Abstract
BackgroundIn dairy cattle populations in which crossbreeding has been used, animals show some level of diversity in their origins. In rotational crossbreeding, for instance, crossbred dams are mated with purebred sires from different pure breeds, and the genetic composition of crossbred animals is an admixture of the breeds included in the rotation. How to use the data of such individuals in genomic evaluations is still an open question. In this study, we aimed at providing methodologies for the use of data from crossbred individuals with an admixed genetic background together with data from multiple pure breeds, for the purpose of genomic evaluations for both purebred and crossbred animals. A three-breed rotational crossbreeding system was mimicked using simulations based on animals genotyped with the 50 K single nucleotide polymorphism (SNP) chip.ResultsFor purebred populations, within-breed genomic predictions generally led to higher accuracies than those from multi-breed predictions using combined data of pure breeds. Adding admixed population’s (MIX) data to the combined pure breed data considering MIX as a different breed led to higher accuracies. When prediction models were able to account for breed origin of alleles, accuracies were generally higher than those from combining all available data, depending on the correlation of quantitative trait loci (QTL) effects between the breeds. Accuracies varied when using SNP effects from any of the pure breeds to predict the breeding values of MIX. Using those breed-specific SNP effects that were estimated separately in each pure breed, while accounting for breed origin of alleles for the selection candidates of MIX, generally improved the accuracies. Models that are able to accommodate MIX data with the breed origin of alleles approach generally led to higher accuracies than models without breed origin of alleles, depending on the correlation of QTL effects between the breeds.ConclusionsCombining all available data, pure breeds’ and admixed population’s data, in a multi-breed reference population is beneficial for the estimation of breeding values for pure breeds with a small reference population. For MIX, such an approach can lead to higher accuracies than considering breed origin of alleles for the selection candidates, and using breed-specific SNP effects estimated separately in each pure breed. Including MIX data in the reference population of multiple breeds by considering the breed origin of alleles, accuracies can be further improved. Our findings are relevant for breeding programs in which crossbreeding is systematically applied, and also for populations that involve different subpopulations and between which exchange of genetic material is routine practice.
Highlights
In dairy cattle populations in which crossbreeding has been used, animals show some level of diversity in their origins
Karaman et al Genet Sel Evol (2021) 53:46 prediction, it depends on the proportion of genetic variance that is explained by genome-wide single nucleotide polymorphisms (SNPs), and the accuracy with which the effect of those SNPs is estimated [2, 3]
We propose a methodology that is suitable for genomic prediction using a reference population of multiple purebred and admixed individuals
Summary
In dairy cattle populations in which crossbreeding has been used, animals show some level of diversity in their origins. Karaman et al Genet Sel Evol (2021) 53:46 prediction, it depends on the proportion of genetic variance that is explained by genome-wide single nucleotide polymorphisms (SNPs), and the accuracy with which the effect of those SNPs is estimated [2, 3]. Both factors are conditional on the linkage disequilibrium (LD) between SNPs and quantitative trait loci (QTL) [1,2,3]. Accuracies of across-breed predictions are generally around zero, and combining data from multiple breeds has not notably improved accuracies in empirical studies [7,8,9,10]
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