大序列数下的祖先状态重建及边长估计
Ancestral state reconstruction with large numbers of sequences and edge-length estimation
摘要：
基于似然的方法被广泛认为是重建祖先状态的最佳方法。虽然已经做了很多努力来研究这些方法的性能，以前的作品往往假设树的拓扑结构和边长是已知的。在某些情况下，树拓扑结构可能是合理的研究中的类群众所周知的。然而，当序列长度远小于物种数时，不太可能准确地估计边长。我们研究了星树下离散性状祖先状态的极大似然和经验贝叶斯估计的一致性。我们证明了基于似然的重建是一致的对称模型下，但可以是不一致的非对称模型。然而，我们发现，一个简单的相合估计量的祖先状态下的非对称模型。结果表明，可能性方法可以意外地具有不期望的属性，因为考虑的序列的数量变得非常大。更广泛的影响的结果进行了讨论。
英文摘要：
Likelihood-based methods are widely considered the best approaches for reconstructing ancestral states. Although much effort has been made to study properties of these methods, previous works often assume that both the tree topology and edge lengths are known. In some scenarios the tree topology might be reasonably well known for the taxa under study. When sequence length is much smaller than the number of species, however, edge lengths are not likely to be accurately estimated. We study the consistency of the maximum likelihood and empirical Bayes estimators of ancestral state of discrete traits in such settings under a star tree. We prove that the likelihood-based reconstruction is consistent under symmetric models but can be inconsistent under non-symmetric models. We show, however, that a simple consistent estimator for the ancestral states is available under non-symmetric models. The results illustrate that likelihood methods can unexpectedly have undesirable properties as the number of sequences considered get very large. Broader implications of the results are discussed.
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