Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602.
Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602.
J Dairy Sci. 2024 Nov;107(11):9628-9637. doi: 10.3168/jds.2024-24767. Epub 2024 Jul 14.
Dairy cattle health traits are paramount from a welfare and economic viewpoint, and modern breeding programs therefore prioritize the genetic improvement of these traits. Estimated breeding values for health traits are published as the probability of animals staying healthy. They are obtained using threshold models, which assume that the observed binary phenotype (i.e., healthy or sick) is dictated by an underlying normally distributed liability exceeding or not exceeding a threshold. This methodology requires significant computing time and faces convergence challenges, as it implies a nonlinear system of equations. Linear models have more straightforward computations and provide a robust approximation to threshold models; thus, they could be used to overcome these challenges. However, linear models yield estimated breeding values on the observed scale, requiring an approximation to the liability scale analogous to that from threshold models to later obtain the estimated breeding values on the probability scale. In addition, the robustness of the approximation of linear to threshold models depends on the amount of information and the incidence of the trait, with extreme incidence (i.e., ≤5%) deviating from optimal approximation. Our objective was to test a transformation from the observed to the liability, and then to the probability scale, in the genetic evaluation of health traits with moderate and very low (extreme) incidence. Data comprised displaced abomasum (5.1 million), ketosis (3.6 million), lameness (5 million), and mastitis (6.3 million) records from a Holstein population with a pedigree of 6 million animals, of which 1.7 million were genotyped. Univariate threshold and linear models were performed to predict breeding values. The agreement between estimated breeding values on the probability scale derived from threshold and linear models was assessed using Spearman rank correlations and comparison of estimated breeding values distributions. Correlations were at least 0.95, and estimated breeding value distributions almost entirely overlapped for all the traits but displaced abomasum, the trait with the lowest incidence (2%). Computing time was ∼3 times longer for threshold than for linear models. In this Holstein population, the approximation was suboptimal for a trait with extreme incidence (2%). However, when the incidence was ≥6%, the approximation was robust, and its use is recommended along with linear models for analyzing categorical traits in large populations to ease the computational burden.
从福利和经济角度来看，奶牛的健康特征至关重要，因此现代育种计划优先考虑这些特征的遗传改良。健康特征的估计育种值是以动物保持健康的概率发布的。它们是使用阈值模型获得的，该模型假设观察到的二元表型（即健康或患病）由超过或不超过阈值的正态分布倾向决定。这种方法需要大量的计算时间，并面临着收敛挑战，因为它意味着一个非线性方程组。线性模型的计算更为直接，并为阈值模型提供了稳健的近似；因此，它们可以用来克服这些挑战。然而，线性模型在观察尺度上提供估计的育种值，需要对倾向尺度进行类似于阈值模型的近似，以便以后在概率尺度上获得估计的育种值。此外，线性模型对阈值模型的近似稳健性取决于信息量和性状的发生率，发生率极高（即≤5%）会偏离最佳近似。我们的目标是在健康性状的遗传评估中，对中度和极低（极端）发生率的观测值、倾向值和概率值进行转换。数据来自一个荷斯坦牛群体的瘤胃酸中毒（360 万头）、跛行（500 万头）和乳腺炎（630 万头）记录，该群体的系谱为 600 万头，其中 170 万头进行了基因分型。使用单变量阈值和线性模型来预测育种值。使用 Spearman 秩相关和估计育种值分布的比较来评估从阈值和线性模型导出的概率尺度上的估计育种值之间的一致性。除了发病率最低（2%）的皱胃移位外，所有性状的相关性至少为 0.95，并且估计的育种值分布几乎完全重叠。阈值模型的计算时间比线性模型长约 3 倍。在这个荷斯坦牛群体中，对于发病率极低（2%）的性状，近似值并不理想。然而，当发病率≥6%时，近似值是稳健的，建议在分析大群体中的分类性状时，与线性模型一起使用，以减轻计算负担。
