Factor analysis models for structuring covariance matrices of additive genetic effects: a Bayesian implementation
Factor analysis models for structuring covariance matrices of additive genetic effects: a Bayesian implementation
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DOI：
10.1051/gse:20070016
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发表时间：
2007-09-01
影响因子：
4.1
通讯作者：
Gianola, Daniel
中科院分区：
文献类型：
--
作者：
de Los Campos, Gustavo;Gianola, Daniel
多元线性模型在数量遗传学中变得越来越重要。在高维规范中，因子分析 (FA) 可以提供构建 (co) 方差矩阵的途径，从而减少描述 (co) 色散所需的参数数量。我们描述了如何使用 FA 在多元线性混合模型的背景下对遗传效应进行建模。使用正交公因子结构在高斯假设下对遗传效应进行建模，使得边际似然是具有结构化遗传（co）方差矩阵的多元正态分布。在标准先验假设下，所有完全条件分布都具有闭合形式，并且联合后验分布的样本可以通过吉布斯采样获得。该模型和为其贝叶斯实施开发的算法用于描述奶牛产奶量的五次重复记录，并将一个常见的 FA 模型与标准多性状模型进行了比较。贝叶斯信息准则支持 FA 模型。
Multivariate linear models are increasingly important in quantitative genetics. In high dimensional specifications, factor analysis (FA) may provide an avenue for structuring (co) variance matrices, thus reducing the number of parameters needed for describing (co) dispersion. We describe how FA can be used to model genetic effects in the context of a multivariate linear mixed model. An orthogonal common factor structure is used to model genetic effects under Gaussian assumption, so that the marginal likelihood is multivariate normal with a structured genetic (co) variance matrix. Under standard prior assumptions, all fully conditional distributions have closed form, and samples from the joint posterior distribution can be obtained via Gibbs sampling. The model and the algorithm developed for its Bayesian implementation were used to describe five repeated records of milk yield in dairy cattle, and a one common FA model was compared with a standard multiple trait model. The Bayesian Information Criterion favored the FA model.