Reconstructability Analysis As A Tool For Identifying

Gene-Gene Interactions In Studies Of Human Diseases


Stephen Shervais(1), Martin Zwick(2) and Patricia Kramer(3)

(1) College of Business and Public Administration, Eastern Washington University, Cheney WA

(2) Systems Science Ph.D. Program, Portland State University, Portland OR

(3) Department of Neurology, Oregon Health and Sciences University, Portland OR

[sshervais@ewu.edu] (1), [zwick@pdx.edu] (2), [kramer@ohsu.edu] (3)

 

Keywords: Epistasis, reconstructability analysis, information theory, gene-gene interaction, gene interaction modeling, Occam, genetics.


Abstract

There are a number of human diseases that are caused by the epistatic interaction of multiple genes. Detecting these interactions with standard statistical tools is difficult, because there may be an interaction effect, but minimal or no main effect. Reconstructability analysis uses Shannon's information theory to detect relationships between variables in categorical datasets. We apply reconstructability analysis to data generated by five different models of gene-gene interaction, with heritability levels from 0.053 to 0.008, using 200 controls and 200 cases. We find that even with heritability levels as low as 0.008, and with the inclusion of 50 non-associated genes in the dataset, we can identify the interacting gene pairs with an accuracy of 80% or better.


Entire Paper (PDF)

Discrete Multivariate Modeling page