| Literature DB >> 19924703 |
Ping An1, Odity Mukherjee, Pritam Chanda, Li Yao, Corinne D Engelman, Chien-Hsun Huang, Tian Zheng, Ilija P Kovac, Marie-Pierre Dubé, Xueying Liang, Jia Li, Mariza de Andrade, Robert Culverhouse, Doerthe Malzahn, Alisa K Manning, Geraldine M Clarke, Jeesun Jung, Michael A Province.
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. (c) 2009 Wiley-Liss, Inc.Entities:
Mesh:
Year: 2009 PMID: 19924703 PMCID: PMC3692280 DOI: 10.1002/gepi.20474
Source DB: PubMed Journal: Genet Epidemiol ISSN: 0741-0395 Impact factor: 2.135