Rapid adaptation of recombining populations on tunable fitness landscapes

Abstract

How does standing genetic variation affect polygenic adaptation in recombining populations? Despite a large body of work in quantitative genetics, epistatic and weak additive fitness effects among simultaneously segregating genetic variants are difficult to capture experimentally or to predict theoretically. In this study, we simulated adaptation on fitness landscapes with tunable ruggedness driven by standing genetic variation in recombining populations. We confirmed that recombination hinders the movement of a population through a rugged fitness landscape. When surveying the effect of epistasis on the fixation of alleles, we found that the combined effects of high ruggedness and high recombination rates lead to preferential fixation of alleles that had a high initial frequency. This indicates that positively epistatic alleles escape from being broken down by recombination only if they start at high frequency. We further infer single selection coefficients and pairwise epistasis along the adaptive path. When taking the final fixed genotype as the genetic background, we observe beneficial direct selection is overestimated and pairwise epistasis is underestimated. Quantitatively, the inferred ratio of epistasis and direct selection (≈ 1) is smaller than expected if the population was traversing the landscape randomly. Thus, adaptation on a rugged fitness landscape may lead to spurious signals of direct selection generated through epistasis. Our study highlights how the interplay of epistasis and recombination constrains the adaptation of a diverse population to a new environment.

STUN: forward-time Simulation on TUnable fitNess landscapes in recombining populations

“…The initial step of the simulation is to generate a population by specifying initial allele frequencies, either by allocating the same allele frequencies to every locus or by drawing from a folded neutral site frequency spectrum (SFS, Hudson [2015]). For the neutral SFS, it is possible to set a genetic drift threshold to exclude low-frequency alleles not experiencing significant selection during the early generations (see Li et al [2023]). The reproduction step is simulated by a Wright-Fisher model with non-overlapping, discrete generations.…”

“…We have previously applied this software to investigate how the interplay between recombination and epistasis affects adaptation (Li et al [2023]). To illustrate the usefulness of our program, we give an additional toy example here.…”