The interaction index, a novel information-theoretic metric for prioritizing interacting genetic variations and environmental factors.
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.
Chanda
Pritam
P
Department of Computer Science and Engineering, State University of New York, Buffalo, NY, USA.
Sucheston
Lara
L
Zhang
Aidong
A
Ramanathan
Murali
M
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004777:Environment; D004813:Epidemiology; D014644:Genetic Variation; D005838:Genotype; D006801:Humans; D018895:Microsatellite Repeats; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide