Dataset Information

The challenge of detecting epistasis (G x G interactions): Genetic Analysis Workshop 16.

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.

SUBMITTER: An P

PROVIDER: S-EPMC3692280 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Publications

The challenge of detecting epistasis (G x G interactions): Genetic Analysis Workshop 16.

An Ping P Mukherjee Odity O Chanda Pritam P Yao Li L Engelman Corinne D CD Huang Chien-Hsun CH Zheng Tian T Kovac Ilija P IP Dubé Marie-Pierre MP Liang Xueying X Li Jia J de Andrade Mariza M Culverhouse Robert R Malzahn Doerthe D Manning Alisa K AK Clarke Geraldine M GM Jung Jeesun J Province Michael A MA

Genetic epidemiology 20090101

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 mod ...[more]

PMID: 19924703

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Project description:BACKGROUND: Epistasis is recognized fundamentally important for understanding the mechanism of disease-causing genetic variation. Though many novel methods for detecting epistasis have been proposed, few studies focus on their comparison. Undertaking a comprehensive comparison study is an urgent task and a pathway of the methods to real applications. RESULTS: This paper aims at a comparison study of epistasis detection methods through applying related software packages on datasets. For this purpose, we categorize methods according to their search strategies, and select five representative methods (TEAM, BOOST, SNPRuler, AntEpiSeeker and epiMODE) originating from different underlying techniques for comparison. The methods are tested on simulated datasets with different size, various epistasis models, and with/without noise. The types of noise include missing data, genotyping error and phenocopy. Performance is evaluated by detection power (three forms are introduced), robustness, sensitivity and computational complexity. CONCLUSIONS: None of selected methods is perfect in all scenarios and each has its own merits and limitations. In terms of detection power, AntEpiSeeker performs best on detecting epistasis displaying marginal effects (eME) and BOOST performs best on identifying epistasis displaying no marginal effects (eNME). In terms of robustness, AntEpiSeeker is robust to all types of noise on eME models, BOOST is robust to genotyping error and phenocopy on eNME models, and SNPRuler is robust to phenocopy on eME models and missing data on eNME models. In terms of sensitivity, AntEpiSeeker is the winner on eME models and both SNPRuler and BOOST perform well on eNME models. In terms of computational complexity, BOOST is the fastest among the methods. In terms of overall performance, AntEpiSeeker and BOOST are recommended as the efficient and effective methods. This comparison study may provide guidelines for applying the methods and further clues for epistasis detection.

Dataset Information

The challenge of detecting epistasis (G x G interactions): Genetic Analysis Workshop 16.

Publications

The challenge of detecting epistasis (G x G interactions): Genetic Analysis Workshop 16.

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