Department of Ecology and Evolutionary Biology, University of Kansas, KS, USA.
Mol Biol Evol. 2012 Mar;29(3):939-55. doi: 10.1093/molbev/msr255. Epub 2011 Nov 2.
We introduce a new model for relaxing the assumption of a strict molecular clock for use as a prior in Bayesian methods for divergence time estimation. Lineage-specific rates of substitution are modeled using a Dirichlet process prior (DPP), a type of stochastic process that assumes lineages of a phylogenetic tree are distributed into distinct rate classes. Under the Dirichlet process, the number of rate classes, assignment of branches to rate classes, and the rate value associated with each class are treated as random variables. The performance of this model was evaluated by conducting analyses on data sets simulated under a range of different models. We compared the Dirichlet process model with two alternative models for rate variation: the strict molecular clock and the independent rates model. Our results show that divergence time estimation under the DPP provides robust estimates of node ages and branch rates without significantly reducing power. Further analyses were conducted on a biological data set, and we provide examples of ways to summarize Markov chain Monte Carlo samples under this model.
我们引入了一种新的模型，用于放宽严格分子钟假设，作为贝叶斯分歧时间估计方法的先验。使用狄利克雷过程先验（DPP）对谱系特异性替代率进行建模，狄利克雷过程是一种随机过程，假设系统发育树的谱系分布在不同的速率类别中。在狄利克雷过程下，速率类别的数量、分支到速率类别的分配以及与每个类别的速率值都被视为随机变量。通过对在一系列不同模型下模拟的数据进行分析，评估了该模型的性能。我们将狄利克雷过程模型与两种替代的速率变化模型（严格分子钟和独立速率模型）进行了比较。我们的结果表明，DPP 下的分歧时间估计在不显著降低功效的情况下，为节点年龄和分支速率提供了稳健的估计。我们还对一个生物学数据集进行了进一步的分析，并提供了在该模型下总结马尔可夫链蒙特卡罗样本的方法示例。
