Signature-Informed Selection Detection: A Novel Method for Multi-Locus Temporal Population Genetic Model with Recombination

TL;DR

This paper introduces a new Bayesian method using signature kernel scoring rules for inferring selection coefficients in multi-locus population genetics models with recombination, demonstrated through simulations and real data.

Contribution

We develop a novel generalized Bayesian framework employing signature kernel scoring rules for multi-locus selection inference, accommodating complex models with recombination.

Findings

Effective inference of selection coefficients in multi-locus models.

Outperforms existing methods in simulation studies.

Successfully applied to real yeast and Drosophila datasets.

Abstract

In population genetics, there is often interest in inferring selection coefficients. This task becomes more challenging if multiple linked selected loci are considered simultaneously. For such a situation, we propose a novel generalized Bayesian framework where we compute a scoring rule posterior for the selection coefficients in multi-locus temporal population genetics models. As we consider trajectories of allele frequencies over time as our data, we choose to use a signature kernel scoring rule - a kernel scoring rule defined for high-dimensional time-series data using iterated path integrals of a path (called signatures). We can compute an unbiased estimate of the signature kernel score using model simulations. This enables us to sample asymptotically from the signature kernel scoring rule posterior of the selection coefficients using pseudo-marginal MCMC-type algorithms. Through a simulation study, we were able to show the inferential efficacy of our method compared to existing benchmark methods for two and three selected locus scenarios under the standard Wright-Fisher model with recombination and selection. We also consider a negative frequency-dependent selection model for one and two locus scenarios, and also joint inference of selection coefficients and initial haplotype frequencies under the standard Wright-Fisher model. Finally, we illustrate the application of our inferential method for two real-life dataset. More specifically, we consider a data set on Yeast, as well as data from an Evolve and Resequence (E\&R) experiment on {\em Drosophila simulans}.