A Dirichlet Process Prior for Estimating Lineage-Specific Substitution Rates
A Dirichlet Process Prior for Estimating Lineage-Specific Substitution Rates
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DOI：
10.1093/molbev/msr255
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发表时间：
2012-03-01
影响因子：
10.7
通讯作者：
Huelsenbeck, John P.
中科院分区：
文献类型：
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作者：
Heath, Tracy A.;Holder, Mark T.;Huelsenbeck, John P.
在贝叶斯发散时间估计方法中，我们引入了一个新的模型来放松严格的分子钟作为先验的假设。使用Dirichlet过程先验(DPP)对特定谱系的替换率进行建模，DPP是一种随机过程，它假设系统发育树的谱系分布到不同的率类中。在Dirichlet过程下，利率类别的数量、对利率类别的分支分配以及与每个类别相关联的利率值被视为随机变量。通过对不同模型下模拟的数据集进行分析，对该模型的性能进行了评估。我们比较了Dirichlet过程模型和两种速率变化的替代模型：严格分子钟模型和独立速率模型。我们的结果表明，DPP下的发散时间估计在不显著降低功率的情况下提供了对节点年龄和分支速率的稳健估计。在生物数据集上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的例子。
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.