Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH 43210, USA.
Mol Phylogenet Evol. 2011 May;59(2):354-63. doi: 10.1016/j.ympev.2011.02.019. Epub 2011 Mar 21.
Development of methods for estimating species trees from multilocus data is a current challenge in evolutionary biology. We propose a method for estimating the species tree topology and branch lengths using approximate Bayesian computation (ABC). The method takes as data a sample of observed rooted gene tree topologies, and then iterates through the following sequence of steps: First, a randomly selected species tree is used to compute the distribution of rooted gene tree topologies. This distribution is then compared to the observed gene topology frequencies, and if the fit between the observed and the predicted distributions is close enough, the proposed species tree is retained. Repeating this many times leads to a collection of retained species trees that are then used to form the estimate of the overall species tree. We test the performance of the method, which we call ST-ABC, using both simulated and empirical data. The simulation study examines both symmetric and asymmetric species trees over a range of branch lengths and sample sizes. The results from the simulation study show that the model performs very well, giving accurate estimates for both the topology and the branch lengths across the conditions studied, and that a sample size of 25 loci appears to be adequate for the method. Further, we apply the method to two empirical cases: a 4-taxon data set for primates and a 7-taxon data set for yeast. In both cases, we find that estimates obtained with ST-ABC agree with previous studies. The method provides efficient estimation of the species tree, and does not require sequence data, but rather the observed distribution of rooted gene topologies without branch lengths. Therefore, this method is a useful alternative to other currently available methods for species tree estimation.
从多基因数据估计物种树的方法是进化生物学当前的一个挑战。我们提出了一种使用近似贝叶斯计算（ABC）估计物种树拓扑结构和分支长度的方法。该方法将观察到的有根基因树拓扑结构的样本作为数据，并通过以下一系列步骤迭代：首先，使用随机选择的物种树来计算有根基因树拓扑结构的分布。然后将该分布与观察到的基因拓扑结构频率进行比较，如果观察到的和预测到的分布之间的拟合足够接近，则保留所提出的物种树。重复多次，就会得到一组保留的物种树，然后使用这些树来形成对总体物种树的估计。我们使用模拟和经验数据测试了该方法的性能，我们称之为 ST-ABC。模拟研究考察了在一系列分支长度和样本大小下的对称和不对称物种树。模拟研究的结果表明，该模型表现非常出色，在研究的条件下，对拓扑结构和分支长度都能给出准确的估计，并且样本大小为 25 个基因座似乎足以满足该方法的要求。此外，我们将该方法应用于两个经验案例：灵长类动物的 4 分类群数据集和酵母的 7 分类群数据集。在这两种情况下，我们发现 ST-ABC 得到的估计值与以前的研究结果一致。该方法提供了物种树的有效估计，并且不需要序列数据，而是需要没有分支长度的有根基因拓扑结构的观察分布。因此，该方法是目前其他可用的物种树估计方法的一种有用替代方法。
