Bayesian inference of natural selection from allele frequency time series

TL;DR

This paper presents a Bayesian method utilizing allele frequency time series data from ancient DNA to accurately infer natural selection parameters, allele age, and demographic effects, improving evolutionary insights.

Contribution

The authors develop a novel path augmentation Bayesian approach with MCMC for analyzing allele frequency time series, accounting for complex demographic histories.

Findings

Effective estimation of selection coefficients and allele age from simulated data.

Ignoring demographic history can bias natural selection inference.

Software implementation available in C++ for practical use.

Abstract

The advent of accessible ancient DNA technology now allows the direct ascertainment of allele frequencies in ancestral populations, thereby enabling the use of allele frequency time series to detect and estimate natural selection. Such direct observations of allele frequency dynamics are expected to be more powerful than inferences made using patterns of linked neutral variation obtained from modern individuals. We develop a Bayesian method to make use of allele frequency time series data and infer the parameters of general diploid selection, along with allele age, in non-equilibrium populations. We introduce a novel path augmentation approach, in which we use Markov chain Monte Carlo to integrate over the space of allele frequency trajectories consistent with the observed data. Using simulations, we show that this approach has good power to estimate selection coefficients and allele age. Moreover, when applying our approach to data on horse coat color, we find that ignoring a relevant demographic history can significantly bias the results of inference. Our approach is made available in a C++ software package.