Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus C, Denmark.
ICBF, Link Road, Carrigrohane, Ballincollig, Co. Cork, P31 D452, Ireland.
Genet Sel Evol. 2023 May 15;55(1):34. doi: 10.1186/s12711-023-00806-1.
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.
最近，杂交动物开始被用作奶牛和肉牛系统下一代的亲本，这增加了对预测这些动物遗传优势的兴趣。本研究的主要目的是研究三种可用于杂交动物基因组预测的方法。在前两种方法中，通过加权平均品种比例（BPM 方法）或其起源品种（BOM 方法）来利用品种内评估的 SNP 效应。第三种方法与 BOM 不同，它使用纯系和杂交数据估计品种特异性 SNP 效应，同时考虑等位基因的起源品种（BOA 方法）。对于品种内评估，因此对于 BPM 和 BOM，使用了 5948 头夏洛来牛、6771 头利木赞牛和 7552 头其他品种（其他品种的混合群体）分别在每个品种内估计 SNP 效应。对于 BOA，通过将大约 4K、8K 或 18K 头杂交动物的数据添加到纯系数据中，增强了纯系数据。对于每只动物，通过考虑品种特异性 SNP 效应来估计其遗传优势预测值（PGM）。估计了杂交动物以及利木赞牛和夏洛来牛的预测能力和无偏性。预测能力是通过 PGM 与调整后的表型之间的相关性来衡量的，而调整后的表型对 PGM 的回归则作为偏倚的衡量标准。
使用 BPM 和 BOM，杂交动物的预测能力分别为 0.468 和 0.472，而使用 BOA 方法，预测能力范围为 0.490 到 0.510。随着参考杂交动物数量的增加和使用相关方法（考虑不同品种基因组中 SNP 效应的相关性），BOA 方法的性能得到了提高。对于所有方法，PGM 对调整后表型的回归斜率都显示出遗传优势的过分散，但通过使用 BOA 方法和增加杂交动物数量，可以减少这种偏差。
对于杂交动物遗传优势的估计，本研究的结果表明，考虑杂交数据的 BOA 方法比使用品种内评估的 SNP 效应的方法可以产生更准确的预测。
