Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G 2W1.
Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, N1G 2W1; Department of Animal Sciences, Purdue University, West Lafayette, IN 47907.
J Dairy Sci. 2022 Mar;105(3):2393-2407. doi: 10.3168/jds.2021-20558. Epub 2022 Jan 5.
Genomic evaluations are routine in most plant and livestock breeding programs but are used infrequently in dairy goat breeding schemes. In this context, the purpose of this study was to investigate the use of the single-step genomic BLUP method for predicting genomic breeding values for milk production traits (milk, protein, and fat yields; protein and fat percentages) in Canadian Alpine and Saanen dairy goats. There were 6,409 and 12,236 Alpine records and 3,434 and 5,008 Saanen records for each trait in first and later lactations, respectively, and a total of 1,707 genotyped animals (833 Alpine and 874 Saanen). Two validation approaches were used, forward validation (i.e., animals born after 2013 with an average estimated breeding value accuracy from the full data set ≥0.50) and forward cross-validation (i.e., subsets of all animals included in the forward validation were used in successive replications). The forward cross-validation approach resulted in similar validation accuracies (0.55 to 0.66 versus 0.54 to 0.61) and biases (-0.01 to -0.07 versus -0.03 to 0.11) to the forward validation when averaged across traits. Additionally, both single and multiple-breed analyses were compared, and similar average accuracies and biases were observed across traits. However, there was a small gain in accuracy from the use of multiple-breed models for the Saanen breed. A small gain in validation accuracy for genomically enhanced estimated breeding values (GEBV) relative to pedigree-based estimated breeding values (EBV) was observed across traits for the Alpine breed, but not for the Saanen breed, possibly due to limitations in the validation design, heritability of the traits evaluated, and size of the training populations. Trait-specific gains in theoretical accuracy of GEBV relative to EBV for the validation animals ranged from 17 to 31% in Alpine and 35 to 55% in Saanen, using the cross-validation approach. The GEBV predicted from the full data set were 12 to 16% more accurate than EBV for genotyped animals, but no gains were observed for nongenotyped animals. The largest gains were found for does without lactation records (35-41%) and bucks without daughter records (46-54%), and consequently, the implementation of genomic selection in the Canadian dairy goat population would be expected to increase selection accuracy for young breeding candidates. Overall, this study represents the first step toward implementation of genomic selection in Canadian dairy goat populations.
基因组评估在大多数植物和牲畜育种计划中是常规操作，但在奶牛山羊育种计划中却很少使用。在这种情况下，本研究的目的是调查单步基因组 BLUP 方法在预测加拿大阿尔卑斯山羊和莎能奶山羊产奶量（奶、蛋白和脂肪产量；蛋白和脂肪百分比）性状的基因组育种值中的应用。在第一泌乳期和后期泌乳期，每个性状分别有 6409 个和 12236 个阿尔卑斯山羊记录，3434 个和 5008 个莎能山羊记录，共有 1707 只基因型动物（833 只阿尔卑斯山羊和 874 只莎能山羊）。使用了两种验证方法，前向验证（即 2013 年后出生的动物，其全数据集的平均估计育种值准确性≥0.50）和前向交叉验证（即将前向验证中包含的所有动物的子集用于连续重复）。平均而言，前向交叉验证方法的验证准确性（0.55 到 0.66 与 0.54 到 0.61）和偏差（-0.01 到-0.07 与-0.03 到 0.11）与前向验证相似。此外，还比较了单品种和多品种分析，观察到各性状的平均准确性和偏差相似。然而，对于莎能品种，使用多品种模型可以略微提高准确性。在阿尔卑斯品种中，与基于系谱的估计育种值（EBV）相比，基因组增强的估计育种值（GEBV）的验证准确性略有提高，但在莎能品种中没有观察到，这可能是由于验证设计、所评估性状的遗传力和训练群体的大小限制。使用交叉验证方法，验证动物的 GEBV 相对于 EBV 的理论准确性的特定性状增益范围为 17%到 31%，在阿尔卑斯山羊中为 35%到 55%。全数据集预测的 GEBV 比基因型动物的 EBV 准确 12%到 16%，但非基因型动物没有观察到增益。增益最大的是没有泌乳记录的母羊（35%到 41%）和没有女儿记录的公羊（46%到 54%），因此，预计在加拿大奶牛山羊群体中实施基因组选择将提高对年轻候选育种者的选择准确性。总的来说，本研究代表了在加拿大奶牛山羊群体中实施基因组选择的第一步。
