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Analysis of Gene–Gene Interactions Underlying Human Disease

  1. Yalu Wen,
  2. Qing Lu

Published Online: 15 JAN 2014

DOI: 10.1002/9780470015902.a0022498



How to Cite

Wen, Y. and Lu, Q. 2014. Analysis of Gene–Gene Interactions Underlying Human Disease. eLS. .

Author Information

  1. Michigan State University, East Lansing, Michigan, USA

Publication History

  1. Published Online: 15 JAN 2014


Following the identification of disease-susceptibility variants in genome-wide association studies by using the standard single-locus analyses, the discovery process is shifting towards gene–gene interactions of functional importance in the pathophysiology and aetiology of complex diseases. The results from these gene–gene interaction analyses could lead to new genetic findings that account for the heritability of human diseases as well as novel insights about underlying genetic aetiology through later bench science research and clinical applications. To facilitate gene–gene interaction analyses, various statistical methods have been proposed, each of which is applicable for certain study designs and has its own advantages under certain conditions. In this article, the authors provide a survey of the statistical methods and software packages that are currently available for population-based and family-based gene–gene interaction studies. The strength of each method is discussed and the difficulties in determining the relationship between biological and statistical interactions are laid out.

Key Concepts:

  • A biological interaction describes a scenario in which two or more genes jointly affect a disease.

  • A statistical interaction describes the nonadditive effect in generalised linear models.

  • The heritability of a phenotype is defined as the proportion of phenotypic variations between individuals due to their genetic differences.

  • Population based case-control study recruits individuals with a disease of interest along with the unrelated healthy individuals, and compares the allele/genotype distributions between cases and controls to determine whether a statistical interaction exists.

  • Family based study design avoids the potential confounding effect due to population stratification and admixture by recruiting the parents and/or siblings of the cases.


  • epistasis;
  • study design;
  • population-based study;
  • family-based study;
  • regression-based methods;
  • data mining approaches;
  • high-dimensional data;
  • high-order interactions