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.

中文翻译：

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使用随机回归测试日模型研究高山山羊和萨能山羊品种首次泌乳乳品性状的遗传参数

随机回归模型（RRM）广泛用于分析遗传评估系统中的纵向数据，因为它们可以通过拟合测试日（TD）特定效应来更好地解释环境效应和动物的附加遗传值的时间过程变化。我们的目标是实施随机回归模型来评估法国山羊的乳制品生产性状。这些数据包括 30,186 只和 32,256 只萨宁山羊和阿尔卑斯山羊第一次泌乳期的牛奶 TD 记录。考虑了产奶量、脂肪产量、蛋白质产量、脂肪含量和蛋白质含量。使用样条曲线对环境因素进行建模。遗传和永久环境影响由相同的勒让德多项式建模。测试了从 0 到 4 阶多项式函数导出的拟合优度和遗传参数。还将结果与计算 250 天总产奶量的哺乳模型的结果以及将整个哺乳期六个时期的表现视为不同性状的多性状模型的结果进行了比较。模型之间的遗传参数是一致的。具有四阶勒让德多项式的模型可以实现数据的最佳拟合。为了减少复杂性、计算时间和解释，使用特征值分解对方差协方差矩阵进行降阶。随着等级降低到 2，前两个主成分正确概括了产奶量水平和持久性的遗传变异，它们之间的相关性接近于 0。法国采用了随机回归模型来评估和选择山羊的产量性状和持久性，这些性状在第一次泌乳期是独立的，即它们之间没有遗传相关性。