Dating Primate Divergences through an Integrated Analysis of Palaeontological and Molecular Data
Dating Primate Divergences through an Integrated Analysis of Palaeontological and Molecular Data
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DOI：
10.1093/sysbio/syq054
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发表时间：
2011-01-01
影响因子：
6.5
通讯作者：
Tavare, Simon
中科院分区：
文献类型：
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作者：
Wilkinson, Richard D.;Steiper, Michael E.;Tavare, Simon
分歧时间的估计通常是使用化石记录或现代物种的序列数据来完成的。我们对古生物学和分子数据进行综合分析，以利用这两种信息来源来估计灵长类动物的分化时间。化石记录中发现的保存灵长类动物物种的数量及其地质年龄分布，与现存灵长类动物物种的数量相结合，以提供灵长类动物和类人猿分化时间的初步估计。这是通过使用随机前向建模方法来完成的，其中物种形成和化石保存和发现是及时模拟的。我们使用化石分析的后验分布作为分子分析中节点年龄的先验分布。来自 15 个灵长类物种的两个基因组区域（人类 7 号染色体上的 CFTR 和 8 号染色体上的 CYP7A1 区域）的序列数据与 PAML 中 mcmctree 中实现的出生-死亡模型一起使用，以推断灵长类树中 14 个节点年龄的后验分布。我们发现这些节点的年龄估计值都比之前报告的除其中一个节点外的所有节点的日期都要早。为了执行推理，引入了一种新的近似贝叶斯计算 (ABC) 算法，其中模型的结构可以在 ABC-within-Gibbs 算法中利用，以提供更有效的分析。
Estimation of divergence times is usually done using either the fossil record or sequence data from modern species. We provide an integrated analysis of palaeontological and molecular data to give estimates of primate divergence times that utilize both sources of information. The number of preserved primate species discovered in the fossil record, along with their geological age distribution, is combined with the number of extant primate species to provide initial estimates of the primate and anthropoid divergence times. This is done by using a stochastic forwards-modeling approach where speciation and fossil preservation and discovery are simulated forward in time. We use the posterior distribution from the fossil analysis as a prior distribution on node ages in a molecular analysis. Sequence data from two genomic regions (CFTR on human chromosome 7 and the CYP7A1 region on chromosome 8) from 15 primate species are used with the birth-death model implemented in mcmctree in PAML to infer the posterior distribution of the ages of 14 nodes in the primate tree. We find that these age estimates are older than previously reported dates for all but one of these nodes. To perform the inference, a new approximate Bayesian computation (ABC) algorithm is introduced, where the structure of the model can be exploited in an ABC-within-Gibbs algorithm to provide a more efficient analysis.