Department of Animal and Dairy Science, University of Georgia, 425 River Road, Athens 30602.
Zoetis, 333 Portage Street, Kalamazoo, MI 49007.
J Dairy Sci. 2021 May;104(5):5728-5737. doi: 10.3168/jds.2020-19451. Epub 2021 Mar 6.
The objective of this study was to predict genomic breeding values for milk yield of crossbred dairy cattle under different scenarios using single-step genomic BLUP (ssGBLUP). The data set included 13,880,217 milk yield measurements on 6,830,415 cows. Genotypes of 89,558 Holstein, 40,769 Jersey, and 22,373 Holstein-Jersey crossbred animals were used, of which all Holstein, 9,313 Jersey, and 1,667 crossbred animals had phenotypic records. Genotypes were imputed to 45K SNP markers. The SNP effects were estimated from single-breed evaluations for Jersey (JE), Holstein (HO) and crossbreds (CROSS), and multibreed evaluations including all Jersey and Holstein (JE_HO) or approximately equal proportions of Jersey, Holstein, and crossbred animals (MIX). Indirect predictions (IP) of the validation animals (358 crossbred animals with phenotypes excluded from evaluations) were calculated using the resulting SNP effects. Additionally, breed proportions (BP) of crossbred animals were applied as a weight when IP were estimated based on each pure breed. The predictive ability of IP was calculated as the Pearson correlation between IP and phenotypes of the validation animals adjusted for fixed effects in the model. Regression of adjusted phenotypes on IP was used to assess the inflation of IP. The predictive ability of IP for CROSS, JE, HO, JE_HO, and MIX scenario was 0.50, 0.50, 0.47, 0.50, and 0.46, respectively. Using BP was the least successful, with a predictive ability of 0.32. The inflation of the IP for crossbred animals using CROSS, JE, HO, JE_HO, MIX, and BP scenarios were 1.17, 0.65, 0.55, 0.78, 1.00, and 0.85, respectively. The IP of crossbred animals can be predicted using single-step GBLUP under a scenario that includes purebred genotypes.
本研究旨在使用一步法基因组 BLUP（ssGBLUP），针对不同情景下的杂交奶牛产奶量进行基因组育种值预测。该数据集包含 6830415 头奶牛的 13880217 次产奶量测量数据。使用了 89558 头荷斯坦、40769 头泽西和 22373 头荷斯坦-泽西杂交动物的基因型，其中所有荷斯坦、9313 头泽西和 1667 头杂交动物都有表型记录。基因型被导入到 45K SNP 标记中。利用对泽西牛（JE）、荷斯坦牛（HO）和杂交牛（CROSS）的单一品种评估、包括所有泽西牛和荷斯坦牛的多品种评估（JE_HO）或大约相等比例的泽西牛、荷斯坦牛和杂交牛的多品种评估（MIX）来估计 SNP 效应。利用由此产生的 SNP 效应，对验证动物（358 头排除在评估之外的杂交动物）进行间接预测（IP）。此外，当基于每个纯品种进行 IP 估计时，应用杂交动物的品种比例（BP）作为权重。通过对模型中的固定效应进行调整，将 IP 与验证动物的表型进行 Pearson 相关，计算 IP 的预测能力。通过回归调整后的表型与 IP，评估 IP 的膨胀情况。CROSS、JE、HO、JE_HO 和 MIX 情景下 IP 的预测能力分别为 0.50、0.50、0.47、0.50 和 0.46。使用 BP 的效果最差，预测能力为 0.32。使用 CROSS、JE、HO、JE_HO、MIX 和 BP 情景下的 IP 对杂交动物的膨胀情况分别为 1.17、0.65、0.55、0.78、1.00 和 0.85。在包括纯种种群基因型的情景下，可以使用一步法 GBLUP 预测杂交动物的 IP。
