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

doi:10.1002/gepi.20474

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

Quantitative Health Sciences

Research output: Contribution to journal › Article › peer-review

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 language	English (US)
Pages (from-to)	S58-S67
Journal	Genetic epidemiology
Volume	33
Issue number	SUPPL. 1
DOIs	https://doi.org/10.1002/gepi.20474
State	Published - 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

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 2009, 'The challenge of detecting epistasis (G x G interactions): Genetic analysis workshop 16', Genetic epidemiology, vol. 33, no. SUPPL. 1, pp. S58-S67. https://doi.org/10.1002/gepi.20474

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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.",

keywords = "Generalized linear model, Machine learning methods",

author = "Ping An and Odity Mukherjee and Pritam Chanda and Li Yao and Engelman, {Corinne D.} and Huang, {Chien Hsun} and Tian Zheng and Kovac, {Ilija P.} and Dub{\'e}, {Marie Pierre} and Xueying Liang and Jia Li and {De Andrade}, Mariza and Robert Culverhouse and Doerthe Malzahn and Manning, {Alisa K.} and Clarke, {Geraldine M.} and Jeesun Jung and Province, {Michael A.}",

year = "2009",

doi = "10.1002/gepi.20474",

language = "English (US)",

volume = "33",

pages = "S58--S67",

journal = "Genetic epidemiology",

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AU - Engelman, Corinne D.

AU - Huang, Chien Hsun

AU - Zheng, Tian

AU - Kovac, Ilija P.

AU - Dubé, Marie Pierre

AU - Liang, Xueying

AU - Li, Jia

AU - De Andrade, Mariza

AU - Culverhouse, Robert

AU - Malzahn, Doerthe

AU - Manning, Alisa K.

AU - Clarke, Geraldine M.

AU - Jung, Jeesun

AU - Province, Michael A.

PY - 2009

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N2 - 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.

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KW - Machine learning methods

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

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Keywords

ASJC Scopus subject areas

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