Animal Breeding and Genomics, Wageningen University and Research, P.O. Box 338, 6700 AH Wageningen, The Netherlands.
J Anim Sci. 2021 Aug 1;99(8). doi: 10.1093/jas/skab205.
Breeding programs aiming to improve the performance of crossbreds may benefit from genomic prediction of crossbred (CB) performance for purebred (PB) selection candidates. In this review, we compared genomic prediction strategies that differed in 1) the genomic prediction model used or 2) the data used in the reference population. We found 27 unique studies, two of which used deterministic simulation, 11 used stochastic simulation, and 14 real data. Differences in accuracy and response to selection between strategies depended on i) the value of the purebred crossbred genetic correlation (rpc), ii) the genetic distance between the parental lines, iii) the size of PB and CB reference populations, and iv) the relatedness of these reference populations to the selection candidates. In studies where a PB reference population was used, the use of a dominance model yielded accuracies that were equal to or higher than those of additive models. When rpc was lower than ~0.8, and was caused mainly by G × E, it was beneficial to create a reference population of PB animals that are tested in a CB environment. In general, the benefit of collecting CB information increased with decreasing rpc. For a given rpc, the benefit of collecting CB information increased with increasing size of the reference populations. Collecting CB information was not beneficial when rpc was higher than ~0.9, especially when the reference populations were small. Collecting only phenotypes of CB animals may slightly improve accuracy and response to selection, but requires that the pedigree is known. It is, therefore, advisable to genotype these CB animals as well. Finally, considering the breed-origin of alleles allows for modeling breed-specific effects in the CB, but this did not always lead to higher accuracies. Our review shows that the differences in accuracy and response to selection between strategies depend on several factors. One of the most important factors is rpc, and we, therefore, recommend to obtain accurate estimates of rpc of all breeding goal traits. Furthermore, knowledge about the importance of components of rpc (i.e., dominance, epistasis, and G × E) can help breeders to decide which model to use, and whether to collect data on animals in a CB environment. Future research should focus on the development of a tool that predicts accuracy and response to selection from scenario specific parameters.
旨在提高杂交种性能的选育计划可能受益于对纯系选育候选者的杂交种（CB）性能进行基因组预测。在本综述中，我们比较了在 1）所使用的基因组预测模型或 2）参考群体中使用的数据方面有所不同的基因组预测策略。我们发现了 27 项独特的研究，其中两项使用了确定性模拟，11 项使用了随机模拟，14 项使用了真实数据。策略之间的准确性和选择响应的差异取决于 i）纯系杂交遗传相关系数（rpc）的值，ii）亲本系之间的遗传距离，iii）纯系和杂交参考群体的大小，以及 iv）这些参考群体与选育候选者的亲缘关系。在使用纯系参考群体的研究中，使用显性模型产生的准确性与加性模型相当或更高。当 rpc 低于约 0.8 时，并且主要由 G×E 引起时，创建在 CB 环境中进行测试的纯系动物参考群体是有益的。一般来说，随着 rpc 的降低，收集 CB 信息的好处增加。对于给定的 rpc，随着参考群体的增大，收集 CB 信息的好处增加。当 rpc 高于约 0.9 时，收集 CB 信息没有好处，尤其是当参考群体较小时。仅收集 CB 动物的表型可能会略微提高准确性和选择响应，但需要知道系谱。因此，建议对这些 CB 动物进行基因分型。最后，考虑等位基因的起源允许在 CB 中对品种特异性效应进行建模，但这并不总是导致更高的准确性。我们的综述表明，策略之间的准确性和选择响应的差异取决于几个因素。最重要的因素之一是 rpc，因此，我们建议获得所有选育目标性状的 rpc 的准确估计值。此外，了解 rpc 组成部分的重要性（即显性、上位性和 G×E）可以帮助饲养员决定使用哪种模型，以及是否在 CB 环境中收集动物数据。未来的研究应侧重于开发一种从特定于场景的参数预测准确性和选择响应的工具。
