A hidden markov model for haplotype inference for present-absent data of clustered genes using identified haplotypes and haplotype patterns

Wu, Jihua, Chen, Guo-Bo, Zhi, Degui, Liu, Nianjun and Zhang, Kui (2014) A hidden markov model for haplotype inference for present-absent data of clustered genes using identified haplotypes and haplotype patterns. Frontiers in Genetics, 5 AUG: . doi:10.3389/fgene.2014.00267

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Author Wu, Jihua
Chen, Guo-Bo
Zhi, Degui
Liu, Nianjun
Zhang, Kui
Title A hidden markov model for haplotype inference for present-absent data of clustered genes using identified haplotypes and haplotype patterns
Journal name Frontiers in Genetics   Check publisher's open access policy
ISSN 1664-8021
Publication date 2014-08
Sub-type Article (original research)
DOI 10.3389/fgene.2014.00267
Open Access Status DOI
Volume 5
Issue AUG
Total pages 11
Place of publication Lausanne, Switzerland
Publisher Frontiers Research Foundation
Collection year 2015
Subject 1311 Genetics
1313 Molecular Medicine
2716 Genetics (clinical)
Abstract The majority of killer cell immunoglobin-like receptor (KIR) genes are detected as either present or absent using locus-specific genotyping technology. Ambiguity arises from the presence of a specific KIR gene since the exact copy number (one or two) of that gene is unknown. Therefore, haplotype inference for these genes is becoming more challenging due to such large portion of missing information. Meantime, many haplotypes and partial haplotype patterns have been previously identified due to tight linkage disequilibrium (LD) among these clustered genes thus can be incorporated to facilitate haplotype inference. In this paper, we developed a hidden Markov model (HMM) based method that can incorporate identified haplotypes or partial haplotype patterns for haplotype inference from present-absent data of clustered genes (e.g., KIR genes). We compared its performance with an expectation maximization (EM) based method previously developed in terms of haplotype assignments and haplotype frequency estimation through extensive simulations for KIR genes. The simulation results showed that the new HMM based method outperformed the previous method when some incorrect haplotypes were included as identified haplotypes and/or the standard deviation of haplotype frequencies were small. We also compared the performance of our method with two methods that do not use previously identified haplotypes and haplotype patterns, including an EM based method, HPALORE, and a HMM based method, MaCH. Our simulation results showed that the incorporation of identified haplotypes and partial haplotype patterns can improve accuracy for haplotype inference. The new software package HaploHMM is available and can be downloaded at http://www.soph.uab.edu/ssg/files/People/KZhang/HaploHMM/haplohmm-index.html.
Keyword Haplotype
Haplotype inference
Haplotype patterns
Hidden markov model
KIR genes
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2015 Collection
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