls with a large number of daughters in both single breed and multi-breed populations
Summary
Two key findings from genomic selection experiments are 1) the reference population used must be very large to subsequently predict accurate genomic estimated breeding values (GEBV), and 2) prediction equations derived in one breed do not predict accurate GEBV when applied to other breeds. To calculate GEBV, first a prediction equation based on a large number of DNA markers, such as SNP (Single Nucleotide Polymorphisms) markers, is derived The effects of these markers are estimated in a reference population in which animals are both phenotyped and genotyped. The accuracy of GEBV predicted in this way has been evaluated in experiments involving dairy cattle populations in the United States, New Zealand, Australia, and the Netherlands [2,3,4]. These experiments used reference populations of between 650 and 4,500 progeny-tested Holstein-Friesian bulls, genotyped for approximately 50,000 genomewide markers. Accuracies of GEBV for young bulls whose phenotypes were not used in the reference population were between 0.4 and 0.82 across a range of traits
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