A hidden Markov model for haplotype inference for present-absent data of clustered genes using identified haplotypes and haplotype patterns
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 inf...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2014-08-01
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| Series: | Frontiers in Genetics |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00267/full |
| _version_ | 1819178744804278272 |
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| author | KUI eZHANG Jihua eWu Guo-bo eChen Degui eZhi Nianjun eLiu |
| author_facet | KUI eZHANG Jihua eWu Guo-bo eChen Degui eZhi Nianjun eLiu |
| author_sort | KUI eZHANG |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T21:47:25Z |
| format | Article |
| id | doaj.art-a3ab72ab3a384e4ca2646267ac243b77 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-22T21:47:25Z |
| publishDate | 2014-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-a3ab72ab3a384e4ca2646267ac243b772022-12-21T18:11:28ZengFrontiers Media S.A.Frontiers in Genetics1664-80212014-08-01510.3389/fgene.2014.0026797286A hidden Markov model for haplotype inference for present-absent data of clustered genes using identified haplotypes and haplotype patternsKUI eZHANG0Jihua eWu1Guo-bo eChen2Degui eZhi3Nianjun eLiu4University of Alabama at BirminghamUniversity of Alabama at BirminghamThe University of Queensland, Queensland Brain InstituteUniversity of Alabama at BirminghamUniversity of Alabama at BirminghamThe 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.http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00267/fullhaplotypeHidden markov modelhaplotype inferenceKIR genesHaplotype Patterns |
| spellingShingle | KUI eZHANG Jihua eWu Guo-bo eChen Degui eZhi Nianjun eLiu A hidden Markov model for haplotype inference for present-absent data of clustered genes using identified haplotypes and haplotype patterns Frontiers in Genetics haplotype Hidden markov model haplotype inference KIR genes Haplotype Patterns |
| title | A hidden Markov model for haplotype inference for present-absent data of clustered genes using identified haplotypes and haplotype patterns |
| title_full | A hidden Markov model for haplotype inference for present-absent data of clustered genes using identified haplotypes and haplotype patterns |
| title_fullStr | A hidden Markov model for haplotype inference for present-absent data of clustered genes using identified haplotypes and haplotype patterns |
| title_full_unstemmed | A hidden Markov model for haplotype inference for present-absent data of clustered genes using identified haplotypes and haplotype patterns |
| title_short | A hidden Markov model for haplotype inference for present-absent data of clustered genes using identified haplotypes and haplotype patterns |
| title_sort | hidden markov model for haplotype inference for present absent data of clustered genes using identified haplotypes and haplotype patterns |
| topic | haplotype Hidden markov model haplotype inference KIR genes Haplotype Patterns |
| url | http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00267/full |
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