J Anim Sci. 2017 Nov;95(11):4752-4763. doi: 10.2527/jas2017.1864.
The objective of the present study was to compare a random regression model, usually used in genetic analyses of longitudinal data, with the structured antedependence (SAD) model to study the longitudinal feed conversion ratio (FCR) in growing Large White pigs and to propose criteria for animal selection when used for genetic evaluation. The study was based on data from 11,790 weekly FCR measures collected on 1,186 Large White male growing pigs. Random regression (RR) using orthogonal polynomial Legendre and SAD models was used to estimate genetic parameters and predict FCR-based EBV for each of the 10 wk of the test. The results demonstrated that the best SAD model (1 order of antedependence of degree 2 and a polynomial of degree 2 for the innovation variance for the genetic and permanent environmental effects, i.e., 12 parameters) provided a better fit for the data than RR with a quadratic function for the genetic and permanent environmental effects (13 parameters), with Bayesian information criteria values of -10,060 and -9,838, respectively. Heritabilities with the SAD model were higher than those of RR over the first 7 wk of the test. Genetic correlations between weeks were higher than 0.68 for short intervals between weeks and decreased to 0.08 for the SAD model and -0.39 for RR for the longest intervals. These differences in genetic parameters showed that, contrary to the RR approach, the SAD model does not suffer from border effect problems and can handle genetic correlations that tend to 0. Summarized breeding values were proposed for each approach as linear combinations of the individual weekly EBV weighted by the coefficients of the first or second eigenvector computed from the genetic covariance matrix of the additive genetic effects. These summarized breeding values isolated EBV trajectories over time, capturing either the average general value or the slope of the trajectory. Finally, applying the SAD model over a reduced period of time suggested that similar selection choices would result from the use of the records from the first 8 wk of the test. To conclude, the SAD model performed well for the genetic evaluation of longitudinal phenotypes.
本研究旨在比较随机回归模型（通常用于纵向数据的遗传分析）和结构同期相关（SAD）模型，以研究大白猪生长阶段的纵向饲料转化率（FCR），并提出用于遗传评估时的动物选择标准。该研究基于 11790 个每周 FCR 测量值，这些数据来自 1186 头大白公猪，收集于 10 周的测试中。使用正交多项式勒让德和 SAD 模型进行随机回归（RR），以估计遗传参数并预测每个测试周的 FCR 基础 EBV。结果表明，最佳 SAD 模型（遗传和永久环境效应的一阶同期相关程度 2 和二阶多项式，即 12 个参数）比 RR 更适合数据，RR 则使用二次函数表示遗传和永久环境效应（13 个参数），贝叶斯信息准则值分别为-10060 和-9838。在测试的前 7 周，SAD 模型的遗传力高于 RR。SAD 模型的遗传相关系数在短间隔周之间高于 0.68，而在最长间隔时则降至 0.08，而 RR 则降至-0.39。这些遗传参数的差异表明，与 RR 方法不同，SAD 模型不会受到边界效应问题的影响，并且可以处理趋于 0 的遗传相关系数。对于每个方法，都提出了汇总的育种值，这些值是通过将个体每周 EBV 乘以由加性遗传效应的遗传协方差矩阵计算得出的第一个或第二个特征向量的系数进行加权得到的线性组合。这些汇总的育种值随时间隔离 EBV 轨迹，捕捉到轨迹的平均值或斜率。最后，在较短的时间段内应用 SAD 模型表明，使用测试前 8 周的记录会产生类似的选择结果。总之，SAD 模型在纵向表型的遗传评估中表现良好。
