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

Wu J, Chen GB, Zhi D, Liu N, Zhang K - Front Genet (2014)

Bottom Line: 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.Our simulation results showed that the incorporation of identified haplotypes and partial haplotype patterns can improve accuracy for haplotype inference.

View Article: PubMed Central - PubMed

Affiliation: Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham Birmingham, AL, USA.

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.

No MeSH data available.


Related in: MedlinePlus

Average values of six measures (IH, SAD, SE, and IE) over 500 replicates with the sample size of 100 and the assumption of HWE under different haplotype frequency distributions. The x-axis represents the standard deviation of haplotype frequencies used in simulations. Results were obtained Results were obtained when there was no missing data and there were two incorrect haplotypes included as identified haplotypes.
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Figure 6: Average values of six measures (IH, SAD, SE, and IE) over 500 replicates with the sample size of 100 and the assumption of HWE under different haplotype frequency distributions. The x-axis represents the standard deviation of haplotype frequencies used in simulations. Results were obtained Results were obtained when there was no missing data and there were two incorrect haplotypes included as identified haplotypes.

Mentions: We investigated if the use of identified haplotypes and haplotype patterns can improve the accuracy for haplotype inference in the absence of missing data and presented the average values of four measures (IH, SAD, SE, and IE) in Figures 5, 6. In the absence of missing data, all methods had much better performance and the differences of the average values of four measures among four methods were much smaller than those of in the presence of missing data. HAPLORE had the best performance when the standard deviation of haplotype frequency was large while HAPLORE had the worst performance when the standard deviation of haplotype frequency was large. When only correct haplotypes were included as identified haplotypes, HaploIHP still had the best performance, followed by HaploHMM and MaCH. However, when some incorrect haplotypes were included as identified haplotypes, HaploHMM had the best performance across all haplotype frequency distributions and MaCH outperformed HaploIHP when the standard deviation of haplotype frequency was less than 0.10.


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

Wu J, Chen GB, Zhi D, Liu N, Zhang K - Front Genet (2014)

Average values of six measures (IH, SAD, SE, and IE) over 500 replicates with the sample size of 100 and the assumption of HWE under different haplotype frequency distributions. The x-axis represents the standard deviation of haplotype frequencies used in simulations. Results were obtained Results were obtained when there was no missing data and there were two incorrect haplotypes included as identified haplotypes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4129397&req=5

Figure 6: Average values of six measures (IH, SAD, SE, and IE) over 500 replicates with the sample size of 100 and the assumption of HWE under different haplotype frequency distributions. The x-axis represents the standard deviation of haplotype frequencies used in simulations. Results were obtained Results were obtained when there was no missing data and there were two incorrect haplotypes included as identified haplotypes.
Mentions: We investigated if the use of identified haplotypes and haplotype patterns can improve the accuracy for haplotype inference in the absence of missing data and presented the average values of four measures (IH, SAD, SE, and IE) in Figures 5, 6. In the absence of missing data, all methods had much better performance and the differences of the average values of four measures among four methods were much smaller than those of in the presence of missing data. HAPLORE had the best performance when the standard deviation of haplotype frequency was large while HAPLORE had the worst performance when the standard deviation of haplotype frequency was large. When only correct haplotypes were included as identified haplotypes, HaploIHP still had the best performance, followed by HaploHMM and MaCH. However, when some incorrect haplotypes were included as identified haplotypes, HaploHMM had the best performance across all haplotype frequency distributions and MaCH outperformed HaploIHP when the standard deviation of haplotype frequency was less than 0.10.

Bottom Line: 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.Our simulation results showed that the incorporation of identified haplotypes and partial haplotype patterns can improve accuracy for haplotype inference.

View Article: PubMed Central - PubMed

Affiliation: Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham Birmingham, AL, USA.

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.

No MeSH data available.


Related in: MedlinePlus