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Query: UMLS:C0003873 (rheumatoid arthritis)
 53,068 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

We proposed a confidence interval method for disease gene localization by testing every position on each chromosome of interest for its possibility of being a disease locus and including those not rejected into the interval. Three test statistics were proposed to perform the tests, including one based on LOD and two generalized likelihood ratio tests with or without model averaging (GLRT/MA and GLRT). For the statistic based on LOD, an integrated procedure was proposed with an adaptive and an importance sampling component. We also proposed asymptotic approaches based on GLRT and GLRT/MA as alternatives that are much more efficient computationally but depends on the reliability of the limiting distributions. Besides its efficiency, the asymptotic procedure based on GLRT/MA also takes model uncertainty into consideration. Applications of these methods to the Genetic Analysis Workshop 15 (GAW15) rheumatoid arthritis data from the French population gave results that successfully captured the well recognized susceptibility gene HLA*DRB1 to a less than 6 cM, 99% confidence interval with the two asymptotic approaches.
BMC Proc 2007
PMID:A likelihood-based procedure for obtaining confidence intervals of disease loci with general pedigree data. 1846 46

Rheumatoid arthritis is a clinically and genetically heterogeneous disease. Anti-cyclic citrullinated (anti-CCP) antibodies have a high specificity for rheumatoid arthritis and levels correlate with disease severity. The focus of this study was to examine whether analyzing anti-CCP levels could increase the power of linkage analysis by identifying a more homogeneous subset of rheumatoid arthritis patients. We also wanted to compare linkage signals when analyzing anti-CCP levels as dichotomized (CCP_binary), categorical (CCP_cat), and continuous traits, with and without transformation (log_CCP and CCP_cont). Illumina single-nucleotide polymorphism scans of the North American Rheumatoid Arthritis Consortium families were analyzed for four chromosomes (6, 7, 11, 22) using nonparametric linkage (NPL) (rheumatoid arthritis and CCP_binary), regress (CCP_cat and Log_CCP), and deviates (CCP_cont) analysis options as implemented in Merlin. Similar linkage results were obtained from analyses of rheumatoid arthritis, CCP_binary, and CCP_cont. The only exception was that we observed improved linkage signals and a narrower region for CCP_binary as compared to a clinical diagnosis of rheumatoid arthritis alone on chromosome 7, a region which previously showed variation in linkage results with rheumatoid arthritis according to anti-CCP levels. Analyses of CCP_cat and Log_CCP had little power to detect linkage. Our data suggested that linkage analyses of anti-CCP levels may facilitate identification of rheumatoid arthritis genes but quantitative analyses did not further improve power. Our study also highlighted that quantitative trait linkage results are highly sensitive to phenotype transformation and analytic approaches.
BMC Proc 2007
PMID:Linkage analysis of anti-CCP levels as dichotomized and quantitative traits using GAW15 single-nucleotide polymorphism scan of NARAC families. 1846 47

The Genetic Analysis Workshop 15 rheumatoid arthritis data included a set of 460 cases and 460 controls genotyped at 2300 closely spaced markers on a 10 megabase region of chromosome 18q. We conducted a multilocus analysis of these data using a localized haplotype clustering method that adapts to linkage disequilibrium structure and can be applied to large, densely genotyped data sets such as this one. We found a protective haplotype carried by 33 individuals that was significantly associated with rheumatoid arthritis in these data after adjusting for multiple testing. This haplotype was located less than 500 base pairs upstream of the CCBE1 gene. The association was not detected using single-marker tests, but could be found using a variety of multilocus tests.
BMC Proc 2007
PMID:Multilocus analysis of GAW15 NARAC chromosome 18 case-control data. 1846 50

Rheumatoid arthritis is a complex disease caused by a combination of genetic, environmental, and hormonal factors, and their additive and/or non-additive effects. We performed a linkage analysis to provide evidence of rheumatoid factor IgM on linkage, based on Bayesian variable selection coupled with the new Haseman-Elston method. For statistical inferences to estimate unknown parameters, we utilized the perfect sampling algorithm, an emerging simulation technique that alleviates concerns over convergence and sampling mixing. Our methods provide powerful and conceptually simple approaches to simultaneous genome scans of main effects and all possible pairwise interactions. We apply them to the Genetic Analysis Workshop 15 data (Problem 2) provided by the North American Rheumatoid Arthritis Consortium (NARAC).
BMC Proc 2007
PMID:A Bayesian genome-wide linkage analysis of quantitative traits for rheumatoid arthritis via perfect sampling. 1846 51

Although rheumatoid arthritis, a chronic and inflammatory disease affecting numerous adults, has a complex genetic component involving the human leukocyte antigen region, additional genomic regions most likely affects susceptibility. Whole-genome scans may assist in identifying these additional candidate regions, but a large number of false-positives are likely to occur using traditional statistical methods. Therefore, novel statistical approaches are needed. Here, we used a single replicate from the Genetic Analysis Workshop 15 simulated data to assess for marker-disease associations in 1500 rheumatoid arthritis cases and 2000 controls on chromosome 6. The statistical methods included a maximum-likelihood estimation approach and a novel Bayesian latent class analysis. The Bayesian analysis "borrows strength" from multiple loci to estimate association parameters and can incorporate differences across loci in the prior probability of association. Because of this, we hypothesized that the Bayesian analysis might be better able to detect true associations while minimizing false positives. The Bayesian posterior means for the log alleleic odds ratios were less variable than the maximum likelihood estimates, but the posterior probabilities were not as good as the simple p-values in distinguishing a signal from a non-signal. Overall, Bayesian latent class analyses provided no obvious improvement over maximum-likelihood estimation. However, our results may not be able to be generalized due to the large effect simulated in the human leukocyte antigen-DR locus.
BMC Proc 2007
PMID:A Bayesian latent class analysis for whole-genome association analyses: an illustration using the GAW15 simulated rheumatoid arthritis dense scan data. 1846 53

We studied rheumatoid arthritis (RA) in the North American Rheumatoid Arthritis Consortium (NARAC) data (1499 subjects; 757 families). Identical methods were applied for studying RA in the Genetic Analysis Workshop 15 (GAW15) simulated data (with a prior knowledge of the simulation answers). Fifty replications of GAW15 simulated data had 3497 +/- 20 subjects in 1500 nuclear families. Two new statistical methods were applied to transform the original phenotypes on these data, the item response theory (IRT) to create a latent variable from nine classifying predictors and a Blom transformation of the anti-CCP (anti-cyclic citrinullated protein) variable. We performed linear mixed-effects (LME) models to study the additive associations of 404 Illumina-genotyped single-nucleotide polymorphisms (SNPs) on the NARAC data, and of 17,820 SNPs of the GAW15 simulated data. In the GAW15 simulated data, the association with anti-CCP Blom transformation showed a 100% sensitivity for SNP1 located in the major histocompatibility complex gene. In contrast, the association of SNP1 with the IRT latent variable showed only 24% sensitivity. From the simulated data, we conclude that the Blom transformation of the anti-CCP variable produced more reliable results than the latent variable from the qualitative combination of a group of RA risk factors. In the NARAC data, the significant RA-SNPs associations found with both phenotype-transformation methods provided a trend that may point toward dynein and energy control genes. Finer genotyping in the NARAC data would grant more exact evidence for the contributions of chromosome 6 to RA.
BMC Proc 2007
PMID:Rheumatoid arthritis, item response theory, Blom transformation, and mixed models. 1846 57

Rheumatoid arthritis is a complex disease that appears to involve multiple genetic and environmental factors. Using the Genetic Analysis Workshop 15 simulated rheumatoid arthritis data and the structural equation modeling framework, we tested hypothesized "causal" rheumatoid arthritis model(s) by employing a novel latent gene construct approach that models individual genes as latent variables defined by multiple dense and non-dense single-nucleotide polymorphisms (SNPs). Our approach produced valid latent gene constructs, particularly with dense SNPs, which when coupled with other factors involved in rheumatoid arthritis, were able to generate good fitting models by certain goodness of fit indices. We observed that Gene F, C, DR, sex and smoking were significant predictors of rheumatoid arthritis but Genes A and E were not, which was generally, but not entirely, consistent with how the data were simulated. Our approach holds promise in unravelling complex diseases and improves upon current "one SNP (haplotype)-at-a-time" regression approaches by decreasing the number of statistical tests while minimizing problems with multicolinearity and haplotype estimation algorithm error. Furthermore, when genes are modeled as latent constructs simultaneously with other key cofactors, the approach provides enhanced control of confounding that should lead to less biased effect estimates among genes as well as between gene(s) and the complex disease. However, further study is needed to quantify bias, evaluate fit index disparity, and resolve multiplicative latent gene interactions. Moreover, because some a priori biological information is needed to form an initial substantive model, our approach may be most appropriate for candidate gene SNP panel applications.
BMC Proc 2007
PMID:Modeling the complex gene x environment interplay in the simulated rheumatoid arthritis GAW15 data using latent variable structural equation modeling. 1846 59

PedGenie beta version 2.1 is a unique, flexible, and easily implemented analysis software tool that is enhanced significantly by incorporation of meta-statistics to allow valid combined analysis of multiple studies, including mixtures of family-based and independent resources, in the detection of genetic association with common disease. Genetic Analysis Workshop 15 Problem 2 data, provided by the North American Rheumatoid Arthritis Consortium, were used to demonstrate PedGenie 2.1 meta-association testing of variants in the PTPN22 gene and rheumatoid arthritis across multiple resources containing both family-based and independent individuals. Our findings are generally consistent with previous reports for a panel of 14 single-nucleotide polymorphism (SNP) markers, including functional coding SNP R620W, in which the minor allele conferred a significant two-fold increased risk. More power to detect associations was achieved in certain analyses by using extra family-based samples, rather than restricting analyses to single cases randomly selected from each pedigree.
BMC Proc 2007
PMID:Meta-genetic association of rheumatoid arthritis and PTPN22 using PedGenie 2.1. 1846 61

We sought to i) identify putative genetic determinants of the severity of rheumatoid arthritis in the NARAC (North American Rheumatoid Arthritis Consortium) data, ii) assess whether known candidate genes for disease status are also associated with disease severity in those affected, and iii) determine whether heterogeneity among the severity phenotypes can be explained by genetic and/or host factors. These questions are addressed by developing bivariate mixed-counting process models for numbers of tender and swollen joints to evaluate genetic association of candidate polymorphisms, such as DRB1, and selected single-nucleotide polymorphisms in known candidate genes/regions for rheumatoid arthritis, including PTPN22, and those in the regions identified by a genome-wide linkage scan of disease severity using the dense Illumina single-nucleotide polymorphism panel. The counting process framework provides a flexible approach to account for the duration of rheumatoid arthritis, an attractive feature when modeling severity of a disease. Moreover, we found a gain in efficiency when using a bivariate compared to a univariate counting process model.
BMC Proc 2007
PMID:Application of bivariate mixed counting process models to genetic analysis of rheumatoid arthritis severity. 1846 62

Non-inherited maternal antigens encoded by specific HLA-DRB1 alleles (NIMA) have been implicated as a rheumatoid arthritis (RA) risk factor. Using genotype data from North American Rheumatoid Arthritis Consortium study participants and the maternal-fetal genotype incompatibility (MFG) test, we find evidence for offspring allelic effects but no evidence for NIMA as a RA risk factor. We discuss possible reasons why our result conflicts with several previous studies (including one of our own) that used RA patients from northern Europe.
BMC Proc 2007
PMID:Using the maternal-fetal genotype incompatibility test to assess non-inherited maternal HLA-DRB1 antigen coding alleles as rheumatoid arthritis risk factors. 1846 66


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