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Open Access Technical Note

AntEpiSeeker: detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm

Yupeng Wang12, Xinyu Liu12, Kelly Robbins1 and Romdhane Rekaya123*

Author Affiliations

1 Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602, USA

2 Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA

3 Department of Statistics, University of Georgia, Athens, GA 30602, USA

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BMC Research Notes 2010, 3:117  doi:10.1186/1756-0500-3-117

Published: 28 April 2010

Abstract

Background

Epistatic interactions of multiple single nucleotide polymorphisms (SNPs) are now believed to affect individual susceptibility to common diseases. The detection of such interactions, however, is a challenging task in large scale association studies. Ant colony optimization (ACO) algorithms have been shown to be useful in detecting epistatic interactions.

Findings

AntEpiSeeker, a new two-stage ant colony optimization algorithm, has been developed for detecting epistasis in a case-control design. Based on some practical epistatic models, AntEpiSeeker has performed very well.

Conclusions

AntEpiSeeker is a powerful and efficient tool for large-scale association studies and can be downloaded from http://nce.ads.uga.edu/~romdhane/AntEpiSeeker/index.html webcite.