title: An information-gain approach to detecting three-way epistatic interactions in genetic association studies.
abstract: BACKGROUND
  Epistasis has been historically used to describe the phenomenon that the effect of a given gene on a phenotype can be dependent on one or more other genes, and is an essential element for understanding the association between genetic and phenotypic variations. Quantifying epistasis of orders higher than two is very challenging due to both the computational complexity of enumerating all possible combinations in genome-wide data and the lack of efficient and effective methodologies.
  
  
  OBJECTIVE
  In this study, we propose a fast, non-parametric, and model-free measure for three-way epistasis.
  
  
  METHODS
  Such a measure is based on information gain, and is able to separate all lower order effects from pure three-way epistasis.
  
  
  RESULTS
  Our method was verified on synthetic data and applied to real data from a candidate-gene study of tuberculosis in a West African population. In the tuberculosis data, we found a statistically significant pure three-way epistatic interaction effect that was stronger than any lower-order associations.
  
  
  CONCLUSIONS
  Our study provides a methodological basis for detecting and characterizing high-order gene-gene interactions in genetic association studies.
authors: Computational Genetics Laboratory, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA.
authors: Ting
authors: T
authors: Hu
authors: Yuanzhu
authors: Y
authors: Chen
authors: Jeff W
authors: JW
authors: Kiralis
authors: Ryan L
authors: RL
authors: Collins
authors: Christian
authors: C
authors: Wejse
authors: Giorgio
authors: G
authors: Sirugo
authors: Scott M
authors: SM
authors: Williams
authors: Jason H
authors: JH
authors: Moore
mesh_terms: D019295:Computational Biology / Q000379:methods*; D004843:Epistasis, Genetic; D056726:Genetic Association Studies; D020022:Genetic Predisposition to Disease; D006801:Humans; D007257:Information Theory; D018709:Statistics, Nonparametric
keywords: epistasis; gene-gene interaction; genetic association studies; high-order interaction; information gain
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