Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada N1G 2W1.
Department of Animal and Dairy Science, University of Georgia, Athens 30602.
J Dairy Sci. 2019 Sep;102(9):8175-8183. doi: 10.3168/jds.2018-15819. Epub 2019 Jul 10.
The use of multi-trait across-country evaluation (MACE) and the exchange of genomic information among countries allows national breeding programs to combine foreign and national data to increase the size of the training populations and potentially increase accuracy of genomic prediction of breeding values. By including genotyped and nongenotyped animals simultaneously in the evaluation, the single-step genomic BLUP (GBLUP) approach has the potential to deliver more accurate and less biased genomic evaluations. A single-step genomic BLUP approach, which enables integration of data from MACE evaluations, can be used to obtain genomic predictions while avoiding double-counting of information. The objectives of this study were to apply a single-step approach that simultaneously includes domestic and MACE information for genomic evaluation of workability traits in Canadian Holstein cattle, and compare the results obtained with this methodology with those obtained using a multi-step approach (msGBLUP). By including MACE bulls in the training population, msGBLUP led to an increase in reliability of genomic predictions of 4.8 and 15.4% for milking temperament and milking speed, respectively, compared with a traditional evaluation using only pedigree and phenotypic information. Integration of MACE data through a single-step approach (ssGBLUP) yielded the highest reliabilities compared with other considered methods. Integration of MACE data also helped reduce bias of genomic predictions. When using ssGBLUP, the bias of genomic predictions decreased by half compared with msGBLUP using domestic and MACE information. Therefore, the reliability and bias of genomic predictions for both traits improved substantially when a single-step approach was used for evaluation compared with a multi-step approach. The use of a single-step approach with integration of MACE information provides an alternative to the current method used in Canadian genomic evaluations.
多性状跨国评估（MACE）和国家间基因组信息交换允许国家的育种计划将国外和国内数据相结合，以增加训练群体的规模，并有可能提高对育种值的基因组预测的准确性。通过同时将基因型和非基因型动物纳入评估，单步基因组 BLUP（GBLUP）方法具有提供更准确和偏差更小的基因组评估的潜力。单步基因组 BLUP 方法可以集成 MACE 评估的数据，用于获得基因组预测，同时避免信息的重复计算。本研究的目的是应用一种单步方法，同时包括加拿大荷斯坦奶牛工作能力性状的国内和 MACE 信息，用于基因组评估，并将该方法的结果与使用多步方法（msGBLUP）的结果进行比较。通过将 MACE 公牛纳入训练群体，与仅使用系谱和表型信息的传统评估相比，msGBLUP 分别使泌乳性情和挤奶速度的基因组预测可靠性提高了 4.8%和 15.4%。与其他考虑的方法相比，通过单步方法（ssGBLUP）集成 MACE 数据产生了最高的可靠性。MACE 数据的集成还有助于减少基因组预测的偏差。当使用 ssGBLUP 时，与使用国内和 MACE 信息的 msGBLUP 相比，基因组预测的偏差减少了一半。因此，与多步方法相比，使用单步方法进行评估可显著提高这两个性状的基因组预测的可靠性和偏差。使用单步方法和集成 MACE 信息为加拿大基因组评估中当前使用的方法提供了替代方案。
