Efficient Maximum-Likelihood Inference For The Isolation-With-Initial-Migration Model With Potentially Asymmetric Gene Flow

TL;DR

This paper introduces a fast maximum-likelihood method for the IIM model, allowing for asymmetric gene flow and unequal population sizes, enabling efficient analysis of large genomic datasets to infer speciation history.

Contribution

It develops a computationally efficient maximum-likelihood approach for the IIM model with asymmetric gene flow, improving inference speed and accuracy over previous methods.

Findings

Method fits large genomic datasets in minutes

Able to distinguish between different evolutionary scenarios

Applied successfully to Drosophila sequence data

Abstract

The isolation-with-migration (IM) model is a common tool to make inferences about the presence of gene flow during speciation, using polymorphism data. However, Becquet and Przeworski (2009) report that the parameter estimates obtained by fitting the IM model are very sensitive to the model's assumptions, including the assumption of constant gene flow until the present. This paper is concerned with the isolation-with-initial-migration (IIM) model of Wilkinson-Herbots (2012), which drops precisely this assumption. In the IIM model, one ancestral population divides into two descendant subpopulations, between which there is an initial period of gene flow and a subsequent period of isolation. We derive a fast method of fitting an extended version of the IIM model, which allows for asymmetric gene flow and unequal subpopulation sizes. This is a maximum-likelihood method, applicable to observations on the number of different sites between pairs of DNA sequences from a large number of independent loci. In addition to obtaining parameter estimates, our method can also be used to distinguish between alternative models representing different evolutionary cenarios, by means of likelihood ratio tests. We illustrate the procedure on pairs of Drosophila sequences from approximately 30,000 loci. The computing time needed to fit the most complex version of the model to this data set is only a couple of minutes. The code to fit the IIM model can be found in the supplementary files of this paper.