Inferring natural selection and allele age from allele frequency time series data via exact simulation

Sant Jaromir, University of Oxford
Jenkins Paul A, University of Warwick
Spanò Dario, University of Warwick
Koskela Jere, Newcastle University

A standard problem in population genetics is to infer evolutionary parameters such as the strength of natural selection and allele age based on a contemporary sample of individuals. That all information stems from a single point in time 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. In this talk I will 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 by augmenting the state space with the unobserved diffusion trajectory we are able to develop an efficient method in which trajectory updates and accept/reject probabilities can be calculated without error, in spite of the diffusion's intractable transition density. We illustrate the method's performance both on simulated and real data. This is joint work with Paul Jenkins, Jere Koskela, and Dario Spano (University of Warwick).

Area: CS13 - Diffusion and coalescent processes in population genetics (Martina Favero)

Keywords: Population Genetics, Wright--Fisher diffusion, Exact Inference, Markov Chain Monte Carlo