Linear Reduction for Haplotype Inference

  • Jingwu He
  • Alex Zelikovsky
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3240)


Haplotype inference problem asks for a set of haplotypes explaining a given set of genotypes. Popular software tools for haplotype inference (e.g., PHASE, HAPLOTYPER) as well as new algorithms recently proposed for perfect phylogeny inference (DPPH) are often not well scalable. When the number of sites (SNP’s) comes to thousands these tools often cannot deliver answer in reasonable time even if the number of haplotypes is small. In this paper we propose a new linear algebra based method which drastically reduces the number of sites in the original data. After solving a reduced instance, linear decoding allows to recover haplotypes of full length for given genotypes. Experiments show that our method significantly speeds up popular haplotype inference tools while finding almost the same solution practically in all cases thus not compromising the quality of the known haplotype inference methods.


Haplotype inference linear independence perfect phylogeny 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jingwu He
    • 1
  • Alex Zelikovsky
    • 1
  1. 1.Department of Computer ScienceGeorgia State UniversityAtlantaUSA

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