Estimation of Species Tree Using Approximate Bayesian Computation
Hang Fan · OhioLink ETD Center (Ohio Library and Information Network) · 2010
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 gene tree topologies without branch lengths, and then iterates through the following sequence of steps: First, a randomly selected species tree is used to compute the distribution of gene trees topologies.This distribution is then compared to the observed gene topology frequencies, and if the fit between the observed and the predicted distribution 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.Thus, our method is able to deal with complex data in a timely and efficient way.In addition, the method does not iii require sequence data, but rather uses the observed distribution of gene topologies.Therefore, this method provides a nice alternative to other currently available methods for species tree estimation.