Limits...
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 SE with the sample size of 50, 100, and 200 and the assumption of HWE under different haplotype frequency distributions. The x-axis represents the standard deviation of haplotype frequencies used in simulations. (A–C) represent the results when no incorrect haplotypes were included as identified haplotypes while (D–F) represent the results when some incorrect haplotypes were included as identified haplotypes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4129397&req=5

Figure 3: Average values of SE with the sample size of 50, 100, and 200 and the assumption of HWE under different haplotype frequency distributions. The x-axis represents the standard deviation of haplotype frequencies used in simulations. (A–C) represent the results when no incorrect haplotypes were included as identified haplotypes while (D–F) represent the results when some incorrect haplotypes were included as identified haplotypes.

Mentions: We assessed the performance of HaploHMM and HaploIHP with different sample size of 50, 100, and 200. Patterns of six measures from HaploHMM, HaploIHP, MaCH, and HAPLORE with the sample size of 50 and 200 were similar as those with the sample size of 100. Figure 3 shows the average SE values for measures with the sample size of 50, 100, and 200. It can be seen that HaploHMM outperformed HaploIHP and HaoloHMM and HaploIHP had much better performance than MaCH and HAPLORE in terms of SE. For both HaploHMM and HaploIHP, the effect of sample size is rather smaller, suggesting that increasing sample size from 50 to 200 does not significantly improve the accuracy for haplotype inference.


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 SE with the sample size of 50, 100, and 200 and the assumption of HWE under different haplotype frequency distributions. The x-axis represents the standard deviation of haplotype frequencies used in simulations. (A–C) represent the results when no incorrect haplotypes were included as identified haplotypes while (D–F) represent the results when some incorrect haplotypes were included as identified haplotypes.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Average values of SE with the sample size of 50, 100, and 200 and the assumption of HWE under different haplotype frequency distributions. The x-axis represents the standard deviation of haplotype frequencies used in simulations. (A–C) represent the results when no incorrect haplotypes were included as identified haplotypes while (D–F) represent the results when some incorrect haplotypes were included as identified haplotypes.
Mentions: We assessed the performance of HaploHMM and HaploIHP with different sample size of 50, 100, and 200. Patterns of six measures from HaploHMM, HaploIHP, MaCH, and HAPLORE with the sample size of 50 and 200 were similar as those with the sample size of 100. Figure 3 shows the average SE values for measures with the sample size of 50, 100, and 200. It can be seen that HaploHMM outperformed HaploIHP and HaoloHMM and HaploIHP had much better performance than MaCH and HAPLORE in terms of SE. For both HaploHMM and HaploIHP, the effect of sample size is rather smaller, suggesting that increasing sample size from 50 to 200 does not significantly improve the accuracy for haplotype inference.

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