Performance analysis of novel methods for detecting epistasis

BMC Bioinformatics

Methodology Article

Junying Zhang¹  Junliang Shang¹  Dan Liu¹  Daojun Ye¹  Yaling Yin²  Yan Sun³ 

[1] School of Computer Science & Technology, Xidian University, 710071, Xi'an, China;School of Computer Science & Technology, Xidian University, 710071, Xi'an, China;Information School, Xi'an Economical and Financial University, 710100, Xi'an, China;Shannxi people's fine arts publishing house, 710003, Xi'an, China;

关键词: Epistatic Interaction;    Multifactor Dimensionality Reduction;    Detection Power;    Large Scale Dataset;    Strong Robustness;   

DOI  :  10.1186/1471-2105-12-475

 received in 2011-04-28, accepted in 2011-12-15,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundEpistasis 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.ResultsThis 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.ConclusionsNone 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.

【 授权许可】

CC BY   
© Shang et al; licensee BioMed Central Ltd. 2011