Department of Animal Sciences, University of Wisconsin, Madison 53706, USA.
J Anim Sci. 2009 Dec;87(12):3845-53. doi: 10.2527/jas.2008-1514. Epub 2009 Aug 14.
A Bayesian model for quantitative genetic analysis of longitudinal traits is presented. It connects the model known as the Kalman filter (KF) with the standard mixed model of quantitative genetics. The KF model can be implemented easily in a Bayesian framework because, under standard prior assumptions, all fully conditional posterior distributions have closed forms. An analysis of beef cattle growth data including comparisons with a standard multivariate model was performed to assess applicability of the KF to animal breeding. Models were compared using the deviance information criterion and the Bayes factor. Models in which a KF acted on additive genetic and maternal effects were favored by the deviance information criterion, although KF did not describe residual (co)variance adequately. The Bayes factor did not provide conclusive evidence in favor of a specific model. Fitting KF to longitudinal traits provides estimates of genetic value for a whole range of time points, assuming that there are genetic differences through time between and within individuals. Different models embedding the KF in a mixed model were demonstrated to provide a more parsimonious (co)variance structure than a standard multitrait specification for the quantitative genetic analysis of longitudinal data.
提出了一种用于纵向性状数量遗传分析的贝叶斯模型。它将称为卡尔曼滤波器（KF）的模型与数量遗传学的标准混合模型联系起来。由于在标准先验假设下，所有完全条件后验分布都具有封闭形式，因此 KF 模型可以很容易地在贝叶斯框架中实现。对包括与标准多变量模型比较在内的肉牛生长数据进行了分析，以评估 KF 在动物育种中的适用性。使用偏差信息准则和贝叶斯因子对模型进行了比较。在偏差信息准则下，KF 作用于加性遗传和母体效应的模型受到青睐，尽管 KF 不能充分描述残差（协）方差。贝叶斯因子并没有提供支持特定模型的确凿证据。假设个体之间和个体内部随时间存在遗传差异，KF 适用于纵向性状，可以为整个时间点提供遗传值估计。将 KF 嵌入混合模型中的不同模型被证明比标准多性状规范为纵向数据的数量遗传分析提供了更简约的（协）方差结构。
