The strength of genetic interactions scales weakly with the mutational effects

TL;DR

This study reveals a surprisingly weak scaling law for genetic interaction strength based on large-scale yeast data, challenging existing models and providing insights into the structure of fitness landscapes and evolutionary dynamics.

Contribution

It introduces a geometric definition of epistasis and demonstrates a simple scaling law for genetic interactions across many mutations, advancing understanding of fitness landscape roughness.

Findings

Weak scaling law for genetic interactions

Quantification of fitness landscape roughness

Differences from Fisher's geometric model

Abstract

Genetic interactions pervade every aspect of biology, from evolutionary theory where they determine the accessibility of evolutionary paths, to medicine where they contribute to complex genetic diseases. Until very recently, studies on epistatic interactions have been based on a handful of mutations, providing at best anecdotal evidence about the frequency and the typical strength of genetic interactions. In this study we analyze the publicly available Data Repository of Yeast Genetic INteractions (DRYGIN), which contains the growth rates of over five million double gene knockout mutants. We discuss a geometric definition of epistasis which reveals a simple and surprisingly weak scaling law for the characteristic strength of genetic interactions as a function of the effects of the mutations being combined. We then utilize this scaling to quantify the roughness of naturally occurring fitness landscapes. Finally, we show how the observed roughness differs from what is predicted by Fisher's geometric model of epistasis and discuss its consequences on the evolutionary dynamics. Although epistatic interactions between specific genes remain largely unpredictable, the statistical properties of an ensemble of interactions can display conspicuous regularities and be described by simple mathematical laws. By exploiting the amount of data produced by modern high-throughput techniques it is now possible to thoroughly test the predictions of theoretical models of genetic interactions and to build informed computational models of evolution on realistic fitness landscapes.