Abstract
The identification of haplotypes, which encode SNPs in a single chromosome, makes it possible to perform a haplotype-based association test with diseases. Given a set of genotypes from a population, the process of recovering the haplotypes that explain the genotypes is called haplotype inference. We propose a new preprocessing algorithm for the haplotype inference by pure parsimony (HIPP). The proposed algorithm excludes a large amount of redundant candidate haplotypes by detecting some groups of haplotypes that are dispensable for optimal solutions. For the well-known synthetic and biological data, the experimental results of our method show that our method run much faster than other preprocessing methods. After applying our preprocessing results, the numbers of haplotypes of HIPP solvers are equal to or slightly larger than that of optimal solutions.
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