Published In

Statistical Applications in Genetics and Molecular Biology

Document Type

Article

Publication Date

3-2010

Subjects

Epistasis -- Genetics, Information theory, Bioinformatics, Genetic polymorphisms, Diseases -- Research, Genetic models

Abstract

There are a number of common human diseases for which the genetic component may include an 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 (RA) uses Shannon’s information theory to detect relationships between variables in categorical datasets. We applied RA to simulated data for five different models of gene-gene interaction, and 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 greater than or equal to 80%. We applied RA to a real dataset of type 2 non-insulin-dependent diabetes (NIDDM) cases and controls, and closely approximated the results of more conventional single SNP disease association studies. In addition, we replicated prior evidence for epistatic interactions between SNPs on chromosomes 2 and 15.

Description

Copyright 2010 The Berkeley Electronic Press. The final publication is available at www.degruyter.com. Reproduced here with author and publisher permission.

DOI: 10.2202/1544-6115.1516

DOI

10.2202/1544-6115.1516

Persistent Identifier

http://archives.pdx.edu/ds/psu/11061

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