title: Multiple ant colony algorithm method for selecting tag SNPs.
abstract: The search for the association between complex disease and single nucleotide polymorphisms (SNPs) or haplotypes has recently received great attention. Finding a set of tag SNPs for haplotyping in a great number of samples is an important step to reduce cost for association study. Therefore, it is essential to select tag SNPs with more efficient algorithms. In this paper, we model problem of selection tag SNPs by MINIMUM TEST SET and use multiple ant colony algorithm (MACA) to search a smaller set of tag SNPs for haplotyping. The various experimental results on various datasets show that the running time of our method is less than GTagger and MLR. And MACA can find the most representative SNPs for haplotyping, so that MACA is more stable and the number of tag SNPs is also smaller than other evolutionary methods (like GTagger and NSGA-II). Our software is available upon request to the corresponding author.
authors.affiliation: The College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China. dragonbw@163.com
authors.forename: Bo
authors.initials: B
authors.lastname: Liao
authors.forename: Xiong
authors.initials: X
authors.lastname: Li
authors.forename: Wen
authors.initials: W
authors.lastname: Zhu
authors.forename: Renfa
authors.initials: R
authors.lastname: Li
authors.forename: Shulin
authors.initials: S
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000818:Animals; D001000:Ants; D003198:Computer Simulation; D030541:Databases, Genetic; D023281:Genomics / Q000379:methods*; D060148:HapMap Project; D006239:Haplotypes; D006801:Humans; D008954:Models, Biological; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide