Waagepetersen Rasmus, Ibánez-Escriche Noelia, Sorensen Daniel
Department of Mathematical Sciences, Aalborg University, 9220 Aalborg, Denmark.
Genet Sel Evol. 2008 Mar-Apr;40(2):161-76. doi: 10.1186/1297-9686-40-2-161. Epub 2008 Feb 27.
In quantitative genetics, Markov chain Monte Carlo (MCMC) methods are indispensable for statistical inference in non-standard models like generalized linear models with genetic random effects or models with genetically structured variance heterogeneity. A particular challenge for MCMC applications in quantitative genetics is to obtain efficient updates of the high-dimensional vectors of genetic random effects and the associated covariance parameters. We discuss various strategies to approach this problem including reparameterization, Langevin-Hastings updates, and updates based on normal approximations. The methods are compared in applications to Bayesian inference for three data sets using a model with genetically structured variance heterogeneity.
在数量遗传学中，马尔可夫链蒙特卡罗（MCMC）方法对于非标准模型（如具有遗传随机效应的广义线性模型或具有遗传结构方差异质性的模型）中的统计推断而言不可或缺。MCMC在数量遗传学中的应用面临的一个特殊挑战是如何高效更新遗传随机效应的高维向量以及相关的协方差参数。我们讨论了处理此问题的各种策略，包括重新参数化、朗之万 - 黑斯廷斯更新以及基于正态近似的更新。使用具有遗传结构方差异质性的模型，将这些方法应用于三个数据集的贝叶斯推断，并进行了比较。
