Title: Inference of natural selection from allele frequency time series data using exact simulation techniques
Authors: Jaromir Sant - University of Warwick (United Kingdom)
Paul Jenkins - University of Warwick (United Kingdom) [presenting]
Jere Koskela - University of Warwick (United Kingdom)
Dario Spano - University of Warwick (United Kingdom)
Abstract: A standard problem in population genetics is to infer evolutionary and biological parameters such as the effective population size, mutation rates, and strength of natural selection from DNA samples extracted from a contemporary population. That all samples come only from the present-day has long been known to limit statistical inference; there is potentially more information available if one also has access to ancient DNA so that inference is based on a time-series of historical changes in allele frequencies. We introduce a Markov Chain Monte Carlo method for Bayesian inference from allele frequency time-series data based on an underlying Wright-Fisher diffusion model of evolution. The chief novelty is that we show this method to be exact in the sense that it is possible to augment the state space explored by MCMC with the unobserved diffusion trajectory, even though the transition function of this diffusion is intractable. Through careful design of a proposal distribution, we describe an efficient method in which updates to the trajectory and accept/reject decisions are calculated without error. We illustrate the method on data capturing changes in coat colour during the domestication of the horse.