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Haplotype Inference Using a Genetic Algorithm  

Lee See-Young (한국외국어대학교 컴퓨터및정보통신공학부)
Han Hyun-Goo (한국외국어대학교 컴퓨터및정보통신공학부)
Kim Hee-Chul (한국외국어대학교 컴퓨터및정보통신공학부)
Publication Information
Journal of KIISE:Computer Systems and Theory / v.33, no.6, 2006 , pp. 316-325 More about this Journal
Abstract
In diploid organisms like human, each chromosome consists of two copies. A haplotype is a SNP(single nucleotide polymorphism) sequence information from each copy. Finding the complete map of haplotypes in human population is one of the important issues in human genome. To obtain haplotypes via experimental methods is both time-consuming and expensive. Therefore, inference methods have been used to infer haplotyes from the genotype samples. In this paper, we propose a new approach using genetic algorithm to infer haplotypes, which is based on the model of finding the minimum number of haplotypes that explain the genotype samples. We show that by doing a computational experiment, our algorithm has the correctness similar to HAPAR[1] which is known to produce good results while the execution time of our algorithm is less than that of HAPAR as the input size is increased. The experimental result is also compared with the result by the recent method PTG[2].
Keywords
Haplotype Inference; Genotype; SNP; Genetic Algorithm;
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