AntEpiSeeker: detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm
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
BMC Research Notes 2010, 3:117 doi:10.1186/1756-0500-3-117Published: 28 April 2010
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.
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.
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.