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BMC Genomics. 2009; 10: 509.
Published online Nov 4, 2009. doi:  10.1186/1471-2164-10-509
PMCID: PMC2779196
Information-theoretic gene-gene and gene-environment interaction analysis of quantitative traits
Pritam Chanda,1 Lara Sucheston,2,3 Song Liu,2,3 Aidong Zhang,1 and Murali Ramanathancorresponding author4
1Department of Computer Science and Engineering, State University of New York, Buffalo, NY, USA
2Department of Biostatistics, State University of New York, Buffalo, NY
3Division of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY, USA
4Department of Pharmaceutical Sciences, State University of New York, Buffalo, NY, USA
corresponding authorCorresponding author.
Pritam Chanda: pchanda/at/cse.buffalo.edu; Lara Sucheston: Lsuchest/at/buffalo.edu; Song Liu: Song.Liu/at/RoswellPark.org; Aidong Zhang: AZhang/at/cse.buffalo.edu; Murali Ramanathan: murali/at/buffalo.edu
Received October 8, 2008; Accepted November 4, 2009.
Abstract
Background
The purpose of this research was to develop a novel information theoretic method and an efficient algorithm for analyzing the gene-gene (GGI) and gene-environmental interactions (GEI) associated with quantitative traits (QT). The method is built on two information-theoretic metrics, the k-way interaction information (KWII) and phenotype-associated information (PAI). The PAI is a novel information theoretic metric that is obtained from the total information correlation (TCI) information theoretic metric by removing the contributions for inter-variable dependencies (resulting from factors such as linkage disequilibrium and common sources of environmental pollutants).
Results
The KWII and the PAI were critically evaluated and incorporated within an algorithm called CHORUS for analyzing QT. The combinations with the highest values of KWII and PAI identified each known GEI associated with the QT in the simulated data sets. The CHORUS algorithm was tested using the simulated GAW15 data set and two real GGI data sets from QTL mapping studies of high-density lipoprotein levels/atherosclerotic lesion size and ultra-violet light-induced immunosuppression. The KWII and PAI were found to have excellent sensitivity for identifying the key GEI simulated to affect the two quantitative trait variables in the GAW15 data set. In addition, both metrics showed strong concordance with the results of the two different QTL mapping data sets.
Conclusion
The KWII and PAI are promising metrics for analyzing the GEI of QT.
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