Discovering pair-wise genetic interactions: an information theory-based approach

PLoS One. 2014 Mar 26;9(3):e92310. doi: 10.1371/journal.pone.0092310. eCollection 2014.

Abstract

Phenotypic variation, including that which underlies health and disease in humans, results in part from multiple interactions among both genetic variation and environmental factors. While diseases or phenotypes caused by single gene variants can be identified by established association methods and family-based approaches, complex phenotypic traits resulting from multi-gene interactions remain very difficult to characterize. Here we describe a new method based on information theory, and demonstrate how it improves on previous approaches to identifying genetic interactions, including both synthetic and modifier kinds of interactions. We apply our measure, called interaction distance, to previously analyzed data sets of yeast sporulation efficiency, lipid related mouse data and several human disease models to characterize the method. We show how the interaction distance can reveal novel gene interaction candidates in experimental and simulated data sets, and outperforms other measures in several circumstances. The method also allows us to optimize case/control sample composition for clinical studies.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Body Weight / genetics
  • Epistasis, Genetic*
  • Female
  • Genetic Markers
  • Humans
  • Information Theory*
  • Male
  • Mice
  • Models, Genetic*
  • Phenotype
  • Polymorphism, Single Nucleotide
  • Saccharomyces cerevisiae / genetics

Substances

  • Genetic Markers

Grants and funding

This work has been funded by “le plan Technologies de la Santé par le Gouvernment du Grand-Duché de Luxembourg” through Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, and supported by NSF grant IIS-1340619. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.