Abstract

BackgroundGenetic disease studies investigate relationships between changes in chromosomes and genetic diseases. Single haplotypes provide useful information for these studies but extracting single haplotypes directly by biochemical methods is expensive. A computational method to infer haplotypes from genotype data is therefore important. We investigate the problem of computing the minimum number of recombination events for general pedigrees with a small number of sites for all members.ResultsWe show that this NP-hard problem can be parametrically reduced to the Bipartization by Edge Removal problem with additional parity constraints. We solve this problem with an exact algorithm that runs in time, where n is the number of members, m is the number of sites, and k is the number of recombination events.ConclusionsThis algorithm infers haplotypes for a small number of sites, which can be useful for genetic disease studies to track down how changes in haplotypes such as recombinations relate to genetic disease.

Highlights

  • Genetic disease studies investigate relationships between changes in chromosomes and genetic diseases

  • We study the haplotype inference for general pedigrees with recombination events when the number of recombination events k and the number of sites m in an input pedigree are small

  • We further show that finding the line index of a signed graph can be reduced to the Graph Bipartization by Edge Removal (GBER) problem with parity constraints

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Summary

Introduction

Genetic disease studies investigate relationships between changes in chromosomes and genetic diseases. RHCopt: Given the genotypes of a general pedigree P containing n members, where each member has m sites (m is small), find a haplotype configuration that minimizes the number of recombination events.

Results
Conclusion
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