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Related Experiment Video

Updated: Feb 16, 2026

A Quantitative Fitness Analysis Workflow

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

Inferring genetic interactions from comparative fitness data.

Kristina Crona¹, Alex Gavryushkin^2,3, Devin Greene¹

¹Department of Mathematics and Statistics, American University, Washington, DC, United States.

|December 21, 2017

Summary

Researchers developed tools to infer gene interactions from incomplete fitness data. This method, using fitness rank orders, reveals higher-order epistasis in diverse biological systems like HIV and antibiotic resistance.

Keywords:

computational biology epistasis fitness graph fitness landscape gene interactions none partial order rank order systems biology

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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.