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Title: The challenge of detecting epistasis (G x G interactions): Genetic Analysis Workshop 16.
Authors: An P,  Mukherjee O,  Chanda P,  Yao L,  Engelman CD,  Huang CH,  Zheng T,  Kovac IP,  Dubé MP,  Liang X,  Li J,  de Andrade M,  Culverhouse R,  Malzahn D,  Manning AK,  Clarke GM,  Jung J,  Province MA
Journal: Genet Epidemiol
Date: 2009
Branches: GEB
PubMed ID: 19924703
PMC ID: PMC3692280
Abstract: 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.