Bayesian Analysis of Evolutionary Divergence with Genomic Data under Diverse Demographic Models

Abstract

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. The new method implements a 2-step analysis, with an initial Markov chain Monte Carlo (MCMC) phase that samples simple coalescent trees, followed by the calculation of the joint posterior density for the parameters of a demographic model. In step 1, the MCMC sampling phase, the method uses a reduced state space, consisting of coalescent trees without migration paths, and a simple importance sampling distribution without the demography of interest. Once obtained, a single sample of trees can be used in step 2 to calculate the joint posterior density for model parameters under multiple diverse demographic models, without having to repeat MCMC runs. Because migration paths are not included in the state space of the MCMC phase, but rather are handled by analytic integration in step 2 of the analysis, the method is scalable to a large number of loci with excellent MCMC mixing properties. With an implementation of the new method in the computer program MIST, we demonstrate the method's accuracy, scalability, and other advantages using simulated data and DNA sequences of two common chimpanzee subspecies: Pan troglodytes (P. t.) troglodytes and P. t. verus.

Recent advances in Bayesian inference of isolation-with-migration models

“…Mixing issues affect the efficiency, and hence the computing time of an MCMC simulation. In the inference of IM models, poor mixing is a major roadblock to the analysis of genomic data or the co-inference of phylogeny [1,16]. For example, the state of genealogy and demographic parameters are given as Fig.…”

“…Estimating divergence from genetic data is difficult because of conflicting evolutionary processes. Genetic drift elevates divergence between populations or between species, while gene flow can remove signals of divergence [1]. An isolation-with-migration (IM) model is a widely used demographic model describing the two conflicting signals.…”

“…Efforts to distinguish between isolation and migration began about 20 years ago, and many methods have employed a Markov chain Monte Carlo (MCMC) simulation to infer an IM model [2][3][4][17][18][19][20][21]. However, most methods have a major roadblock of a long computational time of an MCMC simulation, which typically limits the amount of data that can be analyzed [1]. In addition, the joint estimation of both phylogeny and an IM model is known to be tremendously difficult [16].…”

“…IMa3 [16] is the most recent version of IM/IMa series software and infers the phylogeny and IM models. MIST [1] needs a known (or assumed) phylogeny but is able to analyze thousands of loci. AIM [14,22] is a package in the popular BEAST platform and also infers phylogeny in the presence of gene flow.…”

Accounting for Gene Flow from Unsampled ‘Ghost’ Populations while Estimating Evolutionay History under the Isolation with Migration Model

“…This class of models is widely used as a model of divergence with gene flow, where two sampled populations or subpopulations have diverged from the ancestor, and maintain gene flow via exchange of migrants since divergence. Numerous tools have been developed to estimate evolutionary history under the IM model, including the IM suite (, and (Sethuraman & Hey, 2016; Hey et al, 2018), (Chung & Hey, 2017), and (Gutenkunst, Hernandez, Williamson, & Bustamante, 2009). The IM suite of tools are genealogy samplers that use a Metropolis Coupled Markov Chain Monte Carlo (MCMCMC) to explore the parameter space of evolutionary demographic parameters, propose updates to parameters and genealogies, and examine the posterior density distribution of the set of estimable parameters given genomic data from two or more sampled populations.…”