The challenge of detecting epistasis (G x G interactions): Genetic analysis workshop 16

Ping An, 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

Research output: Contribution to journalArticle

14 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)S58-S67
JournalGenetic epidemiology
Volume33
Issue numberSUPPL. 1
DOIs
StatePublished - 2009

Keywords

  • Generalized linear model
  • Machine learning methods

ASJC Scopus subject areas

  • Epidemiology
  • Genetics(clinical)

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    An, P., Mukherjee, O., Chanda, P., Yao, L., Engelman, C. D., Huang, C. H., Zheng, T., Kovac, I. P., Dubé, M. P., Liang, X., Li, J., De Andrade, M., Culverhouse, R., Malzahn, D., Manning, A. K., Clarke, G. M., Jung, J., & Province, M. A. (2009). The challenge of detecting epistasis (G x G interactions): Genetic analysis workshop 16. Genetic epidemiology, 33(SUPPL. 1), S58-S67. https://doi.org/10.1002/gepi.20474