A Deterministic Method for Haplotype Inference

Haplotypes are widely used in the analysis of relationship between genetics and diseases. Due to the cost of obtaining exact haplotype pairs, genotypes which contain the un-phased information corresponding to the haplotype pairs in the test subjects are used. Various haplotype inference algorithms h...

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Bibliographic Details
Published in2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers pp. 49 - 53
Main Authors Kuo-ching Liang, Xiaodong Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2007
Subjects
Diseases
Drugs
Genetics
Humans
Inference algorithms
Large-scale systems
Monte Carlo methods
Organisms
Partitioning algorithms
Sampling methods
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ISBN9781424421091
1424421098
ISSN1058-6393
DOI10.1109/ACSSC.2007.4487162

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Summary:Haplotypes are widely used in the analysis of relationship between genetics and diseases. Due to the cost of obtaining exact haplotype pairs, genotypes which contain the un-phased information corresponding to the haplotype pairs in the test subjects are used. Various haplotype inference algorithms have been proposed to resolve the un-phased information. In this paper, we propose a deterministic sequential Monte Carlo (DSMC)-based haplotype inference algorithm which allows for large datasets in terms of number of single nucleotide polymorphisms (SNP) and number of subjects, while providing similar or better performance for datasets under various conditions.
ISBN:9781424421091
1424421098
ISSN:1058-6393
DOI:10.1109/ACSSC.2007.4487162