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A maximum likelihood based genetic algorithm for inferring haplotypes from genotypes

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5 Author(s)
Lakshminarasimhan, P. ; Dept. of Comput. Sci., East Stroudsburg Univ., East Stroudsburg, PA, USA ; Marmelstein, R. ; Devito, M. ; Dongsheng Che
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A haplotype is a set of single nucleotide polymorphisms (SNPs) from a given chromosome, and provides valuable information about complex diseases. Current practices that the inferring of large scale SNP haplotypes from raw SNP data (genotypes) using computational approaches has gained a lot of attention, but it presents a grand challenges as it is inherently a NP-Hard problem. In this paper, we propose a heuristic approach, Genetic Algorithm (GA) model for the haplotypes inference method, based on the maximum-likelihood estimates of haplotype frequencies under the assumption of Hardy-Weinberg proportions. The goal of the genetic algorithm method is to obtain high prediction accuracy within a reasonable computing time. The performance of our model was evaluated on both simulated datasets and real datasets, and these results are promising, indicating that our model is a potential computational tool for haplotype inferences.

Published in:

Education Technology and Computer (ICETC), 2010 2nd International Conference on  (Volume:5 )

Date of Conference:

22-24 June 2010