Inferring speciation times under an episodic molecular clock
Inferring speciation times under an episodic molecular clock
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DOI：
10.1080/10635150701420643
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发表时间：
2007-01-01
影响因子：
6.5
通讯作者：
Yang, Ziheng
中科院分区：
文献类型：
--
作者：
Rannala, Bruce;Yang, Ziheng
我们扩展了我们最近开发的马尔可夫链蒙特卡罗算法贝叶斯估计物种的分歧时间，让不同的进化率之间的血统。该方法可以使用来自多个基因位点的异质数据，并适应多个化石校准。使用灵活的统计分布来描述化石校准中的不确定性。缺乏化石校准的节点的发散时间的先验指定使用的出生-死亡过程与物种采样。特定谱系的替代率的先验指定使用相邻谱系之间的自相关率模型（基于速率漂移的几何布朗运动模型）或由对数正态概率分布指定的谱系之间的独立速率模型。我们开发了一个无限站点理论，它预测，当序列数据的数量接近无穷大，后验可信区间的宽度和后验平均发散时间形成一个完美的线性关系，与斜率表示时间估计的不确定性，不能单独减少序列数据。模拟研究了离散率变化和采样位点数对离散时间估计的不确定性的影响。分析表明，后验时间估计通常涉及相当大的不确定性，即使有无限数量的序列数据，化石校准的可靠性和精度是至关重要的发散时间估计。我们将我们的新算法应用到两个经验数据集，并将结果与以前的贝叶斯和似然分析中获得的结果进行比较。结果表明，我们的新算法的实用性。
We extend our recently developed Markov chain Monte Carlo algorithm for Bayesian estimation of species divergence times to allow variable evolutionary rates among lineages. The method can use heterogeneous data from multiple gene loci and accommodate multiple fossil calibrations. Uncertainties in fossil calibrations are described using flexible statistical distributions. The prior for divergence times for nodes lacking fossil calibrations is specified by use of a birth-death process with species sampling. The prior for lineage-specific substitution rates is specified using either a model with autocorrelated rates among adjacent lineages (based on a geometric Brownian motion model of rate drift) or a model with independent rates among lineages specified by a log-normal probability distribution. We develop an infinite-sites theory, which predicts that when the amount of sequence data approaches infinity, the width of the posterior credibility interval and the posterior mean of divergence times form a perfect linear relationship, with the slope indicating uncertainties in time estimates that cannot be reduced by sequence data alone. Simulations are used to study the influence of among-lineage rate variation and the number of loci sampled on the uncertainty of divergence time estimates. The analysis suggests that posterior time estimates typically involve considerable uncertainties even with an infinite amount of sequence data, and that the reliability and precision of fossil calibrations are critically important to divergence time estimation. We apply our new algorithms to two empirical data sets and compare the results with those obtained in previous Bayesian and likelihood analyses. The results demonstrate the utility of our new algorithms.