RT Journal Article
SR Electronic
T1 Bayesian Analysis of Evolutionary Divergence with Genomic Data Under Diverse Demographic Models
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 080606
DO 10.1101/080606
A1 Yujin Chung
A1 Jody Hey
YR 2016
UL http://biorxiv.org/content/early/2016/10/12/080606.abstract
AB We present a new Bayesian method for estimating demographic and phylogenetic history using population genomic data. Several key innovations are introduced that allow the study of diverse models within an Isolation with Migration framework. For the Markov chain Monte Carlo (MCMC) phase of the analysis, we use a reduced state space, consisting of simple coalescent trees without migration paths, and a simple importance sampling distribution without demography. Migration paths are analytically integrated using a Markov chain as a representation of genealogy. The new method is scalable to a large number of loci with excellent MCMC mixing properties. Once obtained, a single sample of trees is used to calculate the joint posterior density for model parameters under multiple diverse demographic models, without having to repeat MCMC runs. As implemented in the computer program MIST, we demonstrate the accuracy, scalability and other advantages of the new method using simulated data and DNA sequences of two common chimpanzee subspecies: Pan troglodytes troglodytes (P. t.) and P. t. verus.