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

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

Author information

1
Luxembourg Centre for Systems Biomedicine, Esch-sur-Alzette, Luxembourg; Pacific Northwest Diabetes Research Institute, Seattle, Washington, United States of America.
2
Luxembourg Centre for Systems Biomedicine, Esch-sur-Alzette, Luxembourg; National Center for Microscopy and Imaging Research, University of California San Diego, La Jolla, California, United States of America.
3
Pacific Northwest Diabetes Research Institute, Seattle, Washington, United States of America.

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.

PMID:
24670935
PMCID:
PMC3966778
DOI:
10.1371/journal.pone.0092310
[Indexed for MEDLINE]
Free PMC Article

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