Division of Livestock Sciences, Department of Sustainable Agricultural Systems, University of Natural Resources and Applied Life Sciences, Gregor-Mendel-Str. 33, 1180 Vienna, Austria.
J Dairy Sci. 2010 Sep;93(9):4351-8. doi: 10.3168/jds.2009-2955.
The performance of different models for genetic analyses of clinical mastitis in Austrian Fleckvieh dual-purpose cows was evaluated. The main objective was to compare threshold sire models (probit and logit) with linear sire and linear animal models using REML algorithm. For comparison, data were also analyzed using a Bayesian threshold sire model. The models were evaluated with respect to ranking of sires and their predictive ability in cross-validation. Only minor differences were observed in estimated variance components and heritability from Bayesian and REML probit models. Heritabilities for probit and logit models were 0.06 and 0.08, respectively, whereas heritabilities for linear sire and linear animal models were lower (0.02). Correlations among ranking of sires from threshold and linear sire models were high (>0.99), whereas correlations between any sire model (threshold or linear) and the linear animal model were slightly lower (0.96). The worst sires were ranked very similar across all models, whereas for the best sires some reranking occurred. Further, models were evaluated based on their ability to predict future data, which is one of the main concerns of animal breeders. The predictive ability of each model was determined by using 2 criteria: mean squared error and Pearson correlation between predicted and observed value. Overall, the 5 models did not differ in predictive ability. In contrast to expectations, sire models had the same predictive ability as animal models. Linear models were found to be robust toward departures from normality and performed equally well as threshold models.
评估了不同模型在奥地利弗莱维赫奶牛临床乳腺炎遗传分析中的表现。主要目的是比较阈值 sire 模型（概率和对数）与使用 REML 算法的线性 sire 和线性动物模型。为了进行比较，还使用贝叶斯阈值 sire 模型分析了数据。根据 sire 的排名和交叉验证中的预测能力对模型进行了评估。从贝叶斯和 REML 概率模型中观察到估计的方差分量和遗传力差异较小。概率和对数模型的遗传力分别为 0.06 和 0.08，而线性 sire 和线性动物模型的遗传力较低（0.02）。阈值和线性 sire 模型中 sire 排名之间的相关性很高（>0.99），而任何 sire 模型（阈值或线性）与线性动物模型之间的相关性略低（0.96）。所有模型都将最差 sire 排名非常相似，而对于最佳 sire，会有一些重新排名。此外，还根据模型预测未来数据的能力进行了评估，这是动物饲养者的主要关注点之一。通过使用 2 个标准来确定每个模型的预测能力：预测值和观测值之间的均方误差和 Pearson 相关系数。总体而言，这 5 种模型在预测能力方面没有差异。与预期相反， sire 模型的预测能力与动物模型相同。线性模型对偏离正态性具有鲁棒性，并且与阈值模型一样表现良好。
