Fast and Consistent Estimation of Species Trees Using Supermatrix Rooted Triples
Fast and Consistent Estimation of Species Trees Using Supermatrix Rooted Triples
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DOI：
10.1093/molbev/msp250
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发表时间：
2010-03-01
影响因子：
10.7
通讯作者：
Degnan, James H.
中科院分区：
文献类型：
--
作者：
DeGiorgio, Michael;Degnan, James H.
串联序列比对通常用于推断物种水平的关系。先前的研究表明，当基因座由于不完整的谱系排序而具有不同的基因树拓扑结构时，使用最大似然（ML）对串联数据进行分析可能会产生误导性结果。在这里，我们开发了一个多项式时间的方法，利用修改后的mincut超树算法来构建一个估计的物种树推断根三元组的串联比对。我们将这种方法称为超矩阵根三元组（SMRT），并使用符号SMRT-ML时，根三元组推断ML。我们使用模拟研究SMRT-ML的性能Jukes-Cantor和一般的时间可逆的替代模型的四个和五个类群的物种树，也适用于酵母基因的经验数据集的方法。我们发现，SMRT-ML收敛到正确的物种树，在许多情况下，ML在完整的连接数据集未能做到这一点。SMRT-ML可能是保守的，因为它的输出树通常对于有问题的分支是部分未解决的。我们分析表明，当物种树是时钟状的，突变发生在Cavender-Farris-Neyman替代模型下，随着基因数量的增加，SMRT-ML越来越有可能推断出正确的物种树，即使最有可能的基因树与物种树不匹配。因此，SMRT-ML是一个计算效率和统计一致的估计的物种树时，基因树分布根据多物种合并模型。
Concatenated sequence alignments are often used to infer species-level relationships. Previous studies have shown that analysis of concatenated data using maximum likelihood (ML) can produce misleading results when loci have differing gene tree topologies due to incomplete lineage sorting. Here, we develop a polynomial time method that utilizes the modified mincut supertree algorithm to construct an estimated species tree from inferred rooted triples of concatenated alignments. We term this method SuperMatrix Rooted Triple (SMRT) and use the notation SMRT-ML when rooted triples are inferred by ML. We use simulations to investigate the performance of SMRT-ML under Jukes-Cantor and general time-reversible substitution models for four- and five-taxon species trees and also apply the method to an empirical data set of yeast genes. We find that SMRT-ML converges to the correct species tree in many cases in which ML on the full concatenated data set fails to do so. SMRT-ML can be conservative in that its output tree is often partially unresolved for problematic clades. We show analytically that when the species tree is clocklike and mutations occur under the Cavender-Farris-Neyman substitution model, as the number of genes increases, SMRT-ML is increasingly likely to infer the correct species tree even when the most likely gene tree does not match the species tree. SMRT-ML is therefore a computationally efficient and statistically consistent estimator of the species tree when gene trees are distributed according to the multispecies coalescent model.