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

被引：103
作者：
Wang Y. [1 ,2 ]
Liu X. [1 ,2 ]
Robbins K. [1 ]
Rekaya R. [1 ,2 ,3 ]
机构：
[1] Department of Animal and Dairy Science, University of Georgia, Athens
[2] Institute of Bioinformatics, University of Georgia, Athens
[3] Department of Statistics, University of Georgia, Athens
来源：
BMC Research Notes | / 3卷 / 1期
关键词：
Epistatic Interaction; Large Scale Dataset; Pheromone Level; Multiple Genetic Variation; Bayesian Epistasis Association Mapping;
D O I：
10.1186/1756-0500-3-117
中图分类号：
学科分类号：
摘要：
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. © 2010 Rekaya et al; licensee BioMed Central Ltd.
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