Bayesian Inference of Genetic Parameters Based on Conditional Decompositions of Multivariate Normal Distributions
Bayesian Inference of Genetic Parameters Based on Conditional Decompositions of Multivariate Normal Distributions
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DOI：
10.1534/genetics.110.114249
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发表时间：
2010-06-01
期刊：
影响因子：
3.3
通讯作者：
Sillanpaa, Mikko J.
中科院分区：
文献类型：
--
作者：
Hallander, Jon;Waldmann, Patrik;Sillanpaa, Mikko J.
混合线性模型是复杂家系分析中参数估计的重要工具，复杂家系包括家系和基因组信息，且相互依赖的遗传因素通常假设服从高维正态分布。我们已经开发了一个贝叶斯统计方法的基础上分解的多元正态先验分布的产品的条件单变量分布。该程序允许在用户友好的计算机软件包WinBUGS内对复杂谱系进行计算要求高的遗传评估。为了证明和评估的灵活性的方法，我们分析了两个例子家系：一个大的非近交系苏格兰松（樟子松L。）这包括加性和显性多基因关系和一个模拟的系谱，其中基因组关系已经计算的基础上，一个密集的标记地图。分析表明，我们的方法是快速，并提供准确的估计，因此，它应该是一个有用的工具，快速，可靠地估计复杂家系的遗传参数。
It is widely recognized that the mixed linear model is an important tool for parameter estimation in the analysis of complex pedigrees, which includes both pedigree and genomic information, and where mutually dependent genetic factors are often assumed to follow multivariate normal distributions of high dimension. We have developed a Bayesian statistical method based on the decomposition of the multivariate normal prior distribution into products of conditional univariate distributions. This procedure permits computationally demanding genetic evaluations of complex pedigrees, within the user-friendly computer package WinBUGS. To demonstrate and evaluate the flexibility of the method, we analyzed two example pedigrees: a large noninbred pedigree of Scots pine (Pinus sylvestris L.) that includes additive and dominance polygenic relationships and a simulated pedigree where genomic relationships have been calculated on the basis of a dense marker map. The analysis showed that our method was fast and provided accurate estimates and that it should therefore be a helpful tool for estimating genetic parameters of complex pedigrees quickly and reliably.