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

doi:10.1186/1471-2164-10-509

BMC Genomics

Volume 10

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Sucheston L
Liu S
Zhang A
Ramanathan M

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Methodology article

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

Pritam Chanda¹ , Lara Sucheston²^,3 , Song Liu²^,3 , Aidong Zhang¹ and Murali Ramanathan⁴

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

author email corresponding author email

BMC Genomics 2009, 10:509doi: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.