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贝叶斯分析通过可逆跳跃马尔可夫链蒙特卡罗分析离散性状的相关性进化。

Bayesian analysis of correlated evolution of discrete characters by reversible-jump Markov chain Monte Carlo.

机构信息

School of Biological Sciences, University of Reading, Reading RG6 6AJ, United Kingdom.

出版信息

Am Nat. 2006 Jun;167(6):808-25. doi: 10.1086/503444. Epub 2006 May 9.

Abstract

We describe a Bayesian method for investigating correlated evolution of discrete binary traits on phylogenetic trees. The method fits a continuous-time Markov model to a pair of traits, seeking the best fitting models that describe their joint evolution on a phylogeny. We employ the methodology of reversible-jump (RJ) Markov chain Monte Carlo to search among the large number of possible models, some of which conform to independent evolution of the two traits, others to correlated evolution. The RJ Markov chain visits these models in proportion to their posterior probabilities, thereby directly estimating the support for the hypothesis of correlated evolution. In addition, the RJ Markov chain simultaneously estimates the posterior distributions of the rate parameters of the model of trait evolution. These posterior distributions can be used to test among alternative evolutionary scenarios to explain the observed data. All results are integrated over a sample of phylogenetic trees to account for phylogenetic uncertainty. We implement the method in a program called RJ Discrete and illustrate it by analyzing the question of whether mating system and advertisement of estrus by females have coevolved in the Old World monkeys and great apes.

摘要

我们描述了一种贝叶斯方法,用于研究系统发育树上离散二元特征的相关进化。该方法为一对特征拟合连续时间马尔可夫模型,寻找最适合描述它们在系统发育上共同进化的模型。我们采用可逆跳跃(RJ)马尔可夫链蒙特卡罗方法在大量可能的模型中进行搜索,其中一些符合两个特征的独立进化,而另一些则符合相关进化。RJ 马尔可夫链按其后验概率访问这些模型,从而直接估计相关进化假设的支持程度。此外,RJ 马尔可夫链还同时估计特征进化模型的速率参数的后验分布。这些后验分布可用于检验替代的进化场景,以解释观察到的数据。所有结果都在系统发育树的样本上进行积分,以解释系统发育不确定性。我们在名为 RJ Discrete 的程序中实现了该方法,并通过分析雌性发情期的交配系统和广告是否在旧世界猴和大猿中共同进化的问题来说明该方法。

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