Quantitative analyses of empirical fitness landscapes

TL;DR

This paper reviews methods to measure epistasis in empirical fitness landscapes, compares their effectiveness, and analyzes how landscape ruggedness relates to mutation effects and epistasis types, also comparing empirical data with the Rough Mt. Fuji model.

Contribution

It systematically compares measures of epistasis for fitness landscapes and assesses factors influencing landscape ruggedness, providing insights into their empirical and theoretical relationships.

Findings

Empirical landscape ruggedness is influenced by beneficial or deleterious mutations.

Intra- and intergenic epistasis differently affect landscape ruggedness.

The Rough Mt. Fuji model effectively captures key features of empirical landscapes.

Abstract

The concept of a fitness landscape is a powerful metaphor that offers insight into various aspects of evolutionary processes and guidance for the study of evolution. Until recently, empirical evidence on the ruggedness of these landscapes was lacking, but since it became feasible to construct all possible genotypes containing combinations of a limited set of mutations, the number of studies has grown to a point where a classification of landscapes becomes possible. The aim of this review is to identify measures of epistasis that allow a meaningful comparison of fitness landscapes and then apply them to the empirical landscapes to discern factors that affect ruggedness. The various measures of epistasis that have been proposed in the literature appear to be equivalent. Our comparison shows that the ruggedness of the empirical landscape is affected by whether the included mutations are beneficial or deleterious and by whether intra- or intergenic epistasis is involved. Finally, the empirical landscapes are compared to landscapes generated with the Rough Mt.\ Fuji model. Despite the simplicity of this model, it captures the features of the experimental landscapes remarkably well.