Departement of Animal Science, Federal University of Viçosa, Viçosa 36570-000, Brazil.
Embrapa Dairy Cattle, Juiz de Fora 36.038-330, Brazil.
J Dairy Sci. 2019 Jul;102(7):6330-6339. doi: 10.3168/jds.2018-15191. Epub 2019 May 2.
The multiple-lactation autoregressive test-day (AR) model is the adopted model for the national genetic evaluation of dairy cattle in Portugal. Under this model, animals' permanent environment effects are assumed to follow a first-order autoregressive process over the long (auto-correlations between parities) and short (auto-correlations between test-days within lactation) terms. Given the relevance of genomic prediction in dairy cattle, it is essential to include marker information in national genetic evaluations. In this context, we aimed to evaluate the feasibility of applying the single-step genomic (G)BLUP to analyze milk yield using the AR model in Portuguese Holstein cattle. In total, 11,434,294 test-day records from the first 3 lactations collected between 1994 and 2017 and 1,071 genotyped bulls were used in this study. Rank correlations and differences in reliability among bulls were used to compare the performance of the traditional (A-AR) and single-step (H-AR) models. These 2 modeling approaches were also applied to reduced data sets with records truncated after 2012 (deleting daughters of tested bulls) to evaluate the predictive ability of the H-AR. Validation scenarios were proposed, taking into account young and proven bulls. Average EBV reliabilities, empirical reliabilities, and genetic trends predicted from the complete and reduced data sets were used to validate the genomic evaluation. Average EBV reliabilities for H-AR (A-AR) using the complete data set were 0.52 (0.16) and 0.72 (0.62) for genotyped bulls with no daughters and bulls with 1 to 9 daughters, respectively. These results showed an increase in EBV reliabilities of 0.10 to 0.36 when genomic information was included, corresponding to a reduction of up to 43% in prediction error variance. Considering the 3 validation scenarios, the inclusion of genomic information improved the average EBV reliability in the reduced data set, which ranged, on average, from 0.16 to 0.26, indicating an increase in the predictive ability. Similarly, empirical reliability increased by up to 0.08 between validation tests. The H-AR outperformed A-AR in terms of genetic trends when unproven genotyped bulls were included. The results suggest that the single-step GBLUP AR model is feasible and may be applied to national Portuguese genetic evaluations for milk yield.
多次泌乳自回归测试日（AR）模型是葡萄牙奶牛全国遗传评估所采用的模型。根据该模型，动物的永久环境效应被假定为长期（胎次之间的自相关）和短期（泌乳期内测试日之间的自相关）呈一阶自回归过程。鉴于基因组预测在奶牛中的重要性，在全国遗传评估中纳入标记信息至关重要。在这种情况下，我们旨在评估应用单步基因组（G）BLUP 分析葡萄牙荷斯坦奶牛泌乳量的可行性，使用 AR 模型。本研究共使用了 1994 年至 2017 年期间收集的前 3 胎的 11434294 个测试日记录和 1071 头已基因分型的公牛。使用秩相关和公牛可靠性差异来比较传统（A-AR）和单步（H-AR）模型的表现。这两种建模方法还应用于记录在 2012 年后截断的简化数据集（删除已测试公牛的女儿），以评估 H-AR 的预测能力。提出了验证方案，考虑了年轻和已证明的公牛。从完整和简化数据集预测的平均 EBV 可靠性、经验可靠性和遗传趋势用于验证基因组评估。使用完整数据集时，H-AR（A-AR）的平均 EBV 可靠性分别为 0.52（0.16）和 0.72（0.62），对于没有女儿的基因分型公牛和有 1 到 9 个女儿的公牛。这些结果表明，当纳入基因组信息时，EBV 可靠性提高了 0.10 到 0.36，这对应于预测误差方差降低了高达 43%。考虑到 3 个验证方案，纳入基因组信息提高了简化数据集的平均 EBV 可靠性，平均范围从 0.16 到 0.26，表明预测能力有所提高。同样，经验可靠性在验证测试之间增加了高达 0.08。当包括未经证实的基因分型公牛时，H-AR 在遗传趋势方面优于 A-AR。结果表明，单步 GBLUP AR 模型是可行的，可应用于葡萄牙全国牛奶产量的遗传评估。
