GenPhySE, Université de Toulouse, INRA, ENVT, Toulouse INP, 31326, Castanet-Tolosan, France.
Institut de l'Elevage, Chemin de Borde Rouge, 31326, Castanet-Tolosan Cedex, France.
Genet Sel Evol. 2019 Aug 13;51(1):43. doi: 10.1186/s12711-019-0485-3.
Random regression models (RRM) are widely used to analyze longitudinal data in genetic evaluation systems because they can better account for time-course changes in environmental effects and additive genetic values of animals by fitting the test-day (TD) specific effects. Our objective was to implement a random regression model for the evaluation of dairy production traits in French goats.
The data consisted of milk TD records from 30,186 and 32,256 first lactations of Saanen and Alpine goats. Milk yield, fat yield, protein yield, fat content and protein content were considered. Splines were used to model the environmental factors. The genetic and permanent environmental effects were modeled by the same Legendre polynomials. The goodness-of-fit and the genetic parameters derived from functions of the polynomials of orders 0 to 4 were tested. Results were also compared to those from a lactation model with total milk yield calculated over 250 days and to those of a multiple-trait model that considers performance in six periods throughout lactation as different traits. Genetic parameters were consistent between models. Models with fourth-order Legendre polynomials led to the best fit of the data. In order to reduce complexity, computing time, and interpretation, a rank reduction of the variance covariance matrix was performed using eigenvalue decomposition. With a reduction to rank 2, the first two principal components correctly summarized the genetic variability of milk yield level and persistency, with a correlation close to 0 between them.
A random regression model was implemented in France to evaluate and select goats for yield traits and persistency, which are independent i.e. no genetic correlation between them, in first lactation.
随机回归模型（RRM）广泛应用于遗传评估系统中的纵向数据分析，因为它们可以通过拟合特定测试日（TD）的效应，更好地考虑环境效应和动物加性遗传值随时间的变化。我们的目的是为法国山羊的产奶性状评估实施随机回归模型。
数据包括 30186 只和 32256 只首次泌乳的萨能山羊和阿尔卑斯山羊的牛奶 TD 记录。考虑了牛奶产量、脂肪产量、蛋白质产量、脂肪含量和蛋白质含量。样条用于模拟环境因素。遗传和永久环境效应通过相同的勒让德多项式进行建模。通过多项式阶数为 0 到 4 的函数对适合度和遗传参数进行了测试。结果还与基于 250 天总产奶量计算的泌乳模型和考虑泌乳期六个时期表现的多性状模型进行了比较。遗传参数在模型之间是一致的。具有四阶勒让德多项式的模型导致数据的最佳拟合。为了降低复杂性、计算时间和解释，使用特征值分解对方差协方差矩阵进行了降秩。通过降秩到 2，前两个主成分正确地总结了产奶量水平和持久性的遗传变异性，它们之间的相关性接近 0。
在法国实施了随机回归模型，以评估和选择具有独立性（即无遗传相关性）的第一泌乳期产奶量性状和持久性的山羊。
