In 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. For 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. Combining 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.

中文翻译：

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使用多个纯品种和混合个体的参考群体进行基因组预测

在使用杂交的奶牛种群中，动物的起源表现出一定程度的多样性。例如，在轮作杂交中，杂交水坝与来自不同纯种的纯种公牛交配，杂交动物的遗传组成是轮作中包含的品种的混合物。如何在基因组评估中使用这些个体的数据仍然是一个悬而未决的问题。在这项研究中，我们旨在提供使用来自具有混合遗传背景的杂交个体的数据以及来自多个纯品种的数据的方法，以便对纯种和杂交动物进行基因组评估。使用基于用 50 K 单核苷酸多态性 (SNP) 芯片进行基因分型的动物的模拟来模拟三品种轮作杂交系统。对于纯种种群，品种内基因组预测通常比使用纯品种组合数据的多品种预测具有更高的准确性。将混合种群 (MIX) 数据添加到合并的纯品种数据中，将 MIX 视为不同品种会导致更高的准确性。当预测模型能够解释等位基因的品种起源时，根据品种之间数量性状位点 (QTL) 效应的相关性，准确度通常高于组合所有可用数据的准确度。使用来自任何纯品种的 SNP 效应来预测 MIX 的育种值时，准确性各不相同。使用在每个纯品种中单独估计的那些品种特异性 SNP 效应，同时考虑 MIX 选择候选者的等位基因的品种起源，总体上提高了精度。能够容纳具有等位基因品种起源方法的 MIX 数据的模型通常比没有等位基因品种起源的模型具有更高的准确度，这取决于品种之间 QTL 效应的相关性。结合所有可用数据、纯品种和混合种群的数据，在多品种参考种群中，有利于估计具有少量参考种群的纯品种的育种值。对于 MIX，这种方法比考虑选择候选者的等位基因的品种起源以及使用在每个纯品种中单独估计的品种特异性 SNP 效应具有更高的准确性。通过考虑等位基因的品种来源，将MIX数据纳入多个品种的参考群体中，可以进一步提高准确性。