title: Tag SNP selection via a genetic algorithm.
abstract: Single Nucleotide Polymorphisms (SNPs) provide valuable information on human evolutionary history and may lead us to identify genetic variants responsible for human complex diseases. Unfortunately, molecular haplotyping methods are costly, laborious, and time consuming; therefore, algorithms for constructing full haplotype patterns from small available data through computational methods, Tag SNP selection problem, are convenient and attractive. This problem is proved to be an NP-hard problem, so heuristic methods may be useful. In this paper we present a heuristic method based on genetic algorithm to find reasonable solution within acceptable time. The algorithm was tested on a variety of simulated and experimental data. In comparison with the exact algorithm, based on brute force approach, results show that our method can obtain optimal solutions in almost all cases and runs much faster than exact algorithm when the number of SNP sites is large. Our software is available upon request to the corresponding author.
authors.affiliation: Department of Bioinformatics, University of Tehran, Tehran, Iran. mahdevar@ibb.ut.ac.ir
authors.forename: Ghasem
authors.initials: G
authors.lastname: Mahdevar
authors.forename: Javad
authors.initials: J
authors.lastname: Zahiri
authors.forename: Mehdi
authors.initials: M
authors.lastname: Sadeghi
authors.forename: Abbas
authors.initials: A
authors.lastname: Nowzari-Dalini
authors.forename: Hayedeh
authors.initials: H
authors.lastname: Ahrabian
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004194:Disease / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000453:epidemiology / Q000235:genetics; D006239:Haplotypes; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D012984:Software