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This article is part of the supplement: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2010

Open Access Proceedings

bNEAT: a Bayesian network method for detecting epistatic interactions in genome-wide association studies

Bing Han and Xue-wen Chen*

Author affiliations

Bioinformatics and Computational Life Sciences Laboratory, ITTC, Department of Electrical Engineering and Computer Science, The University of Kansas, 1520 West 15th Street, Lawrence, KS 66045, USA

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Citation and License

BMC Genomics 2011, 12(Suppl 2):S9  doi:10.1186/1471-2164-12-S2-S9

Published: 27 July 2011



Detecting epistatic interactions plays a significant role in improving pathogenesis, prevention, diagnosis and treatment of complex human diseases. A recent study in automatic detection of epistatic interactions shows that Markov Blanket-based methods are capable of finding genetic variants strongly associated with common diseases and reducing false positives when the number of instances is large. Unfortunately, a typical dataset from genome-wide association studies consists of very limited number of examples, where current methods including Markov Blanket-based method may perform poorly.


To address small sample problems, we propose a Bayesian network-based approach (bNEAT) to detect epistatic interactions. The proposed method also employs a Branch-and-Bound technique for learning. We apply the proposed method to simulated datasets based on four disease models and a real dataset. Experimental results show that our method outperforms Markov Blanket-based methods and other commonly-used methods, especially when the number of samples is small.


Our results show bNEAT can obtain a strong power regardless of the number of samples and is especially suitable for detecting epistatic interactions with slight or no marginal effects. The merits of the proposed approach lie in two aspects: a suitable score for Bayesian network structure learning that can reflect higher-order epistatic interactions and a heuristic Bayesian network structure learning method.