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.
Highlights
Population-based association studies including both genomewide mapping and fine mapping of complex disease genes have become increasingly popular as they offer a potentially more cost-effective and powerful approach than linkage analysis (Ardlie et al, 2002; Botstein and Risch, 2003)
Yoo et al (2007) showed by simulation that HaploIHP is better than PHASE (Stephens et al, 2001) and HAPLORE (Zhang et al, 2005) for killer cell immunoglobin-like receptor (KIR) data even when 60 and 25% of previously identified haplotypes were incorporated into the analysis
Many methods for haplotype inference have been developed and widely used by researchers. These methods can be directly applied to present-absent data (Liu et al, 2006), the large portion of missing data can greatly affect their accuracy for www.frontiersin.org haplotype inference
Summary
Population-based association studies including both genomewide mapping and fine mapping of complex disease genes have become increasingly popular as they offer a potentially more cost-effective and powerful approach than linkage analysis (Ardlie et al, 2002; Botstein and Risch, 2003). Haplotype based analysis can provide additional power in defining effects associated with multiple disease-related alleles within a single gene (Morris and Kaplan, 2002) or when a single marker test fails to capture local complexity of linkage disequilibrium (LD) between multiple markers (Akey et al, 2001). For some diseases such as hypertension, rare haplotypes have been shown to influence the disease susceptibility (Liu et al, 2005; Zhu et al, 2005; Kitsios and Zintzaras, 2010). Effective and accurate methods for haplotype inference in various situations are quite valuable
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
