Pagel Mark, Meade Andrew, Barker Daniel
School of Animal and Microbial Sciences, University of Reading, Whiteknights, Reading, England.
Syst Biol. 2004 Oct;53(5):673-84. doi: 10.1080/10635150490522232.
Biologists frequently attempt to infer the character states at ancestral nodes of a phylogeny from the distribution of traits observed in contemporary organisms. Because phylogenies are normally inferences from data, it is desirable to account for the uncertainty in estimates of the tree and its branch lengths when making inferences about ancestral states or other comparative parameters. Here we present a general Bayesian approach for testing comparative hypotheses across statistically justified samples of phylogenies, focusing on the specific issue of reconstructing ancestral states. The method uses Markov chain Monte Carlo techniques for sampling phylogenetic trees and for investigating the parameters of a statistical model of trait evolution. We describe how to combine information about the uncertainty of the phylogeny with uncertainty in the estimate of the ancestral state. Our approach does not constrain the sample of trees only to those that contain the ancestral node or nodes of interest, and we show how to reconstruct ancestral states of uncertain nodes using a most-recent-common-ancestor approach. We illustrate the methods with data on ribonuclease evolution in the Artiodactyla. Software implementing the methods (BayesMultiState) is available from the authors.
生物学家常常试图从当代生物体中观察到的性状分布来推断系统发育树祖先节点的性状状态。由于系统发育树通常是从数据中推断出来的，所以在推断祖先状态或其他比较参数时，考虑树及其分支长度估计中的不确定性是很有必要的。在此，我们提出一种通用的贝叶斯方法，用于在统计学上合理的系统发育树样本中检验比较假设，重点关注重建祖先状态这一具体问题。该方法使用马尔可夫链蒙特卡罗技术来对系统发育树进行采样，并研究性状进化统计模型的参数。我们描述了如何将系统发育树的不确定性信息与祖先状态估计中的不确定性相结合。我们的方法并不将树的样本仅限制于那些包含感兴趣的一个或多个祖先节点的树，并且我们展示了如何使用最近共同祖先方法来重建不确定节点的祖先状态。我们用偶蹄目核糖核酸酶进化的数据来说明这些方法。实现这些方法的软件（BayesMultiState）可从作者处获得。
