Inferring genetic interactions from comparative fitness data

Area of Science:

• Evolutionary Biology
• Genetics
• Bioinformatics

Background:

• Darwinian fitness is crucial in evolutionary biology but difficult to measure comprehensively.
• Incomplete fitness data from natural populations hinders the study of gene interactions.

Purpose of the Study:

• To develop quantitative tools for inferring epistatic gene interactions from incomplete fitness landscapes.
• To enable the study of genetic interactions even with imprecise measurements or missing observations.

Main Methods:

• Utilizing fitness rank orders (complete or partial) to infer genetic interactions.
• Developing a theoretical framework to characterize rank orders implying higher-order epistasis.
• Applying the theory to diverse genetic systems.

Main Results:

• Genetic interactions can be inferred from fitness rank orders, even partial ones.
• A complete characterization of rank orders indicating higher-order epistasis was provided.
• Higher-order interactions were revealed in HIV-1, Plasmodium vivax, Aspergillus niger, and TEM-family β-lactamase systems.

Conclusions:

• The developed quantitative tools facilitate the investigation of diverse genetic interactions.
• The approach is applicable to various gene interaction types and biological systems.
• Higher-order epistasis is a common feature across different genetic systems.