Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602, USA.
Angus Genetics Inc., St. Joseph, MO 64506, USA.
J Anim Sci. 2022 Mar 1;100(3). doi: 10.1093/jas/skac057.
A spurious negative genetic correlation between direct and maternal effects of weaning weight (WW) in beef cattle has historically been problematic for researchers and industry. Previous research has suggested the covariance between sires and herds may be contributing to this relationship. The objective of this study was to estimate the variance components (VC) for WW in American Angus with and without sire by herd (S×H) interaction effect when genomic information is used or not. Five subsets of ~100k animals for each subset were used. When genomic information was included, genotypes were added for 15,637 animals. Five replicates were performed. Four different models were tested, namely, M1: without S×H interaction effect and with covariance between direct and maternal effect (σam) ≠ 0; M2: with S×H interaction effect and σam ≠ 0; M3: without S×H interaction effect and with σam = 0; M4: with S×H interaction effect and σam = 0. VC were estimated using the restricted maximum likelihood (REML) and single-step genomic REML (ssGREML) with the average information algorithm. Breeding values were computed using single-step genomic BLUP for the models above and one additional model, which had the covariance zeroed after the estimation of VC (M5). The ability of each model to predict future breeding values was investigated with the linear regression method. Under REML, when the S×H interaction effect was added to the model, both direct and maternal genetic variances were greatly reduced, and the negative covariance became positive (i.e., when moving from M1 to M2). Similar patterns were observed under ssGREML, but with less reduction in the direct and maternal genetic variances and still a negative covariance. Models with the S×H interaction effect (M2 and M4) had a better fit according to the Akaike information criteria. Breeding values from those models were more accurate and had less bias than the other three models. The rankings and breeding values of artificial insemination sires (N = 1,977) greatly changed when the S×H interaction effect was fit in the model. Although the S×H interaction effect accounted for 3% to 5% of the total phenotypic variance and improved the model fit, this change in the evaluation model will cause severe reranking among animals.
断奶体重（WW）的直接效应和母体效应之间的虚假负遗传相关在历史上一直是研究人员和行业的问题。先前的研究表明，父本和畜群之间的协方差可能是导致这种关系的原因。本研究的目的是估计美国安格斯牛 WW 的方差分量（VC），同时考虑是否包含基因组信息以及是否存在 sire by herd（S×H）互作效应。每个子集使用了大约 10 万只动物的 5 个子集。当包含基因组信息时，为 15637 只动物添加了基因型。进行了 5 次重复。测试了 4 种不同的模型，即 M1：没有 S×H 互作效应，且直接效应和母体效应之间的协方差（σam）≠0；M2：有 S×H 互作效应，且 σam≠0；M3：没有 S×H 互作效应，且 σam=0；M4：有 S×H 互作效应，且 σam=0。使用约束最大似然（REML）和平均信息算法的单步基因组 REML（ssGREML）估计 VC。使用单步基因组 BLUP 为上述模型和一个额外的模型（在估计 VC 后将协方差归零（M5））计算了育种值。使用线性回归方法研究了每个模型预测未来育种值的能力。在 REML 下，当添加 S×H 互作效应时，直接和母体遗传方差大大降低，负协方差变为正（即，从 M1 到 M2）。ssGREML 下观察到类似的模式，但直接和母体遗传方差的降低幅度较小，且仍为负协方差。根据赤池信息量准则，包含 S×H 互作效应的模型（M2 和 M4）拟合效果更好。与其他三个模型相比，这些模型的育种值更准确，偏差更小。当在模型中拟合 S×H 互作效应时，人工授精种公牛（N=1977）的排名和育种值发生了很大变化。尽管 S×H 互作效应占总表型方差的 3%至 5%，并提高了模型拟合度，但这种评估模型的变化会导致动物之间的严重重新排名。
