Performance of maximum parsimony and likelihood phylogenetics when evolution is heterogeneous
Performance of maximum parsimony and likelihood phylogenetics when evolution is heterogeneous
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DOI：
10.1038/nature02917
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发表时间：
2004-10-21
期刊：
影响因子：
64.8
通讯作者：
Thornton, JW
中科院分区：
文献类型：
--
作者：
Kolaczkowski, B;Thornton, JW
比较生物学中的所有推论都依赖于对进化关系的准确估计。最近的系统发育分析已经从最大简约转向最大似然和贝叶斯马尔可夫链蒙特卡罗(BMCMC)的概率技术。这些概率技术代表了统计系统发育学的一种参数方法，因为它们评估拓扑的标准--给定树的数据的概率--是参照一个明确的进化模型计算的，根据该模型，数据被假定为相同分布。最大简约度可以被认为是非参数的，因为树的评估基于一般度量-在给定树上生成数据所需的最小字符状态更改次数-而不假设特定的分布(1)。向参数方法的转变在很大程度上是因为研究表明，尽管这两种方法在大多数情况下都表现良好(2)，但最大简约度强烈倾向于在某些分支长度组合下恢复不正确的树，而最大似然不是(3-6)。所有这些评估都是通过一个大体上相同的进化过程来模拟序列，在这个过程中，数据是相同分布的。然而，有充分的证据表明，现实世界的基因序列是异质性进化的，并不是相同分布的(7-16)。在这里，我们表明，最大似然和BMCMC可以变得强烈的偏差和统计不一致时，序列位置的发展速度不同的时间不同的变化。最大简约度在广泛的测试条件下比当前的参数方法表现得更好，包括中等的异质性和通常不被认为困难的系统发育问题。
All inferences in comparative biology depend on accurate estimates of evolutionary relationships. Recent phylogenetic analyses have turned away from maximum parsimony towards the probabilistic techniques of maximum likelihood and bayesian Markov chain Monte Carlo (BMCMC). These probabilistic techniques represent a parametric approach to statistical phylogenetics, because their criterion for evaluating a topology-the probability of the data, given the tree-is calculated with reference to an explicit evolutionary model from which the data are assumed to be identically distributed. Maximum parsimony can be considered nonparametric, because trees are evaluated on the basis of a general metric-the minimum number of character state changes required to generate the data on a given tree-without assuming a specific distribution(1). The shift to parametric methods was spurred, in large part, by studies showing that although both approaches perform well most of the time(2), maximum parsimony is strongly biased towards recovering an incorrect tree under certain combinations of branch lengths, whereas maximum likelihood is not(3-6). All these evaluations simulated sequences by a largely homogeneous evolutionary process in which data are identically distributed. There is ample evidence, however, that real-world gene sequences evolve heterogeneously and are not identically distributed(7-16). Here we show that maximum likelihood and BMCMC can become strongly biased and statistically inconsistent when the rates at which sequence sites evolve change non-identically over time. Maximum parsimony performs substantially better than current parametric methods over a wide range of conditions tested, including moderate heterogeneity and phylogenetic problems not normally considered difficult.