%0 Journal Article
%T AntEpiSeeker: detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm
%A Yupeng Wang
%A Xinyu Liu
%A Kelly Robbins
%A Romdhane Rekaya
%J BMC Research Notes
%D 2010
%I BioMed Central
%R 10.1186/1756-0500-3-117
%X 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.Genetic association studies, which aim at detecting association between one or more genetic polymorphisms and a trait of interest such as a quantitative characteristic, discrete attribute or disease, have gained a lot of popularity in the past decade [1]. Although great progress in mapping genes responsible for Mendelian traits has been made, the genetic basis underlying many complex diseases remain unknown. It is widely accepted that these diseases may be caused by the joint effects of multiple genetic variations, which may show little effect individually but strong interactions. Such interactive effects of multiple genetic variations are often referred to as epistasis or epistatic interactions [2]. Recently, increasing numbers of studies have suggested the presence of epistatic interactions in complex diseases, e.g. breast cancer [3], type-2 diabetes [4] and atrial fibrillation [5].A number of multi-locus approaches have been proposed to detect epistatic interactions, such as the combinatorial partitioning method (CPM) [6], restricted partitioning method (RPM) [7], the multifactor-dimensionality reduction (MDR) [3], the focused interaction testing framework (FITF) [8] and the backward genotype-trait association (BGTA) [9]. Although these methods were tested and showed promising performance on small data sets, the computational burden prohibits their application on large scale datasets.Typically, a large scale dataset for association studies may have several tens to hundreds of thousands of markers. For example, the genome-wide case-control data set for Age-related Macular Degeneration
%U http://www.biomedcentral.com/1756-0500/3/117