Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, N1G 2W1, Canada; Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Minas Gerais, 36570-000, Brazil.
Department of Animal and Dairy Science, University of Georgia, Athens 30602.
J Dairy Sci. 2019 Mar;102(3):2365-2377. doi: 10.3168/jds.2018-15466. Epub 2019 Jan 11.
Test-day traits are important for genetic evaluation in dairy cattle and are better modeled by multiple-trait random regression models (RRM). The reliability and bias of genomic estimated breeding values (GEBV) predicted using multiple-trait RRM via single-step genomic best linear unbiased prediction (ssGBLUP) were investigated in the 3 major dairy cattle breeds in Canada (i.e., Ayrshire, Holstein, and Jersey). Individual additive genomic random regression coefficients for the test-day traits were predicted using 2 multiple-trait RRM: (1) one for milk, fat, and protein yields in the first, second, and third lactations, and (2) one for somatic cell score in the first, second, and third lactations. The predicted coefficients were used to derive GEBV for each lactation day and, subsequently, the daily GEBV were compared with traditional daily parent averages obtained by BLUP. To ensure compatibility between pedigree and genomic information for genotyped animals, different scaling factors for combining the inverse of genomic (G) and pedigree (A) relationship matrices were tested. In addition, the inclusion of only genotypes from animals with accurate breeding values (defined in preliminary analysis) was compared with the inclusion of all available genotypes in the analyzes. The ssGBLUP model led to considerably larger validation reliabilities than the BLUP model without genomic information. In general, scaling factors used to combine the G and A matrices had small influence on the validation reliabilities. However, a greater effect was observed in the inflation of GEBV. Less inflated GEBV were obtained by the ssGBLUP compared with the parent average from traditional BLUP when using optimal scaling factors to combine the G and A matrices. Similar results were observed when including either all available genotypes or only genotypes from animals with accurate breeding values. These findings indicate that ssGBLUP using multiple-trait RRM increases reliability and reduces bias of breeding values of young animals when compared with parent average from traditional BLUP in the Canadian Ayrshire, Holstein, and Jersey breeds.
测试日性状对于奶牛的遗传评估很重要，并且可以通过多性状随机回归模型（RRM）更好地建模。通过单步基因组最佳线性无偏预测（ssGBLUP）使用多性状 RRM 预测的基因组估计育种值（GEBV）的可靠性和偏差在加拿大的 3 个主要奶牛品种（即阿伯丁安格斯牛、荷斯坦牛和泽西牛）中进行了研究。使用 2 个多性状 RRM 预测了测试日性状的个体加性基因组随机回归系数：（1）一个用于第 1、第 2 和第 3 泌乳期的牛奶、脂肪和蛋白质产量，（2）一个用于第 1、第 2 和第 3 泌乳期的体细胞评分。预测的系数用于获得每个泌乳日的 GEBV，随后将每日 GEBV 与 BLUP 获得的传统每日亲本平均值进行比较。为了确保遗传动物的系谱和基因组信息之间的兼容性，测试了组合基因组（G）和系谱（A）关系矩阵的逆的不同缩放因子。此外，将仅包含具有准确育种值的动物的基因型（在初步分析中定义）与在分析中包含所有可用基因型进行了比较。ssGBLUP 模型导致验证可靠性大大高于没有基因组信息的 BLUP 模型。一般来说，用于组合 G 和 A 矩阵的缩放因子对验证可靠性的影响很小。然而，在 GEBV 的膨胀方面观察到了更大的影响。与传统 BLUP 中的亲本平均值相比，使用最佳缩放因子组合 G 和 A 矩阵时，ssGBLUP 获得的 GEBV 膨胀较小。当包含所有可用基因型或仅包含具有准确育种值的动物的基因型时，观察到类似的结果。这些发现表明，与传统 BLUP 中的亲本平均值相比，在加拿大阿伯丁安格斯牛、荷斯坦牛和泽西牛品种中，使用多性状 RRM 的 ssGBLUP 增加了年轻动物的可靠性并降低了育种值的偏差。
