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.

中文翻译：

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评论：通过模型改进和数据收集优化杂交性能的基因组选择


旨在提高杂交品种性能的育种计划可能会受益于对纯种（PB）候选品种的杂交（CB）性能的基因组预测。在这篇综述中，我们比较了基因组预测策略，这些策略的不同之处在于 1) 使用的基因组预测模型或 2) 参考群体中使用的数据。我们发现了 27 项独特的研究，其中 2 项使用确定性模拟，11 项使用随机模拟，14 项使用真实数据。策略之间选择的准确性和响应差异取决于 i) 纯种杂交遗传相关性 (rpc) 的值，ii) 亲本系之间的遗传距离，iii) PB 和 CB 参考群体的大小，以及 iv)这些参考人群与选择候选人的相关性。在使用 PB 参考群体的研究中，使用显性模型产生的准确度等于或高于相加模型的准确度。当 rpc 低于 ~0.8 且主要由 G × E 引起时，有利于创建在 CB 环境中进行测试的 PB 动物参考群体。一般来说，收集CB信息的好处随着rpc的减少而增加。对于给定的 rpc，收集 CB 信息的好处随着参考群体规模的增加而增加。当 rpc 高于 ~0.9 时，收集 CB 信息没有好处，特别是当参考群体较小时。仅收集 CB 动物的表型可能会稍微提高选择的准确性和反应，但需要已知谱系。因此，建议对这些 CB 动物进行基因分型。 最后，考虑等位基因的品种起源可以在 CB 中对品种特异性效应进行建模，但这并不总能带来更高的准确性。我们的审查表明，策略选择的准确性和响应的差异取决于几个因素。最重要的因素之一是 rpc，因此，我们建议获得所有育种目标性状的 rpc 的准确估计。此外，了解 rpc 组成部分的重要性（即优势、上位性和 G × E）可以帮助育种者决定使用哪种模型，以及是否收集 CB 环境中动物的数据。未来的研究应该集中于开发一种工具，该工具可以预测场景特定参数选择的准确性和响应。