The challenge of detecting epistasis (G x G interactions): Genetic Analysis Workshop 16

Genet Epidemiol. 2009;33 Suppl 1(0 1):S58-67. doi: 10.1002/gepi.20474.


Interest is increasing in epistasis as a possible source of the unexplained variance missed by genome-wide association studies. The Genetic Analysis Workshop 16 Group 9 participants evaluated a wide variety of classical and novel analytical methods for detecting epistasis, in both the statistical and machine learning paradigms, applied to both real and simulated data. Because the magnitude of epistasis is clearly relative to scale of penetrance, and therefore to some extent, to the choice of model framework, it is not surprising that strong interactions under one model might be minimized or even disappear entirely under a different modeling framework.

Publication types

  • Congress
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Alleles
  • Artificial Intelligence
  • Epistasis, Genetic*
  • Genome-Wide Association Study / methods*
  • Genome-Wide Association Study / statistics & numerical data
  • Humans
  • Linear Models
  • Models, Genetic
  • Molecular Epidemiology
  • Penetrance
  • Principal Component Analysis
  • Proportional Hazards Models
  • Statistics, Nonparametric