WFABC: a Wright-Fisher ABC-based approach for inferring effective population sizes and selection coefficients from time-sampled data

TL;DR

This paper introduces WFABC, an ABC-based method for accurately estimating effective population sizes and selection coefficients from time-sampled genetic data, demonstrating its effectiveness through simulations and real data analysis.

Contribution

The paper presents a novel ABC-based approach, WFABC, that improves inference of population genetic parameters from time-series data, including genome-wide Ne and site-specific selection coefficients.

Findings

WFABC accurately infers genome-wide Ne from time-serial data.

WFABC provides precise estimates of site-specific selection coefficients.

Application to real data suggests a recessive lethal model explains allele frequency variation.

Abstract

With novel developments in sequencing technologies, time-sampled data are becoming more available and accessible. Naturally, there have been efforts in parallel to infer population genetic parameters from these datasets. Here, we compare and analyze four recent approaches based on the Wright-Fisher model for inferring selection coefficients (s) given effective population size (Ne), with simulated temporal datasets. Furthermore, we demonstrate the advantage of a recently proposed ABC-based method that is able to correctly infer genome-wide average Ne from time-serial data, which is then set as a prior for inferring per-site selection coefficients accurately and precisely. We implement this ABC method in a new software and apply it to a classical time-serial dataset of the medionigra genotype in the moth Panaxia dominula. We show that a recessive lethal model is the best explanation for the observed variation in allele frequency by implementing an estimator of the dominance ratio (h).