Article (Scientific journals)
Discovering pair-wise genetic interactions: an information theory-based approach.
Ignac, Tomasz; Skupin, Alexander; Sakhanenko, Nikita A. et al.
2014In PLoS ONE, 9 (3), p. 92310
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Abstract :
[en] 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.
Research center :
- Luxembourg Centre for Systems Biomedicine (LCSB): Integrative Cell Signalling (Skupin Group)
Luxembourg Centre for Systems Biomedicine (LCSB): Experimental Neurobiology (Balling Group)
- Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group)
Disciplines :
Life sciences: Multidisciplinary, general & others
Author, co-author :
Ignac, Tomasz ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Skupin, Alexander  ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Sakhanenko, Nikita A.
Galas, David J. 
External co-authors :
yes
Language :
English
Title :
Discovering pair-wise genetic interactions: an information theory-based approach.
Publication date :
2014
Journal title :
PLoS ONE
ISSN :
1932-6203
Publisher :
Public Library of Science, United States - California
Volume :
9
Issue :
3
Pages :
e92310
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBilu :
since 03 November 2015

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