A compound poisson process for relaxing the molecular clock.
A compound poisson process for relaxing the molecular clock.
复制标题
DOI：
--
复制
发表时间：
2000-04
期刊：
影响因子：
3.3
通讯作者：
J. Huelsenbeck;B. Larget;D. Swofford
中科院分区：
文献类型：
--
作者：
J. Huelsenbeck;B. Larget;D. Swofford
分子钟假说仍然是进化生物学中一个重要的概念和分析工具，尽管一再观察到时钟假说不能完美地解释观察到的DNA序列变异。我们引入了一个参数模型，放宽了分子时钟，允许利率根据复合泊松过程的不同谱系。根据泊松过程，将替换率变化的事件放置到系统发育树上。当发生替代率变化事件时，当前替代率由伽马分布的随机变量修改。可以使用贝叶斯推断来估计模型的参数。我们使用马尔可夫链蒙特卡罗积分来评估后验概率分布，因为后验概率涉及高维积分和求和。具体来说，我们使用Metropolis-Hastings-绿色算法与11种不同的移动类型来评估后验分布。我们通过分析来自23种哺乳动物的完整mtDNA序列数据集来演示该方法。这里提出的模型有几个潜在的优势，比其他模型，已提出放松时钟，因为它是参数，并没有假设，只有在物种形成事件的速率变化。这个模型应该被证明是有用的估计分歧时，替代率各不相同的血统。
The molecular clock hypothesis remains an important conceptual and analytical tool in evolutionary biology despite the repeated observation that the clock hypothesis does not perfectly explain observed DNA sequence variation. We introduce a parametric model that relaxes the molecular clock by allowing rates to vary across lineages according to a compound Poisson process. Events of substitution rate change are placed onto a phylogenetic tree according to a Poisson process. When an event of substitution rate change occurs, the current rate of substitution is modified by a gamma-distributed random variable. Parameters of the model can be estimated using Bayesian inference. We use Markov chain Monte Carlo integration to evaluate the posterior probability distribution because the posterior probability involves high dimensional integrals and summations. Specifically, we use the Metropolis-Hastings-Green algorithm with 11 different move types to evaluate the posterior distribution. We demonstrate the method by analyzing a complete mtDNA sequence data set from 23 mammals. The model presented here has several potential advantages over other models that have been proposed to relax the clock because it is parametric and does not assume that rates change only at speciation events. This model should prove useful for estimating divergence times when substitution rates vary across lineages.