A general comparison of relaxed molecular clock models
A general comparison of relaxed molecular clock models
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DOI：
10.1093/molbev/msm193
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发表时间：
2007-12-01
影响因子：
10.7
通讯作者：
Lartillot, Nicolas
中科院分区：
文献类型：
--
作者：
Lepage, Thomas;Bryant, David;Lartillot, Nicolas
已经提出了几种模型来放松分子钟，以估计发散时间。然而，目前还不清楚哪种模型最适合真实的数据，因此应该用于进行分子测年。特别是，我们不知道是否应该考虑利率自相关或之前的分歧时间应使用。在这项工作中，我们提出了替代放松时钟模型的一般基准。我们已经重新实现了大多数已经存在的模型，包括流行的对数正态模型，以及各种先验选择的分歧时间（出生死亡，狄利克雷，均匀），在一个共同的贝叶斯统计框架。我们还提出了一个新的自相关模型，称为“CIR”过程，具有明确的平稳特性。我们评估这些模型和先验的相对适应性，当应用于3个不同的蛋白质数据集，从真核生物，脊椎动物和哺乳动物，计算贝叶斯因子使用一种称为热力学积分的数值方法。我们发现，2个自相关模型，CIR和对数正态分布，具有类似的拟合，并在所有3个数据集上明显优于不相关模型。相反，发散时间先验的最佳选择更多地依赖于所研究的数据。总之，我们的研究结果提供了有用的指导方针，在分子测年领域的模型选择，同时开辟了更广泛的模型比较。
Several models have been proposed to relax the molecular clock in order to estimate divergence times. However, it is unclear which model has the best fit to real data and should therefore be used to perform molecular dating. In particular, we do not know whether rate autocorrelation should be considered or which prior on divergence times should be used. In this work, we propose a general bench mark of alternative relaxed clock models. We have reimplemented most of the already existing models, including the popular lognormal model, as well as various prior choices for divergence times (birth-death, Dirichlet, uniform), in a common Bayesian statistical framework. We also propose a new autocorrelated model, called the "CIR" process, with well-defined stationary properties. We assess the relative fitness of these models and priors, when applied to 3 different protein data sets from eukaryotes, vertebrates, and mammals, by computing Bayes factors using a numerical method called thermodynamic integration. We find that the 2 autocorrelated models, CIR and lognormal, have a similar fit and clearly outperform uncorrelated models on all 3 data sets. In contrast, the optimal choice for the divergence time prior is more dependent on the data investigated. Altogether, our results provide useful guidelines for model choice in the field of molecular dating while opening the way to more extensive model comparisons.