Dairy cattle production systems are mostly based on purebreds, but recently the use of crossbreeding has received increased interest. For genetic evaluations including crossbreds, several methods based on single-step genomic best linear unbiased prediction (ssGBLUP) have been proposed, including metafounder ssGBLUP (MF-ssGBLUP) and breed-specific ssGBLUP (BS-ssGBLUP). Ideally, models that account for breed effects should perform better than simple models, but knowledge on the performance of these methods is lacking for two-way crossbred cattle. In addition, the differences in the estimates of genetic parameters (such as the genetic variance component and heritability) between these methods have rarely been investigated. Therefore, the aims of this study were to (1) compare the estimates of genetic parameters for average daily gain (ADG) and feed conversion ratio (FCR) between these methods; and (2) evaluate the impact of these methods on the predictive ability for crossbred performance. Bivariate models using standard ssGBLUP, MF-ssGBLUP and BS-ssGBLUP for the genetic evaluation of ADG and FCR were investigated. To measure the predictive ability of these three methods, we estimated four estimators, bias, dispersion, population accuracy and ratio of population accuracies, using the linear regression (LR) method. The results show that, for both ADG and FCR, the heritabilities were low with the three methods. For FCR, the differences in the estimated genetic parameters were small between the three methods, while for ADG, those estimated with BS-ssGBLUP deviated largely from those estimated with the other two methods. Bias and dispersion were similar across the three methods. Population accuracies for both ADG and FCR were always higher with MF-ssGBLUP than with ssGBLUP, while with BS-ssGBLUP the population accuracy was highest for FCR and lowest for ADG. Our results indicate that in the genetic evaluation for crossbred performance in a two-way crossbred cattle production system, the predictive ability of MF-ssGBLUP and BS-ssGBLUP is greater than that of ssGBLUP, when the estimated variance components are consistent across the three methods. Compared with BS-ssGBLUP, MF-ssGBLUP is more robust in its superiority over ssGBLUP.

中文翻译：

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牛双向杂交性能的基因组评估

奶牛生产系统主要基于纯种，但最近对杂交育种的使用越来越感兴趣。对于包括杂交品种在内的遗传评估，已经提出了几种基于单步基因组最佳线性无偏预测 (ssGBLUP) 的方法，包括 metafounder ssGBLUP (MF-ssGBLUP) 和品种特异性 ssGBLUP (BS-ssGBLUP)。理想情况下，考虑品种效应的模型应该比简单模型表现更好，但对于双向杂交牛，缺乏关于这些方法性能的知识。此外，很少研究这些方法之间遗传参数（如遗传方差分量和遗传力）估计值的差异。所以，本研究的目的是 (1) 比较这些方法对平均日增重 (ADG) 和饲料转化率 (FCR) 的遗传参数估计值；(2) 评估这些方法对杂交性能预测能力的影响。研究了使用标准 ssGBLUP、MF-ssGBLUP 和 BS-ssGBLUP 对 ADG 和 FCR 进行遗传评估的双变量模型。为了衡量这三种方法的预测能力，我们使用线性回归 (LR) 方法估计了四个估计量、偏差、离差、总体准确度和总体准确度比率。结果表明，对于 ADG 和 FCR，这三种方法的遗传力都很低。对于 FCR，三种方法估计的遗传参数差异很小，而对于 ADG，使用 BS-ssGBLUP 估计的那些与使用其他两种方法估计的那些有很大偏差。三种方法的偏差和离散度相似。MF-ssGBLUP 的 ADG 和 FCR 种群精度始终高于 ssGBLUP，而对于 BS-ssGBLUP，FCR 的种群精度最高，ADG 的种群精度最低。我们的结果表明，在双向杂交牛生产系统的杂交性能遗传评估中，当估计的方差分量在三种方法中一致时，MF-ssGBLUP 和 BS-ssGBLUP 的预测能力大于 ssGBLUP . 与 BS-ssGBLUP 相比，MF-ssGBLUP 在其优于 ssGBLUP 方面更具鲁棒性。MF-ssGBLUP 的 ADG 和 FCR 种群精度始终高于 ssGBLUP，而对于 BS-ssGBLUP，FCR 的种群精度最高，ADG 的种群精度最低。我们的结果表明，在双向杂交牛生产系统的杂交性能遗传评估中，当估计的方差分量在三种方法中一致时，MF-ssGBLUP 和 BS-ssGBLUP 的预测能力大于 ssGBLUP . 与 BS-ssGBLUP 相比，MF-ssGBLUP 在其优于 ssGBLUP 方面更具鲁棒性。MF-ssGBLUP 的 ADG 和 FCR 种群精度始终高于 ssGBLUP，而对于 BS-ssGBLUP，FCR 的种群精度最高，ADG 的种群精度最低。我们的结果表明，在双向杂交牛生产系统的杂交性能遗传评估中，当估计的方差分量在三种方法中一致时，MF-ssGBLUP 和 BS-ssGBLUP 的预测能力大于 ssGBLUP . 与 BS-ssGBLUP 相比，MF-ssGBLUP 在其优于 ssGBLUP 方面更具鲁棒性。当估计的方差分量在三种方法中一致时，MF-ssGBLUP 和 BS-ssGBLUP 的预测能力大于 ssGBLUP。与 BS-ssGBLUP 相比，MF-ssGBLUP 在其优于 ssGBLUP 方面更具鲁棒性。当估计的方差分量在三种方法中一致时，MF-ssGBLUP 和 BS-ssGBLUP 的预测能力大于 ssGBLUP。与 BS-ssGBLUP 相比，MF-ssGBLUP 在其优于 ssGBLUP 方面更具鲁棒性。