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 Jul;105(7):5985-6000. doi: 10.3168/jds.2021-21713. Epub 2022 May 6.
Conformation traits are functional traits known to affect longevity, production efficiency, and profitability of dairy goats. However, genetic progress for these traits is expected to be slower than for milk production traits due to the limited number of herds participating in type classification programs, and often lower heritability estimates. Genomic selection substantially accelerates the rate of genetic progress in many species and industries, especially for lowly heritable, difficult, or expensive to measure traits. Therefore, the main objectives of this study were (1) to evaluate the potential benefits of the implementation of single-step genomic evaluations for conformation traits in Canadian Alpine and Saanen dairy goats, and (2) to investigate the effect of the use of single- and multiple-breed training populations. The phenotypes used in this study were linear conformation scores, on a 1-to-9 scale, for 8 traits (i.e., body capacity, dairy character, fore udder, feet and legs, general appearance, rear udder, medial suspensory ligament, and teats) of 5,158 Alpine and 2,342 Saanen does. Genotypes were available for 833 Alpine and 874 Saanen animals. Averaged across all traits, the use of multiple-breed analyses increased validation accuracy for Saanen, and reduced bias of genomically enhanced breeding values (GEBV) for both Alpine and Saanen compared with single-breed analyses. Little benefit was observed from the use of GEBV relative to pedigree-based EBV in terms of validation accuracy and bias, possibly due to limitations in the validation design, but substantial gains of 0.14 to 0.21 (32-50%) were observed in the theoretical accuracy of validation animals when averaged across traits for single- and multiple-breed analyses. Across the whole genotyped population, average gains in theoretical accuracy for GEBV compared with EBV across all traits ranged from 0.15 to 0.17 (32-37%) for Alpine and 0.17 to 0.19 (40-41%) for Saanen, depending on the model used. The largest gains were observed for does without classification records (0.19-0.22 or 50-55%) and bucks without daughter classification records (0.20-0.27 or 57-82%), which have the least information contributing to their traditional EBV. The use of multiple-breed rather than single-breed models was most beneficial for the Saanen breed, which had fewer phenotypic records available for the analyses. These results suggest that the implementation of genomic selection could increase the accuracy of breeding values for conformation traits in Canadian dairy goats.
体形体况特征是影响奶山羊长寿、生产效率和盈利能力的功能特征。然而,由于参与体型分类计划的羊群数量有限,而且通常遗传力估计值较低,因此这些特征的遗传进展预计会比产奶量特征缓慢。基因组选择极大地加速了许多物种和行业的遗传进展速度,特别是对于遗传力低、难以测量或昂贵的性状。因此,本研究的主要目的是:(1) 评估在加拿大阿尔卑斯山羊和萨能奶山羊中实施单一步骤基因组评估对体形体况特征的潜在益处;(2) 研究使用单一和多品种训练群体的效果。本研究使用的表型是 5158 只阿尔卑斯山羊和 2342 只萨能奶山羊的 8 个性状(即身体容量、奶用特征、前乳房、脚和腿、总体外观、后乳房、内侧悬韧带和乳头)的 1 到 9 线性体况评分。833 只阿尔卑斯山羊和 874 只萨能奶山羊的基因型可用。平均而言,与单一品种分析相比,多品种分析增加了萨能奶山羊的验证准确性,并降低了阿尔卑斯山羊和萨能奶山羊的基因组增强育种值(GEBV)的偏差。与基于系谱的 EBV 相比,GEBV 在验证准确性和偏差方面几乎没有好处,这可能是由于验证设计的限制,但在单一和多品种分析中,平均而言,在所有性状上,验证动物的理论准确性平均提高了 0.14 到 0.21(32-50%)。在整个基因分型群体中,与 EBV 相比,GEBV 在所有性状上的理论准确性平均提高了 0.15 到 0.17(32-37%),对于阿尔卑斯山羊,对于萨能奶山羊为 0.17 到 0.19(40-41%),具体取决于所使用的模型。在没有分类记录的母羊(0.19-0.22 或 50-55%)和没有女儿分类记录的公羊(0.20-0.27 或 57-82%)中观察到最大收益,因为它们的传统 EBV 贡献信息最少。对于萨能奶山羊品种,使用多品种而不是单一品种模型最有益,因为该品种可用于分析的表型记录较少。这些结果表明,基因组选择的实施可以提高加拿大奶山羊体形体况特征的育种值准确性。