Combinatorial genetic analysis of a regulatory network reveals the importance of higher order epistasis for gene deletion phenotypes

Abstract

A key challenge in biology is to understand how mutations combine to alter phenotypes. Each genetic variant in a genome can have diverse effects, for example decreasing, increasing, inactivating, or changing the function of a protein or RNA. In contrast, systematic analyses of how mutations interact have typically used a single variant of each gene, most often a null allele. We therefore lack an understanding of how the full range of genetic variants that occur in individuals can interact. To address this shortcoming, we developed an approach to combine >5000 pairs of diverse mutations in a model regulatory network. The outcome of most mutation combinations could be accurately predicted by simple rules that capture the ‘stereotypical’ genetic interactions (epistasis) in the network. However, for individual genotypes, additional, unexpected pairwise and higher order genetic interactions can be important. These include ‘harmonious’ combinations of individually detrimental alleles that reconstitute alternative functional switches. Our results provide an overview of how the full spectra of possible mutations in genes interact and how these interactions can be predicted. Moreover, they illustrate the importance of rare genetic interactions for individuals, including the impact of higher order epistatic interactions that dramatically alter the consequences of inactivating genes.