A0636
Title: Bayesian inference from time series of allele frequency data using exact simulation techniques
Authors: Jaromir Sant - Universita di Torino (Italy) [presenting]
Paul Jenkins - University of Warwick (United Kingdom)
Dario Spano - University of Warwick (United Kingdom)
Jere Koskela - Necastle University (United Kingdom)
Abstract: A central statistical problem in population genetics is to infer evolutionary and biological parameters such as the strength of natural selection and allele age 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. An MCMC method is introduced for Bayesian inference from allele frequency time-series data based on an underlying Wright-Fisher diffusion, through which one can infer the parameters of essentially any selection model, including those with frequency-dependent effects. The chief novelty is that the method is shown to be exact in the sense that it is possible to augment the state space with the unobserved diffusion trajectory, even though the transition function 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. The method is illustrated on data capturing changes in coat color over the past 20,000 years, and evidence is found to support previous findings that the mutant alleles ASIP and MC1R responsible for changes in coat color have experienced very strong, possibly overdominant, selection, and estimates are further provided for the ages of these genes.
