Genetic association mapping via evolution-based clustering of haplotypes

Multilocus analysis of single nucleotide polymorphism haplotypes is a promising approach to dissecting the genetic basis of complex diseases. We propose a coalescent-based model for association mapping that potentially increases the power to detect disease-susceptibility variants in genetic association studies. The approach uses Bayesian partition modelling to cluster haplotypes with similar disease risks by exploiting evolutionary information. We focus on candidate gene regions with densely spaced markers and model chromosomal segments in high linkage disequilibrium therein assuming a perfect phylogeny. To make this assumption more realistic, we split the chromosomal region of interest into sub-regions or windows of high linkage disequilibrium. The haplotype space is then partitioned into disjoint clusters, within which the phenotype–haplotype association is assumed to be the same. For example, in case-control studies, we expect chromosomal segments bearing the causal variant on a common ancestral background to be more frequent among cases than controls, giving rise to two separate haplotype clusters. The novelty of our approach arises from the fact that the distance used for clustering haplotypes has an evolutionary interpretation, as haplotypes are clustered according to the time to their most recent common ancestor. Our approach is fully Bayesian and we develop a Markov Chain Monte Carlo algorithm to sample efficiently over the space of possible partitions. We compare the proposed approach to both single-marker analyses and recently proposed multi-marker methods and show that the Bayesian partition modelling performs similarly in localizing the causal allele while yielding lower false-positive rates. Also, the method is computationally quicker than other multi-marker approaches. We present an application to real genotype data from the CYP2D6 gene region, which has a confirmed role in drug metabolism, where we succeed in mapping the location of the susceptibility variant within a small error.     

Bayesian logistic regression using a perfect phylogeny

Haplotype data capture the genetic variation among individuals in a population and among populations. An understanding of this variation and the ancestral history of haplotypes is important in genetic association studies of complex disease. We introduce a method for detecting associations between disease and haplotypes in a candidate gene region or candidate block with little or no recombination. A perfect phylogeny demonstrates the evolutionary relationship between single-nucleotide polymorphisms (SNPs) in the haplotype blocks. Our approach extends the logic regression technique of Ruczinski and others (2003) to a Bayesian framework, and constrains the model space to that of a perfect phylogeny. Environmental factors, as well as their interactions with SNPs, may be incorporated into the regression framework. We demonstrate our method on simulated data from a coalescent model, as well as data from a candidate gene study of sarcoidosis.     

Haplotypes at ATM identify coding-sequence variation and indicate a region of extensive linkage disequilibrium

Genetic variation in the human population may lead to functional variants of genes that contribute to risk for common chronic diseases such as cancer. In an effort to detect such possible predisposing variants, we constructed haplotypes for a candidate gene and tested their efficacy in association studies. We developed haplotypes consisting of 14 biallelic neutral-sequence variants that span 142 kb of the ATM locus. ATM is the gene responsible for the autosomal recessive disease ataxia-telangiectasia (AT). These ATM noncoding single-nucleotide polymorphisms (SNPs) were genotyped in nine CEPH families (89 individuals) and in 260 DNA samples from four different ethnic origins. Analysis of these data with an expectation-maximization algorithm revealed 22 haplotypes at this locus, with three major haplotypes having frequencies > or = .10. Tests for recombination and linkage disequilibrium (LD) show reduced recombination and extensive LD at the ATM locus, in all four ethnic groups studied. The most striking example was found in the study population of European ancestry, in which no evidence for recombination could be discerned. The potential of ATM haplotypes for detection of genetic variants through association studies was tested by analysis of 84 individuals carrying one of three ATM coding SNPs. Each coding SNP was detected by association with an ATM haplotype. We demonstrate that association studies with haplotypes for candidate genes have significant potential for the detection of genetic backgrounds that contribute to disease.     

Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium

Common genetic polymorphisms may explain a portion of the heritable risk for common diseases. Within candidate genes, the number of common polymorphisms is finite, but direct assay of all existing common polymorphism is inefficient, because genotypes at many of these sites are strongly correlated. Thus, it is not necessary to assay all common variants if the patterns of allelic association between common variants can be described. We have developed an algorithm to select the maximally informative set of common single-nucleotide polymorphisms (tagSNPs) to assay in candidate-gene association studies, such that all known common polymorphisms either are directly assayed or exceed a threshold level of association with a tagSNP. The algorithm is based on the r(2) linkage disequilibrium (LD) statistic, because r(2) is directly related to statistical power to detect disease associations with unassayed sites. We show that, at a relatively stringent r(2) threshold (r2>0.8), the LD-selected tagSNPs resolve >80% of all haplotypes across a set of 100 candidate genes, regardless of recombination, and tag specific haplotypes and clades of related haplotypes in nonrecombinant regions. Thus, if the patterns of common variation are described for a candidate gene, analysis of the tagSNP set can comprehensively interrogate for main effects from common functional variation. We demonstrate that, although common variation tends to be shared between populations, tagSNPs should be selected separately for populations with different ancestries.     

HapScope: a software system for automated and visual analysis of functionally annotated haplotypes

We have developed a software analysis package, HapScope, which includes a comprehensive analysis pipeline and a sophisticated visualization tool for analyzing functionally annotated haplotypes. The HapScope analysis pipeline supports: (i) computational haplotype construction with an expectation-maximization or Bayesian statistical algorithm; (ii) SNP classification by protein coding change, homology to model organisms or putative regulatory regions; and (iii) minimum SNP subset selection by either a Brute Force Algorithm or a Greedy Partition Algorithm. The HapScope viewer displays genomic structure with haplotype information in an integrated environment, providing eight alternative views for assessing genetic and functional correlation. It has a user-friendly interface for: (i) haplotype block visualization; (ii) SNP subset selection; (iii) haplotype consolidation with subset SNP markers; (iv) incorporation of both experimentally determined haplotypes and computational results; and (v) data export for additional analysis. Comparison of haplotypes constructed by the statistical algorithms with those determined experimentally shows variation in haplotype prediction accuracies in genomic regions with different levels of nucleotide diversity. We have applied HapScope in analyzing haplotypes for candidate genes and genomic regions with extensive SNP and genotype data. We envision that the systematic approach of integrating functional genomic analysis with population haplotypes, supported by HapScope, will greatly facilitate current genetic disease research.     

Dissection of a Complex Disease Susceptibility Region Using a Bayesian Stochastic Search Approach to Fine Mapping

Identification of candidate causal variants in regions associated with risk of common diseases is complicated by linkage disequilibrium (LD) and multiple association signals. Nonetheless, accurate maps of these variants are needed, both to fully exploit detailed cell specific chromatin annotation data to highlight disease causal mechanisms and cells, and for design of the functional studies that will ultimately be required to confirm causal mechanisms. We adapted a Bayesian evolutionary stochastic search algorithm to the fine mapping problem, and demonstrated its improved performance over conventional stepwise and regularised regression through simulation studies. We then applied it to fine map the established multiple sclerosis (MS) and type 1 diabetes (T1D) associations in the IL-2RA (CD25) gene region. For T1D, both stepwise and stochastic search approaches identified four T1D association signals, with the major effect tagged by the single nucleotide polymorphism, rs12722496. In contrast, for MS, the stochastic search found two distinct competing models: a single candidate causal variant, tagged by rs2104286 and reported previously using stepwise analysis; and a more complex model with two association signals, one of which was tagged by the major T1D associated rs12722496 and the other by rs56382813. There is low to moderate LD between rs2104286 and both rs12722496 and rs56382813 (r² ≃ 0:3) and our two SNP model could not be recovered through a forward stepwise search after conditioning on rs2104286. Both signals in the two variant model for MS affect CD25 expression on distinct subpopulations of CD4⁺ T cells, which are key cells in the autoimmune process. The results support a shared causal variant for T1D and MS. Our study illustrates the benefit of using a purposely designed model search strategy for fine mapping and the advantage of combining disease and protein expression data.     

Using an uncertainty-coding matrix in Bayesian regression models for haplotype-specific risk detection in family association studies

Haplotype association studies based on family genotype data can provide more biological information than single marker association studies. Difficulties arise, however, in the inference of haplotype phase determination and in haplotype transmission/non-transmission status. Incorporation of the uncertainty associated with haplotype inference into regression models requires special care. This task can get even more complicated when the genetic region contains a large number of haplotypes. To avoid the curse of dimensionality, we employ a clustering algorithm based on the evolutionary relationship among haplotypes and retain for regression analysis only the ancestral core haplotypes identified by it. To integrate the three sources of variation, phase ambiguity, transmission status and ancestral uncertainty, we propose an uncertainty-coding matrix which combines these three types of variability simultaneously. Next we evaluate haplotype risk with the use of such a matrix in a Bayesian conditional logistic regression model. Simulation studies and one application, a schizophrenia multiplex family study, are presented and the results are compared with those from other family based analysis tools such as FBAT. Our proposed method (Bayesian regression using uncertainty-coding matrix, BRUCM) is shown to perform better and the implementation in R is freely available.     

Inference on haplotype effects in case-control studies using unphased genotype data

A variety of statistical methods exist for detecting haplotype-disease association through use of genetic data from a case-control study. Since such data often consist of unphased genotypes (resulting in haplotype ambiguity), such statistical methods typically apply the expectation-maximization (EM) algorithm for inference. However, the majority of these methods fail to perform inference on the effect of particular haplotypes or haplotype features on disease risk. Since such inference is valuable, we develop a retrospective likelihood for estimating and testing the effects of specific features of single-nucleotide polymorphism (SNP)-based haplotypes on disease risk using unphased genotype data from a case-control study. Our proposed method has a flexible structure that allows, among other choices, modeling of multiplicative, dominant, and recessive effects of specific haplotype features on disease risk. In addition, our method relaxes the requirement of Hardy-Weinberg equilibrium of haplotype frequencies in case subjects, which is typically required of EM-based haplotype methods. Also, our method easily accommodates missing SNP information. Finally, our method allows for asymptotic, permutation-based, or bootstrap inference. We apply our method to case-control SNP genotype data from the Finland-United States Investigation of Non-Insulin-Dependent Diabetes Mellitus (FUSION) Genetics study and identify two haplotypes that appear to be significantly associated with type 2 diabetes. Using the FUSION data, we assess the accuracy of asymptotic P values by comparing them with P values obtained from a permutation procedure. We also assess the accuracy of asymptotic confidence intervals for relative-risk parameters for haplotype effects, by a simulation study based on the FUSION data.     

Joint modeling of linkage and association: identifying SNPs responsible for a linkage signal

Once genetic linkage has been identified for a complex disease, the next step is often association analysis, in which single-nucleotide polymorphisms (SNPs) within the linkage region are genotyped and tested for association with the disease. If a SNP shows evidence of association, it is useful to know whether the linkage result can be explained, in part or in full, by the candidate SNP. We propose a novel approach that quantifies the degree of linkage disequilibrium (LD) between the candidate SNP and the putative disease locus through joint modeling of linkage and association. We describe a simple likelihood of the marker data conditional on the trait data for a sample of affected sib pairs, with disease penetrances and disease-SNP haplotype frequencies as parameters. We estimate model parameters by maximum likelihood and propose two likelihood-ratio tests to characterize the relationship of the candidate SNP and the disease locus. The first test assesses whether the candidate SNP and the disease locus are in linkage equilibrium so that the SNP plays no causal role in the linkage signal. The second test assesses whether the candidate SNP and the disease locus are in complete LD so that the SNP or a marker in complete LD with it may account fully for the linkage signal. Our method also yields a genetic model that includes parameter estimates for disease-SNP haplotype frequencies and the degree of disease-SNP LD. Our method provides a new tool for detecting linkage and association and can be extended to study designs that include unaffected family members.     

Bayesian Detection of Causal Rare Variants under Posterior Consistency

Identification of causal rare variants that are associated with complex traits poses a central challenge on genome-wide association studies. However, most current research focuses only on testing the global association whether the rare variants in a given genomic region are collectively associated with the trait. Although some recent work, e.g., the Bayesian risk index method, have tried to address this problem, it is unclear whether the causal rare variants can be consistently identified by them in the small--large- situation. We develop a new Bayesian method, the so-called Bayesian Rare Variant Detector (BRVD), to tackle this problem. The new method simultaneously addresses two issues: (i) (Global association test) Are there any of the variants associated with the disease, and (ii) (Causal variant detection) Which variants, if any, are driving the association. The BRVD ensures the causal rare variants to be consistently identified in the small--large- situation by imposing some appropriate prior distributions on the model and model specific parameters. The numerical results indicate that the BRVD is more powerful for testing the global association than the existing methods, such as the combined multivariate and collapsing test, weighted sum statistic test, RARECOVER, sequence kernel association test, and Bayesian risk index, and also more powerful for identification of causal rare variants than the Bayesian risk index method. The BRVD has also been successfully applied to the Early-Onset Myocardial Infarction (EOMI) Exome Sequence Data. It identified a few causal rare variants that have been verified in the literature.     

Host genetics and Chlamydia disease: prediction and validation of disease severity mechanisms

Genetic mapping studies may provide association between sequence variants and disease susceptibility that can, with further experimental and computational analysis, lead to discovery of causal mechanisms and effective intervention. We have previously demonstrated that polymorphisms in immunity-related GTPases (IRG) confer a significant difference in susceptibility to Chlamydia psittaci infection in BXD recombinant mice. Here we combine genetic mapping and network modeling to identify causal pathways underlying this association. We infected a large panel of BXD strains with C. psittaci and assessed host genotype, IRG protein polymorphisms, pathogen load, expression of 32 cytokines, inflammatory cell populations, and weight change. Proinflammatory cytokines correlated with each other and were controlled by a novel genetic locus on chromosome 1, but did not affect disease status, as quantified by weight change 6 days after infection In contrast, weight change correlated strongly with levels of inflammatory cell populations and pathogen load that were controlled by an IRG encoding genetic locus (Ctrq3) on chromosome 11. These data provided content to generate a predictive model of infection using a Bayesian framework incorporating genotypes, immune system parameters, and weight change as a measure of disease severity. Two predictions derived from the model were tested and confirmed in a second round of experiments. First, strains with the susceptible IRG haplotype lost weight as a function of pathogen load whereas strains with the resistant haplotype were almost completely unaffected over a very wide range of pathogen load. Second, we predicted that macrophage activation by Ctrq3 would be central in conferring pathogen tolerance. We demonstrated that macrophage depletion in strains with the resistant haplotype led to neutrophil influx and greater weight loss despite a lower pathogen burden. Our results show that genetic mapping and network modeling can be combined to identify causal pathways underlying chlamydial disease susceptibility.     

Evolutionary signatures of common human cis-regulatory haplotypes

Variation in gene expression may give rise to a significant fraction of inter-individual phenotypic variation. Studies searching for the underlying genetic controls for such variation have been conducted in model organisms and humans in recent years. In our previous effort of assessing conserved underlying haplotype patterns across ethnic populations, we constructed common haplotypes using SNPs having conserved linkage disequilibrium (LD) across ethnic populations. These common haplotypes cluster into a simple evolutionary structure based on their frequencies, defining only up to three conserved clusters termed 'haplotype frameworks'. One intriguing preliminary finding was that a significant portion of reported variants strongly associated with cis-regulation tags these globally conserved haplotype frameworks. Here we expand the investigation by collecting genes showing stringently determined cis-association between genotypes and expression phenotypes from major studies. We conducted phylogenetic analysis of current major haplotypes along with the corresponding haplotypes derived from chimpanzee reference sequences. Our analysis reveals that, for the vast majority of such cis-regulatory genes, the tagging SNPs showing the strongest association also tag the haplotype lineages directly separated from ancestry, inferred from either chimpanzee reference sequences or the allele frequency-derived haplotype frameworks, suggesting that the differentially expressed phenotypes were evolved relatively early in human history. Such evolutionary signatures provide keys for a more effective identification of globally-conserved candidate regulatory haplotypes across human genes in future epidemiologic and pharmacogenetic studies.     

Sequencing complex diseases With HapMap

Determining the patterns of DNA sequence variation in the human genome is a useful first step toward identifying the genetic basis of a common disease. A haplotype map (HapMap), aimed at describing these variation patterns across the entire genome, has been recently developed by the International HapMap Consortium. In this article, we present a novel statistical model for directly characterizing specific sequence variants that are responsible for disease risk based on the haplotype structure provided by HapMap. Our model is developed in the maximum-likelihood context, implemented with the EM algorithm. We perform simulation studies to investigate the statistical properties of this disease-sequencing model. A worked example from a human obesity study with 155 patients was used to validate this model. In this example, we found that patients carrying a haplotype constituted by allele Gly16 at codon 16 and allele Gln27 at codon 27 genotyped within the beta2AR candidate gene display significantly lower body mass index than patients carrying the other haplotypes. The implications and extensions of our model are discussed.     

Population variation in linkage disequilibrium across the COMT gene considering promoter region and coding region variation

Catechol-O-methyl transferase (COMT) catalyzes the first step in one of the major pathways in the degradation of catecholamines. The COMT gene on chromosome 22 has been considered a candidate gene for many neuropsychiatric disorders, in part because an exon 4 single nucleotide polymorphism (SNP) in COMT causes an amino acid substitution associated with significantly altered enzyme activity. This functional variant, detected as an NlaIII restriction site polymorphism (RSP), is polymorphic in populations from around the world. A four-site haplotype spanning 28 kb effectively encompasses COMT. This haplotype is comprised of two novel polymorphisms [a tetranucleotide short tandem repeat polymorphism (STRP) in intron 1 and a HindIII RSP at the 5' end of COMT], the NlaIII site, and another previously published site - a BglI RSP at the 3' end of the gene. Overall linkage disequilibrium (LD) for this haplotype is strong and significant in 32 population samples from around the world. Conditional probabilities indicate that, in spite of moderate to strong disequilibrium in most non-African populations, the NlaIII site, although often used for prediction, would not always be a reliable predictor of allelic variation at the other sites. Because other functional variation might exist, especially regulatory variation, these findings indicate that haplotypes would be more effective indicators of possible involvement of COMT in disease etiology.     

Genetic variants on apolipoprotein gene cluster influence triglycerides with a risk of coronary artery disease among Indians

Apolipoprotein C3 and apolipoprotien A5 are proteins coded from the APOA1/C3/A4/A5 gene cluster. Sst I polymorphism on apolipoprotein C3 and −1131C polymorphism of apolipoprotien A5 are key variants involved in triglyceride metabolism and cause a significant cardio-metabolic risk. Here, we have evaluated these two variants for their roles in coronary artery disease in patients of the Indian population. The apolipoprotein gene cluster variants were analysed in 416 angiographically determined coronary artery disease patients and matched 416 controls using polymerase chain reaction—restriction fragment length polymorphism. The characteristics of the study subjects were analyzed statistically for their association with the polymorphisms. The alleles were combined as haplotypes and their combined risks were evaluated. The minor allele genotypes of both apolipoprotein C3 (S2) and apolipoprotien A5 (C) had a significant risk for coronary artery disease. The S2 allele genotyped patients had a significantly increased triglyceride level (P < 0.001) and increased triglycerides were observed among both patient and control CC genotype carriers. We identified the haplotype S2/C with a significant increased risk (P < 0.001) to coronary artery disease with increased levels of circulating triglycerides compared to other haplotypes in patients. We conclude that the variants on apolipoprotein C3 and apolipoprotien A5 modulate serum triglyceride levels and increase the risk of coronary artery disease.     

Broadly reactive antibodies specific for Plasmodium falciparum MSP-119 are associated with the protection of naturally exposed children against infection

ABSTRACT: BACKGROUND: The 19 kDa C-terminal region of Plasmodium falciparum Merozoite Surface Protein-1 is a known target of naturally acquired humoral immunity and a malaria vaccine candidate. MSP- 119 has four predominant haplotypes resulting in amino acid changes labelled EKNG, QKNG, QTSR and ETSR. IgG antibodies directed against all four variants have been detected, but it is not known if these variant specific antibodies are associated with haplotype-specific protection from infection. METHODS: Blood samples from 201 healthy Kenyan adults and children who participated in a 12-week treatment time-to-infection study were evaluated. Venous blood drawn at baseline (week 0) was examined for functional and serologic antibodies to MSP-119 and MSP-142 variants. MSP-119 haplotypes were detected by a multiplex PCR assay at baseline and weekly throughout the study. Generalized linear models controlling for age, baseline MSP-119 haplotype and parasite density were used to determine the relationship between infecting P. falciparum MSP-119 haplotype and variant-specific antibodies. RESULTS: A total of 964 infections resulting in 1,533 MSP-119 haplotypes detected were examined. The most common haplotypes were EKNG and QKNG, followed by ETSR and QTSR. Children had higher parasite densities, greater complexity of infection (>1 haplotype), and more frequent changes in haplotypes over time compared to adults. Infecting MSP-119 haplotype at baseline (week 0) had no influence on haplotypes detected over the subsequent 11 weeks among children or adults. Children but not adults with MSP-119 and some MSP-142 variant antibodies detected by serology at baseline had delayed time-to-infection. There was no significant association of variant-specific serology or functional antibodies at baseline with infecting haplotype at baseline or during 11 weeks of follow up among children or adults. CONCLUSIONS: Variant transcending IgG antibodies to MSP-119 are associated with protection from infection in children, but not adults. These data suggest that inclusion of more than one MSP-119 variant may not be required in a malaria blood stage vaccine.     

Bayesian spatial modeling of haplotype associations

We review methods for relating the risk of disease to a collection of single nucleotide polymorphisms (SNPs) within a small region. Association studies using case-control designs with unrelated individuals could be used either to test for a direct effect of a candidate gene and characterize the responsible variant(s), or to fine map an unknown gene by exploiting the pattern of linkage disequilibrium (LD). We consider a flexible class of logistic penetrance models based on haplotypes and compare them with an alternative formulation based on unphased multilocus genotypes. The likelihood for haplotype-based models requires summation over all possible haplotype assignments consistent with the observed genotype data, and can be fitted using either Expectation-Maximization (E-M) or Markov chain Monte Carlo (MCMC) methods. Subtleties involving ascertainment correction for case-control studies are discussed. There has been great interest in methods for LD mapping based on the coalescent or ancestral recombination graphs as well as methods based on haplotype sharing, both of which we review briefly. Because of their computational complexity, we propose some alternative empirical modeling approaches using techniques borrowed from the Bayesian spatial statistics literature. Here, space is interpreted in terms of a distance metric describing the similarity of any pair of haplotypes to each other, and hence their presumed common ancestry. Specifically, we discuss the conditional autoregressive model and two spatial clustering models: Potts and Voronoi. We conclude with a discussion of the implications of these methods for modeling cryptic relatedness, haplotype blocks, and haplotype tagging SNPs, and suggest a Bayesian framework for the HapMap project.     

Multiple divergent haplotypes express completely distinct sets of class I MHC genes in zebrafish

The zebrafish is an important animal model for stem cell biology, cancer, and immunology research. Histocompatibility represents a key intersection of these disciplines; however, histocompatibility in zebrafish remains poorly understood. We examined a set of diverse zebrafish class I major histocompatibility complex (MHC) genes that segregate with specific haplotypes at chromosome 19, and for which donor-recipient matching has been shown to improve engraftment after hematopoietic transplantation. Using flanking gene polymorphisms, we identified six distinct chromosome 19 haplotypes. We describe several novel class I U lineage genes and characterize their sequence properties, expression, and haplotype distribution. Altogether, ten full-length zebrafish class I genes were analyzed, mhc1uba through mhc1uka. Expression data and sequence properties indicate that most are candidate classical genes. Several substitutions in putative peptide anchor residues, often shared with deduced MHC molecules from additional teleost species, suggest flexibility in antigen binding. All ten zebrafish class I genes were uniquely assigned among the six haplotypes, with dominant or codominant expression of one to three genes per haplotype. Interestingly, while the divergent MHC haplotypes display variable gene copy number and content, the different genes appear to have ancient origin, with extremely high levels of sequence diversity. Furthermore, haplotype variability extends beyond the MHC genes to include divergent forms of psmb8. The many disparate haplotypes at this locus therefore represent a remarkable form of genomic region configuration polymorphism. Defining the functional MHC genes within these divergent class I haplotypes in zebrafish will provide an important foundation for future studies in immunology and transplantation.     

Fine localization of a major disease-susceptibility locus for diffuse panbronchiolitis

Diffuse panbronchiolitis affecting East Asians is strongly associated with the class I human leukocyte antigen (HLA) alleles. Recent observations suggest that a major disease-susceptibility gene may be located between the HLA-B and HLA-A loci in the class I region of the major histocompatibility complex on chromosome 6. To test this possibility, we analyzed 14 polymorphic markers in 92 Japanese patients and 93 healthy controls. Of these, seven marker alleles, including HLA-B54 and HLA-A11, were significantly associated with the disease. Maximum-likelihood haplotype analysis and subsequent direct determination of individual haplotypes identified a group of disease-associated haplotypes, one of which contained all seven disease-associated marker alleles. Another haplotype, containing HLA-B*5504, was also associated with the disease. All these haplotypes seem to have diverged from a common ancestral haplotype in East Asians and share a specific segment containing three consecutive markers between the S and TFIIH loci in the class I region. Furthermore, one of the markers within the candidate region showed the highest delta value, indicating the strongest association. Of 20 Korean patients with diffuse panbronchiolitis, 17 also shared the combination of the disease-associated marker alleles within the candidate region. These results indicate that an HLA-associated major susceptibility gene for diffuse panbronchiolitis is probably located within the 200 kb in the class I region 300 kb telomeric of the HLA-B locus on the chromosome 6p21.3.