Haplotype Inference Constrained by Plausible Haplotype Data

Michael R. Fellows, Tzvika Hartman, Dan Hermelin, Gad M. Landau, Frances Rosamond, Liat Rozenberg

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


The haplotype inference problem (HIP) asks to find a set of haplotypes which resolve a given set of genotypes. This problem is of enormous importance in many practical fields, such as the investigation of diseases, or other types of genetic mutations. In order to find the haplotypes that are as close as possible to the real set of haplotypes that comprise the genotypes, two models have been suggested which by now have become widely accepted: The perfect phylogeny model and the pure parsimony model. All known algorithms up till now for the above problem may find haplotypes that are not necessarily plausible, i.e. very rare haplotypes or haplotypes that were never observed in the population. In order to overcome this disadvantage we study in this paper, for the first time, a new constrained version of HIP under the above mentioned models. In this new version, a pool of plausible haplotypes H̃is given together with the set of genotypes G, and the goal is to find a subset H ⊆ H̃ that resolves G. For the constrained perfect phylogeny haplotyping (CPPH) problem we provide initial insights and polynomial-time algorithms for some restricted cases that help understanding the complexity of that problem. We also prove that the constrained parsimony haplotyping (CPH) problem is fixed parameter tractable by providing a parameterized algorithm that applies an interesting dynamic programming technique for solving the problem.

Original languageEnglish GB
Title of host publicationCombinatorial Pattern Matching
Subtitle of host publication20th Annual Symposium, CPM 2009, Proceedings
EditorsGregory Kucherov, Esko Ukkonen
Number of pages14
ISBN (Print)9783642024405
StatePublished - 9 Nov 2009
Externally publishedYes
Event20th Annual Symposium on Combinatorial Pattern Matching, CPM 2009 - Lille, France
Duration: 22 Jun 200924 Jun 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
ISSN (Print)0302-9743


Conference20th Annual Symposium on Combinatorial Pattern Matching, CPM 2009

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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