Reconstructability analysis as a tool for identifying gene-gene interactions in studies of human diseases

Stephen Shervais, Patricia L. Kramer, Shawn K. Westaway, Nancy J. Cox, Martin Zwick

Research output: Contribution to journalArticle

8 Scopus citations

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.

Original languageEnglish (US)
Article number18
JournalStatistical Applications in Genetics and Molecular Biology
Volume9
Issue number1
DOIs
StatePublished - Apr 13 2010

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Keywords

  • Bioinformatics
  • Epistasis
  • Gene interaction modeling
  • Genetics
  • Information theory
  • OCCAM
  • Reconstructability analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Molecular Biology
  • Genetics
  • Computational Mathematics

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