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Member Research & Reports

Member Research & Reports

UAB Develops and Tests a Hidden Markov Model for Haplotype Inference

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 a recent study, Dr. Kui Zhang, associate professor in the department of biostatistics, section on statistical genetics at the University of Alabama at Birmingham, 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). Co-investigators include department colleagues Dr. Nianjun Liu, associate professor; Dr. Jihua Wu, postdoctoral fellow; and Dr. Degui Zhi, assistant professor.

The researchers 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. The team also compared the performance of their 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.

Dr. Zhang and his colleagues concluded that the simulation results showed that the incorporation of identified haplotypes and partial haplotype patterns can improve accuracy for haplotype inference.

“A Hidden Markov Model for Haplotype Inference for Present-Absent Data of Clustered Genes Using Identified Haplotypes and Haplotype Patterns” was published in July in the journal Frontiers in Science.

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