Adaptive walks and distribution of beneficial fitness effects

TL;DR

This paper investigates how asexual populations adapt on rugged fitness landscapes by analyzing the distribution of beneficial effects, revealing distinct patterns of fitness gains depending on the extreme value domain, with implications for experimental identification.

Contribution

It introduces a model considering large-effect mutations and identifies how fitness differences evolve in different extreme value domains, differing from previous small-effect assumptions.

Findings

Fitness differences show diminishing returns in Weibull domain.

Fitness differences show accelerating returns in Fr{é}chet domain.

The length of adaptive walks varies qualitatively with mutation effect size.

Abstract

We study the adaptation dynamics of a maladapted asexual population on rugged fitness landscapes with many local fitness peaks. The distribution of beneficial fitness effects is assumed to belong to one of the three extreme value domains, viz. Weibull, Gumbel and Fr{\'e}chet. We work in the strong selection-weak mutation regime in which beneficial mutations fix sequentially, and the population performs an uphill walk on the fitness landscape until a local fitness peak is reached. A striking prediction of our analysis is that the fitness difference between successive steps follows a pattern of diminishing returns in the Weibull domain and accelerating returns in the Fr{\'e}chet domain, as the initial fitness of the population is increased. These trends are found to be robust with respect to fitness correlations. We believe that this result can be exploited in experiments to determine the extreme value domain of the distribution of beneficial fitness effects. Our work here differs significantly from the previous ones that assume the selection coefficient to be small. On taking large effect mutations into account, we find that the length of the walk shows different qualitative trends from those derived using small selection coefficient approximation.