Email updates

Keep up to date with the latest news and content from BMC Genomics and BioMed Central.

Open Access Highly Accessed Methodology article

Information-theoretic gene-gene and gene-environment interaction analysis of quantitative traits

Pritam Chanda1, Lara Sucheston23, Song Liu23, Aidong Zhang1 and Murali Ramanathan4*

Author Affiliations

1 Department of Computer Science and Engineering, State University of New York, Buffalo, NY, USA

2 Department of Biostatistics, State University of New York, Buffalo, NY

3 Division of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY, USA

4 Department of Pharmaceutical Sciences, State University of New York, Buffalo, NY, USA

For all author emails, please log on.

BMC Genomics 2009, 10:509  doi:10.1186/1471-2164-10-509

Published: 4 November 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.