Damgaard L H
Department of Genetics and Biotechnology, Danish Institute of Agricultural Sciences, PO Box 50, 8830 Tjele, Denmark.
J Anim Sci. 2007 Jun;85(6):1363-8. doi: 10.2527/jas.2006-543. Epub 2007 Jan 3.
This paper deals with Bayesian inferences of animal models using Gibbs sampling. First, we suggest a general and efficient method for updating additive genetic effects, in which the computational cost is independent of the pedigree depth and increases linearly only with the size of the pedigree. Second, we show how this approach can be used to draw inferences from a wide range of animal models using the computer package Winbugs. Finally, we illustrate the approach in a simulation study, in which the data are generated and analyzed using Winbugs according to a linear model with i.i.d errors having Student's t distributions. In conclusion, Winbugs can be used to make inferences in small-sized, quantitative, genetic data sets applying a wide range of animal models that are not yet standard in the animal breeding literature.
本文探讨了使用吉布斯采样对动物模型进行贝叶斯推断。首先,我们提出了一种更新加性遗传效应的通用且高效的方法,其中计算成本与系谱深度无关,仅随系谱规模线性增加。其次,我们展示了如何使用计算机软件包Winbugs,通过这种方法从各种动物模型中进行推断。最后,我们在一个模拟研究中阐述了该方法,在该研究中,根据具有学生t分布的独立同分布误差的线性模型,使用Winbugs生成并分析数据。总之,Winbugs可用于对小型定量遗传数据集进行推断,应用一系列在动物育种文献中尚未成为标准的动物模型。