Reconstructability Analysis as a Tool for Identifying Gene-Gene Interactions in Studies of Human Diseases

Shervais Stephen, Kramer Patricia L, Westaway Shawn K., Cox Nancy J and Zwick Martin
Additional contact information
Shervais Stephen: Eastern Washington University
Kramer Patricia L: Oregon Health & Science University
Westaway Shawn K.: Oregon Health & Science University
Cox Nancy J: University of Chicago
Zwick Martin: Portland State University

Statistical Applications in Genetics and Molecular Biology, 2010, vol. 9, issue 1, 27

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 ?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.

Keywords: epistasis; reconstructability analysis; information theory; gene interaction modeling; OCCAM; genetics; bioinformatics (search for similar items in EconPapers)
Date: 2010
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2202/1544-6115.1516 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:9:y:2010:i:1:n:18

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.2202/1544-6115.1516

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().