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An FPT haplotyping algorithm on pedigrees with a small number of sites.

Doan DD, Evans PA - Algorithms Mol Biol (2011)

Bottom Line: A computational method to infer haplotypes from genotype data is therefore important.We 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Computer Science, University of New Brunswick, Fredericton, New Brunswick, Canada. pevans@unb.ca.

ABSTRACT

Background: Genetic 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.

Results: We 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.

Conclusions: This 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.

No MeSH data available.


Related in: MedlinePlus

Compression step.
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Figure 4: Compression step.

Mentions: Consider a graph G in Figure 4a where ⊕ denotes a red vertex, ∅ a green vertex, and O a grey vertex. A minimal edge bipartization set X' of size 4 illustrated by dashed lines is given in Figure 4b. We compute a mincut Y for G\X' as in Figure 4c. Set Y is the edge bipartization set of size 3 for G in Figure 4d.


An FPT haplotyping algorithm on pedigrees with a small number of sites.

Doan DD, Evans PA - Algorithms Mol Biol (2011)

Compression step.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3108306&req=5

Figure 4: Compression step.
Mentions: Consider a graph G in Figure 4a where ⊕ denotes a red vertex, ∅ a green vertex, and O a grey vertex. A minimal edge bipartization set X' of size 4 illustrated by dashed lines is given in Figure 4b. We compute a mincut Y for G\X' as in Figure 4c. Set Y is the edge bipartization set of size 3 for G in Figure 4d.

Bottom Line: A computational method to infer haplotypes from genotype data is therefore important.We 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Computer Science, University of New Brunswick, Fredericton, New Brunswick, Canada. pevans@unb.ca.

ABSTRACT

Background: Genetic 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.

Results: We 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.

Conclusions: This 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.

No MeSH data available.


Related in: MedlinePlus