Recently, crossbred animals have begun to be used as parents in the next generations of dairy and beef cattle systems, which has increased the interest in predicting the genetic merit of those animals. The primary objective of this study was to investigate three available methods for genomic prediction of crossbred animals. In the first two methods, SNP effects from within-breed evaluations are used by weighting them by the average breed proportions across the genome (BPM method) or by their breed-of-origin (BOM method). The third method differs from the BOM in that it estimates breed-specific SNP effects using purebred and crossbred data, considering the breed-of-origin of alleles (BOA method). For within-breed evaluations, and thus for BPM and BOM, 5948 Charolais, 6771 Limousin and 7552 Others (a combined population of other breeds) were used to estimate SNP effects separately within each breed. For the BOA, the purebreds' data were enhanced with data from ~ 4K, ~ 8K or ~ 18K crossbred animals. For each animal, its predictor of genetic merit (PGM) was estimated by considering the breed-specific SNP effects. Predictive ability and absence of bias were estimated for crossbreds and the Limousin and Charolais animals. Predictive ability was measured as the correlation between PGM and the adjusted phenotype, while the regression of the adjusted phenotype on PGM was estimated as a measure of bias. With BPM and BOM, the predictive abilities for crossbreds were 0.468 and 0.472, respectively, and with the BOA method, they ranged from 0.490 to 0.510. The performance of the BOA method improved as the number of crossbred animals in the reference increased and with the use of the correlated approach, in which the correlation of SNP effects across the genome of the different breeds was considered. The slopes of regression for PGM on adjusted phenotypes for crossbreds showed overdispersion of the genetic merits for all methods but this bias tended to be reduced by the use of the BOA method and by increasing the number of crossbred animals. For the estimation of the genetic merit of crossbred animals, the results from this study suggest that the BOA method that accommodates crossbred data can yield more accurate predictions than the methods that use SNP effects from separate within-breed evaluations.

中文翻译：

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包含杂交数据的等位基因起源品种模型提高了多品种种群中杂交动物的预测能力

最近，杂交动物已开始被用作下一代奶牛和肉牛系统的亲本，这增加了人们对预测这些动物的遗传价值的兴趣。本研究的主要目的是研究杂交动物基因组预测的三种可用方法。在前两种方法中，品种内评估的 SNP 效应通过按整个基因组的平均品种比例（BPM 方法）或按其品种起源（BOM 方法）加权来使用。第三种方法与 BOM 的不同之处在于它使用纯种和杂交数据估计品种特异性 SNP 效应，考虑等位基因的品种来源（BOA 方法）。对于品种内评估，因此对于 BPM 和 BOM，5948 Charolais，6771 Limousin 和 7552 Others（其他品种的组合种群）用于分别估计每个品种内的 SNP 效应。对于 BOA，纯种动物的数据通过来自 ~ 4K、~ 8K 或 ~ 18K 杂交动物的数据得到增强。对于每只动物，其遗传优点 (PGM) 预测因子是通过考虑品种特异性 SNP 效应来估计的。对杂交种以及利木赞和夏洛来动物的预测能力和无偏差进行了评估。预测能力被衡量为 PGM 与调整后的表型之间的相关性，而调整后的表型对 PGM 的回归被估计为偏差的量度。使用 BPM 和 BOM，杂交种的预测能力分别为 0.468 和 0.472，使用 BOA 方法，它们的范围为 0.490 到 0.510。随着参考中杂交动物数量的增加和相关方法的使用，BOA 方法的性能得到改善，其中考虑了不同品种基因组中 SNP 效应的相关性。PGM 对杂交调整表型的回归斜率显示所有方法的遗传优点过度分散，但这种偏差往往通过使用 BOA 方法和增加杂交动物的数量来减少。对于杂交动物遗传价值的估计，这项研究的结果表明，与使用单独品种内评估的 SNP 效应的方法相比，适应杂交数据的 BOA 方法可以产生更准确的预测。其中考虑了不同品种基因组中 SNP 效应的相关性。PGM 对杂交调整表型的回归斜率显示所有方法的遗传优点过度分散，但这种偏差往往通过使用 BOA 方法和增加杂交动物的数量来减少。对于杂交动物遗传价值的估计，这项研究的结果表明，与使用单独品种内评估的 SNP 效应的方法相比，适应杂交数据的 BOA 方法可以产生更准确的预测。其中考虑了不同品种基因组中 SNP 效应的相关性。PGM 对杂交调整表型的回归斜率显示所有方法的遗传优点过度分散，但这种偏差往往通过使用 BOA 方法和增加杂交动物的数量来减少。对于杂交动物遗传价值的估计，这项研究的结果表明，与使用单独品种内评估的 SNP 效应的方法相比，适应杂交数据的 BOA 方法可以产生更准确的预测。PGM 对杂交调整表型的回归斜率显示所有方法的遗传优点过度分散，但这种偏差往往通过使用 BOA 方法和增加杂交动物的数量来减少。对于杂交动物遗传价值的估计，这项研究的结果表明，与使用单独品种内评估的 SNP 效应的方法相比，适应杂交数据的 BOA 方法可以产生更准确的预测。PGM 对杂交调整表型的回归斜率显示所有方法的遗传优点过度分散，但这种偏差往往通过使用 BOA 方法和增加杂交动物的数量来减少。对于杂交动物遗传价值的估计，这项研究的结果表明，与使用单独品种内评估的 SNP 效应的方法相比，适应杂交数据的 BOA 方法可以产生更准确的预测。