Improved Reversible Jump Algorithms for Bayesian Species Delimitation
Improved Reversible Jump Algorithms for Bayesian Species Delimitation
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DOI：
10.1534/genetics.112.149039
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发表时间：
2013-05-01
期刊：
影响因子：
3.3
通讯作者：
Yang, Ziheng
中科院分区：
文献类型：
--
作者：
Rannala, Bruce;Yang, Ziheng
最近已经提出了几种计算方法来界定物种使用多位点序列数据。其中，Yang和Rannala的贝叶斯方法使用似然框架中的多物种合并模型来计算不同物种划界模型的后验概率。它有一个良好的统计基础，并被发现有很好的统计特性，在模拟研究，如低错误率的欠分裂和过分裂。然而，该方法遭受的可逆跳马尔可夫链蒙特卡罗（rjMCMC）算法的混合不良。在这里，我们描述了几个修改的算法。我们提出了一个灵活的先验，允许用户指定的概率，每个节点上的指导树代表一个真正的物种形成事件。我们还介绍了修改的rjMCMC算法，删除新物种的分歧时间的限制时，分裂和改变基因树，以消除不兼容性。新的算法被发现，以改善混合的马尔可夫链的模拟和经验数据集。
Several computational methods have recently been proposed for delimiting species using multilocus sequence data. Among them, the Bayesian method of Yang and Rannala uses the multispecies coalescent model in the likelihood framework to calculate the posterior probabilities for the different species-delimitation models. It has a sound statistical basis and is found to have nice statistical properties in simulation studies, such as low error rates of undersplitting and oversplitting. However, the method suffers from poor mixing of the reversible-jump Markov chain Monte Carlo (rjMCMC) algorithms. Here, we describe several modifications to the algorithms. We propose a flexible prior that allows the user to specify the probability that each node on the guide tree represents a true speciation event. We also introduce modifications to the rjMCMC algorithms that remove the constraint on the new species divergence time when splitting and alter the gene trees to remove incompatibilities. The new algorithms are found to improve mixing of the Markov chain for both simulated and empirical data sets.