A Hidden Markov Model for Haplotype Inference for Present-Absent Genotype Data Using Previously Identified Haplotype and Haplotype Patterns

• Activity Number: 588
• Type: Contributed
• Date/Time: Wednesday, August 3, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Jihua Wu and Guo-Bo Chen and Degui Zhi and Nianjun Liu and Kui Zhang*+
• Companies: University of Alabama at Birmingham and University of Alabama at Birmingham and University of Alabama at Birmingham and University of Alabama at Birmingham and University of Alabama at Birmingham
• Keywords: haplotype ; hidden markov model ; genotype

Abstract

Killer immunoglobulin-like receptor (KIR) genes vary considerably in their presence or absence on a specific regional haplotype. Because presence or absence of these genes is largely detected using locus-specific genotyping technology, the distinction between homozygosity and hemizygosity is often ambiguous. The performance of methods for haplotype inference for KIR genes may be compromised due to the large portion of ambiguous data. At the same time, many haplotypes or partial haplotype patterns have been previously identified and can be incorporated to facilitate haplotype inference for unphased genotype data. To accommodate the increased ambiguity of present-absent genotyping of KIR genes, we developed a hidden Markov model, which incorporated information about identified haplotypes or partial haplotype patterns and compared several measures on simulated KIR genotype in order to evaluate the reliability of haplotype assignments and the accuracy in estimating haplotype frequency. The simulation study shows that our method outperformed the two existing techniques (HAPLO-IHP and PHASE).