Department of Animal Sciences, University of Wisconsin, Madison 53706, USA.
J Anim Sci. 2009 Dec;87(12):3854-64. doi: 10.2527/jas.2008-1515. Epub 2009 Aug 14.
A set of analyses using a multiple-trait model (model 1) and dynamic models for the evaluation of beef cattle growth is presented. All models contained additive direct and maternal environmental effects, as well as contemporary groups as nuisance parameters. The predictive ability of models at different parts of the growth trajectory was compared. Body weight records of 6,856 Nelore animals taken at 6 different ages (birth to 540 d) were used. Different models embedding a Kalman filter (KF) into a mixed model representation were fitted. Model 2 assumed that additive, maternal, and residual effects changed over time according to a linear autoregressive process. Model 3 was similar to model 2, but all regression coefficients were set to 1. In model 4, KF was applied only to direct genetic and maternal environmental effects. A leave-one-out cross-validation check was used to assess the predictive ability of models. Estimates of additive variance were similar in the analysis with models 1, 3, and 4 for all ages. Posterior means of maternal components increased slightly after birth and decreased after 135 d of age. Posterior means of additive rates of change were close to 1 at almost all time points, irrespective of the model. The posterior means of residual rates of change, which varied from 0.096 to 0.529, did not support the restrictions that regression coefficients were equal to 1 imposed by model 3. Estimates of additive and maternal correlations obtained with dynamic models were larger than those from a multivariate model. Model 3 produced different phenotypic correlations. Models 2 and 4 had better predictive ability than the multivariate specification. Model 3 predicted the data very poorly, and errors increased markedly with age. The KF can be a useful tool for structuring (co)variance matrices without reducing dimensionality. This model provided accurate predictions and plausible estimates of (co)variance components. Moreover, KF is a flexible specification, because a multivariate structure can be used for some random effects, whereas a dynamic feature can be incorporated for others.
本文提出了一套使用多性状模型（模型 1）和动态模型来评估肉牛生长的分析方法。所有模型都包含加性直接和母体环境效应，以及同期组作为干扰参数。比较了不同生长轨迹部分模型的预测能力。使用了 6856 头尼洛拉牛在 6 个不同年龄（出生到 540 天）的体重记录。不同的模型将卡尔曼滤波器（KF）嵌入混合模型表示中进行拟合。模型 2 假设加性、母体和残差效应随时间呈线性自回归过程变化。模型 3 类似于模型 2，但所有回归系数均设为 1。在模型 4 中，KF 仅应用于直接遗传和母体环境效应。使用留一交叉验证检查来评估模型的预测能力。在所有年龄的分析中，模型 1、3 和 4 的加性方差估计值相似。母体成分的后验均值在出生后略有增加，在 135 日龄后下降。加性变化率的后验均值在几乎所有时间点都接近 1，与模型无关。残差变化率的后验均值从 0.096 到 0.529 不等，这并不支持模型 3 中回归系数等于 1 的限制。动态模型得到的加性和母体相关系数估计值大于多元模型的估计值。模型 3 产生了不同的表型相关系数。模型 2 和 4 比多元规范具有更好的预测能力。模型 3 对数据的预测效果很差，误差随年龄显著增加。KF 可以是一种有用的工具，用于构建（协）方差矩阵而不降低维数。该模型提供了准确的预测和合理的（协）方差分量估计。此外，KF 是一种灵活的规范，因为多元结构可用于某些随机效应，而动态特征可用于其他随机效应。
