1. ¹Department of Computer Science and Engineering, State University of New York, Buffalo, NY, USA
2. ²Department of Biostatistics, State University of New York, Buffalo, NY, USA
3. ³Department of Pharmaceutical Sciences, State University of New York, Buffalo, NY, USA

Correspondence: Dr M Ramanathan, Department of Pharmaceutical Sciences, State University of New York, 427 Cooke Hall, Buffalo, NY 14260, USA. Tel: +1 716 645 2842 ext. 242; Fax: +1 716 645 3693; E-mail: murali@buffalo.edu

Received 30 October 2008; Revised 22 January 2009; Accepted 12 February 2009; Published online 18 March 2009.

Abstract

We developed an information-theoretic metric called the Interaction Index for prioritizing genetic variations and environmental variables for follow-up in detailed sequencing studies. The Interaction Index was found to be effective for prioritizing the genetic and environmental variables involved in GEI for a diverse range of simulated data sets. The metric was also evaluated for a 103-SNP Crohn's disease dataset and a simulated data set containing 9187 SNPs and multiple covariates that was modeled on a rheumatoid arthritis data set. Our results demonstrate that the Interaction Index algorithm is effective and efficient for prioritizing interacting variables for a diverse range of epidemiologic data sets containing complex combinations of direct effects, multiple GGI and GEI.