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The challenge of detecting epistasis (G×G Interactions): Genetic Analysis Workshop 16
Author(s) -
An Ping,
Mukherjee Odity,
Chanda Pritam,
Yao Li,
Engelman Corinne D.,
Huang ChienHsun,
Zheng Tian,
Kovac Ilija P.,
Dubé MariePierre,
Liang Xueying,
Li Jia,
de Andrade Mariza,
Culverhouse Robert,
Malzahn Doerthe,
Manning Alisa K.,
Clarke Geraldine M.,
Jung Jeesun,
Province Michael A.
Publication year - 2009
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.20474
Subject(s) - epistasis , penetrance , variety (cybernetics) , genome wide association study , computational biology , variance (accounting) , genetic association , biology , evolutionary biology , statistics , computer science , machine learning , econometrics , genetics , artificial intelligence , mathematics , genotype , gene , phenotype , single nucleotide polymorphism , accounting , business
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. Genet. Epidemiol . 33 (Suppl. 1):S58–S67, 2009. © 2009 Wiley‐Liss, Inc.