Animal Breeding and Genomics, Wageningen University and Research, PO Box 338, 6700 AH Wageningen, the Netherlands; Mococha Research Station, National Institute of Forestry, Agriculture and Livestock Research, 97454 Mococha, Yucatan, Mexico.
Animal Breeding and Genomics, Wageningen University and Research, PO Box 338, 6700 AH Wageningen, the Netherlands.
J Dairy Sci. 2017 Nov;100(11):9103-9114. doi: 10.3168/jds.2017-12741. Epub 2017 Aug 31.
Given the interest of including dry matter intake (DMI) in the breeding goal, accurate estimated breeding values (EBV) for DMI are needed, preferably for separate lactations. Due to the limited amount of records available on DMI, 2 main approaches have been suggested to compute those EBV: (1) the inclusion of predictor traits, such as fat- and protein-corrected milk (FPCM) and live weight (LW), and (2) the addition of genomic information of animals using what is called genomic prediction. Recently, several methodologies to estimate EBV utilizing genomic information (EBV) have become available. In this study, a new method known as single-step ridge-regression BLUP (SSRR-BLUP) is suggested. The SSRR-BLUP method does not have an imposed limit on the number of genotyped animals, as the commonly used methods do. The objective of this study was to estimate genetic parameters using a relatively large data set with DMI records, as well as compare the accuracies of the EBV for DMI. These accuracies were obtained using 4 different methods: BLUP (using pedigree for all animals with phenotypes), genomic BLUP (GBLUP; only for genotyped animals), single-step GBLUP (SS-GBLUP), and SSRR-BLUP (for genotyped and nongenotyped animals). Records from different lactations, with or without predictor traits (FPCM and LW), were used in the model. Accuracies of EBV for DMI (defined as the correlation between the EBV and pre-adjusted DMI phenotypes divided by the average accuracy of those phenotypes) ranged between 0.21 and 0.38 across methods and scenarios. Accuracies of EBV for DMI using BLUP were the lowest accuracies obtained across methods. Meanwhile, accuracies of EBV for DMI were similar in SS-GBLUP and SSRR-BLUP, and lower for the GBLUP method. Hence, SSRR-BLUP could be used when the number of genotyped animals is large, avoiding the construction of the inverse genomic relationship matrix. Adding information on DMI from different lactations in the reference population gave higher accuracies in comparison when only lactation 1 was included. Finally, no benefit was obtained by adding information on predictor traits to the reference population when DMI was already included. However, in the absence of DMI records, having records on FPCM and LW from different lactations is a good way to obtain EBV with a relatively good accuracy.
鉴于将干物质采食量 (DMI) 纳入育种目标的重要性，需要准确估计 DMI 的估计育种值 (EBV)，最好是针对单独的泌乳期。由于 DMI 的记录数量有限，已经提出了两种主要的方法来计算这些 EBV：(1) 包含预测性状，如脂肪和蛋白质校正乳 (FPCM) 和活重 (LW)，以及 (2) 使用所谓的基因组预测添加动物的基因组信息。最近，已经有几种利用基因组信息 (EBV) 估计 EBV 的方法。在这项研究中，提出了一种新的方法，称为单步岭回归 BLUP（SSRR-BLUP）。与常用方法不同，SSRR-BLUP 方法对基因型动物的数量没有设定限制。本研究的目的是使用具有 DMI 记录的相对较大数据集估计遗传参数，并比较 DMI 的 EBV 准确性。这些准确性是使用 4 种不同的方法获得的：BLUP（使用表型所有动物的系谱）、基因组 BLUP（GBLUP；仅用于基因型动物）、单步 GBLUP（SS-GBLUP）和 SSRR-BLUP（用于基因型和非基因型动物）。模型中使用了来自不同泌乳期的记录，有或没有预测性状（FPCM 和 LW）。DMI 的 EBV 准确性（定义为 EBV 与预调整的 DMI 表型之间的相关性除以这些表型的平均准确性）在不同方法和方案中在 0.21 到 0.38 之间。使用 BLUP 获得的 DMI 的 EBV 准确性是所有方法中获得的最低准确性。同时，SS-GBLUP 和 SSRR-BLUP 中的 DMI 的 EBV 准确性相似，而 GBLUP 方法的准确性较低。因此，当基因型动物数量较大时，可以使用 SSRR-BLUP，避免构建逆基因组关系矩阵。在参考群体中添加来自不同泌乳期的 DMI 信息，与仅包含第 1 泌乳期的信息相比，可获得更高的准确性。最后，当已经包含 DMI 信息时，向参考群体添加预测性状的信息不会带来好处。但是，在没有 DMI 记录的情况下，从不同泌乳期获得关于 FPCM 和 LW 的记录是一种获得相对准确 EBV 的好方法。
