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A maximum likelihood based genetic algorithm for inferring haplotypes from genotypes

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5 Author(s)
Priyadarshini Lakshminarasimhan ; Department of Computer Science, East Stroudsburg University, PA 18301, USA ; Robert Marmelstein ; Mary Devito ; 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:

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

Date of Conference:

22-24 June 2010