Estimated Haplotype Frequencies Research Articles

Estimation of HLA-A, -B, -DRB1 Haplotype Frequencies Using Mixed Resolution Data from a National Registry with Selective Retyping of Volunteers

Large registries of volunteer hematopoietic stem cell donors typed for HLA contain potentially valuable data for studying haplotype frequencies in the general population. However the usual assumptions for use of the expectation-maximization (EM) algorithm are typically violated in these registries. To avoid this problem, previous studies using registry data have reduced the HLA typings to low-resolution and/or excluded subjects who were selected for testing on behalf of a specific patient ("patient-directed" typings). These restrictions, added to avoid bias from selection of nonrepresentative volunteers for higher-resolution typing, have limited previous results to haplotypes defined at low resolution. In this article we eliminate the need for such restrictions by formally relaxing the assumptions necessary for the EM algorithm. We show mathematically and through simulation that varying levels of resolution can be incorporated even if the level of typing resolution is chosen based on the HLA type. This allows use of intermediate and high resolution data from patient-directed typings to extend haplotype frequency estimates to the allele level for HLA-DRB1. We demonstrate the feasibility of using this computationally demanding algorithm on large datasets by applying it to more than 3 million volunteers listed in the National Marrow Donor Program Registry.

Cubic exact solutions for the estimation of pairwise haplotype frequencies: implications for linkage disequilibrium analyses and a web tool 'CubeX'.

BackgroundThe frequency of a haplotype comprising one allele at each of two loci can be expressed as a cubic equation (the 'Hill equation'), the solution of which gives that frequency. Most haplotype and linkage disequilibrium analysis programs use iteration-based algorithms which substitute an estimate of haplotype frequency into the equation, producing a new estimate which is repeatedly fed back into the equation until the values converge to a maximum likelihood estimate (expectation-maximisation).ResultsWe present a program, "CubeX", which calculates the biologically possible exact solution(s) and provides estimated haplotype frequencies, D', r2 and χ2 values for each. CubeX provides a "complete" analysis of haplotype frequencies and linkage disequilibrium for a pair of biallelic markers under situations where sampling variation and genotyping errors distort sample Hardy-Weinberg equilibrium, potentially causing more than one biologically possible solution. We also present an analysis of simulations and real data using the algebraically exact solution, which indicates that under perfect sample Hardy-Weinberg equilibrium there is only one biologically possible solution, but that under other conditions there may be more.ConclusionOur analyses demonstrate that lower allele frequencies, lower sample numbers, population stratification and a possible |D'| value of 1 are particularly susceptible to distortion of sample Hardy-Weinberg equilibrium, which has significant implications for calculation of linkage disequilibrium in small sample sizes (eg HapMap) and rarer alleles (eg paucimorphisms, q < 0.05) that may have particular disease relevance and require improved approaches for meaningful evaluation.

A New Method for Haplotype Inference Including Full-Sib Information

Recent literature has suggested that haplotype inference through close relatives, especially from nuclear families, can be an alternative strategy in determining linkage phase and estimating haplotype frequencies. In the case of no possibility to obtain genotypes for parents, and only full-sib information being used, a new approach is suggested to infer phase and to reconstruct haplotypes. We present a maximum-likelihood method via an expectation-maximization algorithm, called FSHAP, using only full-sib information when parent information is not available. FSHAP can deal with families with an arbitrary number of children, and missing parents or missing genotypes can be handled as well. In a simulation study we compare FSHAP with another existing expectation-maximization (EM)-based approach (FAMHAP), the conditioning approach implemented in FBAT and GENEHUNTER, which is only pedigree based and assumes linkage equilibrium. In most situations, FSHAP has the smallest discrepancy of haplotype frequency estimation and the lowest error rate in haplotype reconstruction, only in some cases FAMHAP yields comparable results. GENEHUNTER produces the largest discrepancy, and FBAT produces the highest error rate in offspring in most situations. Among the methods compared, FSHAP has the highest accuracy in reconstructing the diplotypes of the unavailable parents. Potential limitations of the method, e.g., in analyzing very large haplotypes, are indicated and possible solutions are discussed.

No evidence for significant association between GABA receptor genes in chromosome 15q11–q13 and autism in a Japanese population

The gamma-aminobutyric acid (GABA) receptor genes GABRB3, GABRA5, and GABRG3 located on chromosome 15q11-q13 have been major candidates for susceptibility genes for autism, a neurodevelopmental disorder with a complex genetic etiology. In this study, we first investigated the association between the GABA receptor genes and autism in a Japanese population by analyzing 11 single nucleotide polymorphisms (SNPs). Intron 3 of GABRB3 was densely mapped because the previous studies observed the association of the microsatellite 155CA-2 located in the region. We observed no significant difference in allelic frequencies or genotypic distributions of the 11 SNPs between patients and controls. A permutation test showed no significant global differences in estimated haplotype frequencies between patients and controls. Analysis after confining the subjects to males showed similar results. Thus, this study provides no positive evidence of an association between the GABA receptor genes and autism in a Japanese population. However, in a SNP (rs3212337) located near the microsatellite 155CA-2, a significant deviation from the Hardy-Weinberg equilibrium was observed in patients (p = 0.029, corrected for multiple testing). This finding may suggest further studies around the markers for more definitive conclusions.

HaploPool: improving haplotype frequency estimation through DNA pools and phylogenetic modeling

The search for genetic variants that are linked to complex diseases such as cancer, Parkinson's;, or Alzheimer's; disease, may lead to better treatments. Since haplotypes can serve as proxies for hidden variants, one method of finding the linked variants is to look for case-control associations between the haplotypes and disease. Finding these associations requires a high-quality estimation of the haplotype frequencies in the population. To this end, we present, HaploPool, a method of estimating haplotype frequencies from blocks of consecutive SNPs. HaploPool leverages the efficiency of DNA pools and estimates the population haplotype frequencies from pools of disjoint sets, each containing two or three unrelated individuals. We study the trade-off between pooling efficiency and accuracy of haplotype frequency estimates. For a fixed genotyping budget, HaploPool performs favorably on pools of two individuals as compared with a state-of-the-art non-pooled phasing method, PHASE. Of independent interest, HaploPool can be used to phase non-pooled genotype data with an accuracy approaching that of PHASE. We compared our algorithm to three programs that estimate haplotype frequencies from pooled data. HaploPool is an order of magnitude more efficient (at least six times faster), and considerably more accurate than previous methods. In contrast to previous methods, HaploPool performs well with missing data, genotyping errors and long haplotype blocks (of between 5 and 25 SNPs).

Direct haplotyping of bi-allelic SNPs using ARMS and RFLP analysis techniques

Haplotype analysis of single nucleotide polymorphisms (SNPs) is an important and rapidly growing approach for association studies. In recent years, statistical procedures to haplotype determination from genotypic information have employed in population studies. These procedures, even though some advantages for estimation of haplotype frequencies in large population samples, have limitations in the accuracy of the analysis. In this study, we have designed a reliable method for direct haplotyping of polymorphic sites using the amplification refractory mutation system (ARMS) and restriction fragment length polymorphism (RFLP) analysis techniques. We applied the method to determination of haplotypes composed of three SNPs within the paraoxonase1 gene promoter and found the approach can be used in many studies in population and in a variety of clinical settings.

Haplotype-based score test for linkage in nuclear families

BackgroundTo look for genetic linkage between angiotensin-I converting enzyme(ACE) gene and hypertension in a Korean adolescent cohort, we developed a powerful test using the covariances between marginal differences and their variances in a transmission/non-transmission table.ResultsWe estimated haplotype frequencies using the parental and affected offspring's genotypes and then constructed a transmission/non-transmission table for the parental haplotypes transmitted to the offspring. We then proposed a test for checking the marginal homogeneity in the table. Because the cells in the table were dependent due to the uncertainty of the parental haplotypes, we adopted a randomization procedure to estimate the significance of the observed test statistic. Simulations show that our test performs well on a nominal level and has a monotone power, which increases as the relative risk increases. With our test, there was no evidence of genetic linkage between the ACE gene and hypertension in the Korean adolescent cohort.ConclusionWe developed a score test for linkage and used simulations to demonstrate that our test performs well at a nominal level. Under some situations where the diversity of haplotypes is low, the proposed test gained a little power over the method based on only variances between marginal differences in a transmission/non-transmission table.

Comparison of ENCODE region SNPs between Cebu Filipino and Asian HapMap samples

Patterns of linkage disequilibrium (LD) act as the framework for designing efficient association studies; these patterns are being studied and catalogued by The International HapMap Project. The current study assessed the transferability of tag SNPs chosen from HapMap panels to a cohort of 80 individuals from metro Cebu, Philippines, who participated in the Cebu Longitudinal Health and Nutrition Survey (CLHNS). The analyses focused on 627 single nucleotide polymorphisms (SNPs) in the central 40 kb within each of the 10 HapMap ENCODE regions. The similarity between the genetic variants in Cebu Filipino samples and HapMap panels was examined using allele frequency estimates, measures of pairwise linkage disequilibrium (LD), and haplotype frequency estimates. For these measures, strong correlations were observed between the Cebu Filipino samples and the Asian panels from HapMap, with the strongest correlations observed with the Han Chinese from Beijing (CHB) panel. Tag SNPs selected using the HapMap CHB panel were particularly effective at representing the genetic variation in Cebu Filipino samples. These results suggest that the HapMap data will be an effective resource for future studies in Cebu Filipino samples.

Comparison of artificial neural network analysis with other multimarker methods for detecting genetic association

BackgroundDebate remains as to the optimal method for utilising genotype data obtained from multiple markers in case-control association studies. I and colleagues have previously described a method of association analysis using artificial neural networks (ANNs), whose performance compared favourably to single-marker methods. Here, the perfomance of ANN analysis is compared with other multi-marker methods, comprising different haplotype-based analyses and locus-based analyses.ResultsOf several methods studied and applied to simulated SNP datasets, heterogeneity testing of estimated haplotype frequencies using asymptotic p values rather than permutation testing had the lowest power of the methods studied and ANN analysis had the highest power. The difference in power to detect association between these two methods was statistically significant (p = 0.001) but other comparisons between methods were not significant. The raw t statistic obtained from ANN analysis correlated highly with the empirical statistical significance obtained from permutation testing of the ANN results and with the p value obtained from the heterogeneity test.ConclusionAlthough ANN analysis was more powerful than the standard haplotype-based test it is unlikely to be taken up widely. The permutation testing necessary to obtain a valid p value makes it slow to perform and it is not underpinned by a theoretical model relating marker genotypes to disease phenotype. Nevertheless, the superior performance of this method does imply that the widely-used haplotype-based methods for detecting association with multiple markers are not optimal and efforts could be made to improve upon them. The fact that the t statistic obtained from ANN analysis is highly correlated with the statistical significance does suggest a possibility to use ANN analysis in situations where large numbers of markers have been genotyped, since the t value could be used as a proxy for the p value in preliminary analyses.

Minor differences in haplotype frequency estimates can produce very large differences in heterogeneity test statistics

BackgroundTests for association between a haplotype and disease are commonly performed using a likelihood ratio test for heterogeneity between case and control haplotype frequencies. Using data from a study of association between heroin dependence and the DRD2 gene, we obtained estimated haplotype frequencies and the associated likelihood ratio statistic using two different computer programs, MLOCUS and GENECOUNTING. We also carried out permutation testing to assess the empirical significance of the results obtained.ResultsBoth programs yielded similar, though not identical, estimates for the haplotype frequencies. MLOCUS produced a p value of 1.8*10-15 and GENECOUNTING produced a p value of 5.4*10-4. Permutation testing produced a p value 2.8*10-4.ConclusionThe fact that very large differences occur between the likelihood ratio statistics from the two programs may reflect the fact that the haplotype frequencies for the combined group are not constrained to be equal to the weighted averages of the frequencies for the cases and controls, as they would be if they were directly observed rather than being estimated. Minor differences in haplotype frequency estimates can result in very large differences in the likelihood ratio statistic and associated p value.

Homozygosity Haplotype Allows a Genomewide Search for the Autosomal Segments Shared among Patients

A promising strategy for identifying disease susceptibility genes for both single- and multiple-gene diseases is to search patients' autosomes for shared chromosomal segments derived from a common ancestor. Such segments are characterized by the distinct identity of their haplotype. The methods and algorithms currently available have only a limited capability for determining a high-resolution haplotype genomewide. We herein introduce the homozygosity haplotype (HH), a haplotype described by the homozygous SNPs that are easily obtained from high-density SNP genotyping data. The HH represents haplotypes of both copies of homologous autosomes, allowing for direct comparisons of the autosomes among multiple patients and enabling the identification of the shared segments. The HH successfully detected the shared segments from members of a large family with Marfan syndrome, which is an autosomal dominant, single-gene disease. It also detected the shared segments from patients with model multigene diseases originating with common ancestors who lived 10-25 generations ago. The HH is therefore considered to be useful for the identification of disease susceptibility genes in both single- and multiple-gene diseases.

Common variants in genes that mediate immunity and risk of multiple myeloma

Multiple myeloma (MM) is a B-cell malignancy characterized by aberrant immune function. Using genomic DNA extracted from 127 MM cases aged 21-84 years and 545 population-based controls, we examined the risk of MM associated with 82 common variants in 45 genes that mediate immunity among women of European American descent. Genotyping was determined using validated and optimized TaqMan assays. We estimated haplotype frequencies from unphased genotype data for 20 of these genes using the expectation-maximization progressive insertion algorithm. Compared with controls, MM risk was positively associated with homozygotes of single loci, IL4R (-28120T, rs2107356) and FCGR2A (-120G, rs1801274) (OR = 1.91, 95% CI 1.08-3.38 and 1.95, 95% CI 1.06-3.60, respectively). For genes in which linkage disequilibrium was observed between multiple loci, MM risk was positively associated with the haplotype block covering part of the LTA*TNF complex (LTA -82C/-90G *TNF -1036C/-487G/-417G, OR = 1.63, 95% CI 1.02-2.16) compared with the most frequently occurring haplotype observed among controls (LTA -82A/-90A *TNF -1036C/-487G/-417G). Our findings provide preliminary evidence that common genetic variants in specific immune-mediated pathways could influence the risk of MM.

Preliminary analysis of a KIR haplotype estimation algorithm: a simulation study

The analysis of Killer cell immunoglobulin-like receptors (KIRs) in terms of haplotypes have only been done through genotyping numerous and selected families. Consequently and schematically, KIR haplotypes have been roughly described by two groups (A and B) based on their gene contents. No further KIR adapted methods have been applied to the estimation of haplotype frequencies using unrelated data. We propose here a maximum likelihood (ML) estimation of KIR haplotype frequencies. ML estimation was developed as an extension of those successfully applied to human leukocyte antigen (HLA) data including the handling of missing values and HLA nomenclature. It has been implemented using an adapted Expectation Masimisation algorithm. KIR types on 11 loci in more than 40 Irish families were used to validate the method in a simulation study. Estimated haplotype frequencies are compared to the phase known. Various allele or gene frequency estimation methods were also compared. We demonstrated the interest and reliability of the haplotype method and underline the effect of the sample size on the quality of the estimation. The ML haplotype method also provides by collapsing more accurate estimation of allele or gene frequencies in population. Such an algorithm opens new perspectives in the analysis of KIR genotypes. Large sample size studies are required using phase-known data and/or simulations. It would allow a genotype-based approach to explore the KIR gene haplotype diversity. The haplotype frequencies may be used to compare populations.

PyPop update – a software pipeline for large‐scale multilocus population genomics

Population genetic statistics from multilocus genotype data inform our understanding of the patterns of genetic variation and their implications for evolutionary studies, generally, and human disease studies in particular. In any given population one can estimate haplotype frequencies, identify deviation from Hardy-Weinberg equilibrium, test for balancing or directional selection, and investigate patterns of linkage disequilibrium. Existing software packages are oriented primarily toward the computation of such statistics on a population-by-population basis, not on comparisons among populations and across different statistics. We developed PyPop (Python for Population Genomics) to facilitate the analyses of population genetic statistics across populations and the relationships among different statistics within and across populations. PyPop is an open-source framework for performing large-scale population genetic analyses on multilocus genotype data. It computes the statistics described above, among others. PyPop deploys a standard Extensible Markup Language (XML) output format and can integrate the results of multiple analyses on various populations that were performed at different times into a common output format that can be read into a spreadsheet. The XML output format allows PyPop to be embedded as part of a larger analysis pipeline. Originally developed to analyze the highly polymorphic genetic data of the human leukocyte antigen region of the human genome, PyPop has applicability to any kind of multilocus genetic data. It is the primary analysis platform for analyzing data collected for the Anthropological component of the 13th and 14th International Histocompatibility Workshops. PyPop has also been successfully used in studies by our group, with collaborators, and in publications by several independent research teams.

Accounting for Genotyping Errors in Tagging SNP Selection

One limitation of the existing tagging SNP selection algorithms is that they assume the reported genotypes are error free. However, genotyping errors are often unavoidable in practice. Many tagging SNP selection methods depend heavily on the estimated haplotype frequencies. Recent studies have demonstrated that even slight genotyping errors can lead to serious consequences with regard to haplotype reconstruction and frequency estimation. Here we present a tagging SNP selection method that allows for genotyping errors. Our method is a modification of the pair-wise r(2) tagging SNP selection algorithm proposed by Carlson et al. (2004). We have replaced the standard EM algorithm in Carlson's method with an EM that accounts for genotyping errors, in an attempt to obtain better estimates of the haplotype frequencies and r(2) measure. Through simulation studies we compared the performance of our modified algorithm with that of the original algorithm. We found that the number of tags selected by both methods increased with increasing genotyping errors, though our method led to smaller increase. The power of haplotype association tests using the selected tags decreased dramatically with increasing genotyping errors. The power of single marker tests also decreased, but the reduction was not as much as the reduction in power of haplotype tests. When restricting the mean number of tags selected by both methods to be similar to the baseline number, Carlson's method and our method led to similar power for the subsequent haplotype and single marker tests. Our results showed that, by accounting for random genotyping errors, our method can select tagging SNPs more efficiently than Carlson's method. The computer program that implements our modified tagging SNP selection algorithm is available at our web site: http://www.personal.psu.edu/tuy104/.

Association of lipoprotein lipase gene polymorphisms with obesity and type 2 diabetes in an Asian Indian population

Lipoprotein lipase (LPL), a pivotal enzyme in lipoprotein metabolism, catalyzes the hydrolysis of triglycerides of very low-density lipoproteins and chylomicrons. Assuming that the variants in the promoter of the LPL gene may be associated with changes in lipid metabolism leading to obesity and type 2 diabetes, we examined the role of promoter variants (-T93G and -G53C) in the LPL gene in an urban South Indian population. The study subjects (619 type 2 diabetic and 731 normal glucose-tolerant (NGT) subjects) were chosen from the Chennai Urban Rural Epidemiology Study, an ongoing population-based study in southern India. The polymorphisms were genotyped using polymerase chain reaction-restriction-fragment length polymorphism (PCR-RFLP). Linkage disequilibrium (LD) was estimated from the estimates of haplotypic frequencies. The two polymorphisms studied were not in LD. The -T93G was not associated with type 2 diabetes but was associated with obesity. 11.5% of the obese subjects (62/541) had the XG(TG+GG) genotype compared with 6.4% of the nonobese subjects (52/809; P=0.001). The odds ratio for obesity for the XG genotype was 1.766 (95% CI: 1.19-2.63, P=0.005). Subjects with XG genotype also had higher body mass index and waist circumference compared with those with TT genotype. With respect to G53C, subjects with the XC(GC+CC) genotype had 0.527 and 0.531 times lower risk for developing type 2 diabetes and obesity, respectively. Among Asian Indians, the -T93G SNP of the LPL gene is associated with obesity but not type 2 diabetes, whereas the -G53C SNP appears to be protective against both obesity and type 2 diabetes.

Conditional Likelihood Inference in a Case-Cohort Design: An Application to Haplotype Analysis

Under the setting of a case-cohort design, covariate values are ascertained for a smaller subgroup of the original study cohort which typically is a representative sample from a population. Individuals with a specific event outcome are selected to the second stage study group as cases and an additional subsample is selected to act as a control group. We carry out analysis of such a design using conditional likelihood where the likelihood expression is conditioned on the ascertainment to the second stage study group. Such likelihood expression involves the probability of ascertainment which need to be expressed in terms of the model parameters. We present examples of conditional likelihoods for models for categorical response and time-to-event response. We show that the conditional likelihood inference leads to valid estimation of population parameters. Our application considers joint estimation of haplotype-event association parameters and population haplotype frequencies based on SNP genotype data collected under a case-cohort design.

An Expectation Maximization Approach to Estimate Malaria Haplotype Frequencies in Multiply Infected Children

Characterizing genetic variability in the human pathogenic Plasmodium species, the group of parasites that cause Malaria, may have broad global health implications. Specifically, discerning the combinations of mutations that lead to viable parasites and the population level frequencies of these clonal sequences will allow for targeted vaccine development and individualized treatment choices. This presents an analytical challenge, however, since haplotypic phase (i.e. the alignment of bases on a single DNA strand) is generally unobservable in multiply infected individuals. This manuscript describes an expectation maximization (EM) approach to maximum likelihood estimation of haplotype frequencies in this missing data setting. The approach is applied to a cohort of N=341 malaria infected children in Uganda, Cameroon and Sudan to characterize regional differences. A simulation study is also presented to characterize method performance and assess sensitivity to distributional assumptions.

A population-based association study of SNPs of GSTP1, MnSOD, GPX2 and Barrett's esophagus and esophageal adenocarcinoma

Oxidative stress appears to be important in the pathogenesis of Barrett's esophagus (BE) and esophageal adenocarcinoma (EAC). Single-nucleotide polymorphisms (SNPs) of antioxidant enzyme genes may play a part in determining individual susceptibility to these diseases. The Factors Influencing the Barrett's Adenocarcinoma Relationship (FINBAR) study is a population-based, case-control study of BE and EAC in Ireland. DNA from EAC (n = 207), BE (> or =3 cm BE at endoscopy with specialized intestinal metaplasia on biopsy, n = 189) and normal population controls (n = 223) were analyzed. Several SNPs spanning the genes for glutathione S-transferase P1 (GSTP1), manganese superoxide dismutase (MnSOD) and glutathione peroxidase 2 (GPX2) were genotyped using multiplex polymerase chain reaction and SNaPshottrade mark. The chi(2) test was used to compare genotype and allele frequencies between case and control subjects. Linkage disequilibrium between SNPs was quantified using Lewontin's D' value and haplotype frequency estimates obtained using Haploview. Eleven SNPs were genotyped (six for GSTP1, three for MnSOD and two for GPX2); all were in Hardy-Weinberg equilibrium. None was significantly associated with EAC or BE even before Bonferroni correction. Odds ratios for EAC for individual SNPs ranged from 0.68 [95% confidence interval (CI) 0.43-1.08] to 1.25 (95% CI 0.73-2.16), and for BE from 0.84 (95% CI 0.52-1.30) to 1.30 (95% CI 0.85-1.97). SNPs in all three genes were in strong linkage disequilibrium (D' > 0.887) but haplotype analysis did not show any significant association with EAC or BE. SNPs involving the GSTP1, MnSOD and GPX2 genes were not associated with BE or EAC. Further studies aimed at identifying susceptibility genes should focus on different antioxidant genes or different pathways.

Solute Carrier Family 11 Member A1 Gene Polymorphisms in Reactive Arthritis

To investigate the role of SLC 11A1 polymorphisms in the development of reactive arthritis, 91 patients with reactive arthritis and 163 healthy controls were enrolled in this study. The SLC 11A1 polymorphisms were determined by the method of polymerase chain reaction/restriction fragment length polymorphism. The genotype distributions of SLC 11A1 274, 823, 1703, and 1729+ 55 del 4 were significantly different between the patients with reactive arthritis and controls. The genotype frequency of SLC 11A1 274C/C was significantly decreased in the patients with reactive arthritis when compared with that of the controls. In contrast, the SLC 11A1 274C/T showed a significant association with reactive arthritis. The patients with reactive arthritis have a significantly higher frequency of SLC 11A1 823C/C than the controls. However, SLC 11A1 823T/T was resistant to the development of reactive arthritis. The allele frequencies of SLC 11A1 274T and 823C were significantly increased in the patients with reactive arthritis in comparison with those of the controls, independent of HLA-B27. On the contrary, the allele frequencies of SLC 11A1 274C and 823T were significantly decreased in the patients with reactive arthritis. The estimated haplotype frequency of SLC 11A1 274C 823T 1703G 1729+55del 4 TGTG+ was significantly decreased in the patients with reactive arthritis when compared with that of the controls. In contrast, the estimated haplotype frequency of SLC 11A1 274T 823C 1703G 1729+55 del 4 TGTG+ was significantly increased in the patients with reactive arthritis. This study shows that the SLC 11A1 274T and 823C alleles are associated with susceptibility to reactive arthritis independently of HLA-B27 in Taiwan. The SLC 11A1 274T 823C 1703G 1729+55 del 4 TGTG+ haplotype is associated with the development of reactive arthritis in Taiwan. In contrast, the SLC 11A1 274C 823T 1703G 1729+55 del 4 TGTG+ haplotype may be a protective factor.