Bayesian Coalescent Inference of Past Population Dynamics from Molecular Sequences

TLDR

The authors present the Bayesian skyline plot, a nonparametric method for estimating past population dynamics from molecular sequences. They implement a Markov chain Monte Carlo sampler that generates posterior distributions of effective population size, validate it on simulated data, and benchmark it against existing coalescent methods using hepatitis C virus and bison mitochondrial DNA datasets. The method accurately reconstructs simulated demographic histories and reveals a previously unrecognized 10,000‑year‑old bottleneck in Beringian bison coinciding with early human presence and Holocene megafaunal extinctions.

Abstract

We introduce the Bayesian skyline plot, a new method for estimating past population dynamics through time from a sample of molecular sequences without dependence on a prespecified parametric model of demographic history. We describe a Markov chain Monte Carlo sampling procedure that efficiently samples a variant of the generalized skyline plot, given sequence data, and combines these plots to generate a posterior distribution of effective population size through time. We apply the Bayesian skyline plot to simulated data sets and show that it correctly reconstructs demographic history under canonical scenarios. Finally, we compare the Bayesian skyline plot model to previous coalescent approaches by analyzing two real data sets (hepatitis C virus in Egypt and mitochondrial DNA of Beringian bison) that have been previously investigated using alternative coalescent methods. In the bison analysis, we detect a severe but previously unrecognized bottleneck, estimated to have occurred 10,000 radiocarbon years ago, which coincides with both the earliest undisputed record of large numbers of humans in Alaska and the megafaunal extinctions in North America at the beginning of the Holocene.