Inferring modulators of genetic interactions with epistatic nested effects models

Area of Science:

• Systems Biology
• Computational Biology
• Genetics

Background:

• Genetic interaction maps reveal functional redundancies in cellular networks.
• High-dimensional gene expression profiles offer detailed insights into genetic interactions.
• Interpreting mixed epistasis, where different gene sets respond variably, remains challenging.

Purpose of the Study:

• To test the hypothesis that a third gene can modulate mixed epistasis between a gene pair.
• To extend Nested Effects Models (NEMs) to incorporate logical functions for gene interactions.
• To develop a computational framework for analyzing complex genetic interactions.

Main Methods:

• Extension of the Nested Effects Models (NEMs) framework.
• Incorporation of logical functions to model regulator interactions.
• Benchmarking using simulation studies and application to S. cerevisiae deletion mutant data.

Main Results:

• The extended NEMs accurately infer underlying genetic interaction models in simulations.
• Epistatic NEMs successfully identify modulators of genetic interactions in yeast kinase and phosphatase data.
• The approach provides a robust method for dissecting complex gene regulatory networks.

Conclusions:

• Mixed epistasis can be explained by the action of modulating genes.
• Epistatic NEMs offer a powerful tool for uncovering gene regulatory mechanisms.
• The R-package 'epiNEM' facilitates the analysis of complex genetic interactions.