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

The goal of this study was to identify single-locus and epistasis effects of SNP markers on anti-cyclic citrullinated peptide (anti-CCP) that is associated with rheumatoid arthritis, using the North American Rheumatoid Arthritis Consortium data. A square root transformation of the phenotypic values of anti-CCP with sex, smoking status, and a selected subset of 20 single-nucleotide polymorphism (SNP) markers in the model achieved residual normality (p > 0.05). Three single-locus effects of two SNPs were significant (p < 10-4). The epistasis analysis tested five effects of each pair of SNPs, the two-locus interaction, additive x additive, additive x dominance, dominance x additive, and dominance x dominance effects. A total of ten epistasis effects of eight pairs of SNPs on 11 autosomes and the X chromosome had significant epistasis effects (p < 10-7). Three of these epistasis effects reached significance levels of p < 10-8, p < 10-9, and p < 10-10, respectively. Two potential SNP epistasis networks were identified. The results indicate that the genetic factors underlying anti-CCP may include single-gene action and gene interactions and that the gene-interaction mechanism underlying anti-CCP could be a complex mechanism involving pairwise epistasis effects and multiple SNPs.
BMC Proc 2007
PMID:Genome-wide analysis of single-locus and epistasis single-nucleotide polymorphism effects on anti-cyclic citrullinated peptide as a measure of rheumatoid arthritis. 1846 69

We propose two new haplotype-sharing methods for identifying disease loci: the haplotype sharing statistic (HSS), which compares length of shared haplotypes between cases and controls, and the CROSS test, which tests whether a case and a control haplotype show less sharing than two random haplotypes. The significance of the HSS is determined using a variance estimate from the theory of U-statistics, whereas the significance of the CROSS test is estimated from a sequential randomization procedure. Both methods are fast and hence practical, even for whole-genome screens with high marker densities. We analyzed data sets of Problems 2 and 3 of Genetic Analysis Workshop 15 and compared HSS and CROSS to conventional association methods. Problem 2 provided a data set of 2300 single-nucleotide polymorphisms (SNPs) in a 10-Mb region of chromosome 18q, which had shown linkage evidence for rheumatoid arthritis. The CROSS test detected a significant association at approximately position 4407 kb. This was supported by single-marker association and HSS. The CROSS test outperformed them both with respect to significance level and signal-to-noise ratio. A 20-kb candidate region could be identified. Problem 3 provided a simulated 10 k SNP data set covering the whole genome. Three known candidate regions for rheumatoid arthritis were detected. Again, the CROSS test gave the most significant results. Furthermore, both the HSS and the CROSS showed better fine-mapping accuracy than straightforward haplotype association. In conclusion, haplotype sharing methods, particularly the CROSS test, show great promise for identifying disease gene loci.
BMC Proc 2007
PMID:Association testing by haplotype-sharing methods applicable to whole-genome analysis. 1846 71

This paper presents a novel method of identifying phenotypically important regions of the genome. It involves a form of association mapping that works by summarizing properties of the ancestral recombination graph (ARG) of a sample of unrelated phenotyped and genotyped individuals. By breaking the sample into many small sub-samples and averaging the results, it becomes computationally tractable to measure the degree to which the evolutionary history of any locus is consistent with the distribution of the phenotypes in the sample. Analysis of simulated rheumatoid arthritis data demonstrates the efficiency and effectiveness of this method in identifying loci of large phenotypic effect.
BMC Proc 2007
PMID:Association mapping through heuristic evolutionary history reconstruction-application to GAW15 Problem 3. 1846 74

Large-scale genome-wide association studies are increasingly common, due in large part to recent advances in genotyping technology. Despite a dramatic drop in genotyping costs, it is still too expensive to genotype thousands of individuals for hundreds of thousands single-nucleotide polymorphisms (SNPs) for large-scale whole-genome association studies for many researchers. A two-stage design has been a promising alternative: in the first stage, only a small fraction of samples are genotyped and tested using a dense set of SNPs, and only a small subset of markers that show moderate associations with the disease will be genotyped in the second stage. In this report, I developed an approach to select and prioritize SNPs for association studies with a two-stage or multi-stage design. In the first stage, the method not only evaluates associations of SNPs with the disease of interest, it also explicitly explores correlations among SNPs. I applied the approach on the simulated Genetic Analysis Workshop 15 Problem 3 data sets, which have modeled the complex genetic architecture of rheumatoid arthritis. Results show that the method can greatly reduce the number of SNPs required in later stage(s) without sacrificing mapping precision.
BMC Proc 2007
PMID:Marker selection for whole-genome association studies with two-stage designs using dense single-nucleotide polymorphisms. 1846 79

Finding a genetic marker associated with a trait is a classic problem in human genetics. Recently, two-stage approaches have gained popularity in marker-trait association studies, in part because researchers hope to reduce the multiple testing problem by testing fewer markers in the final stage. We compared one two-stage family-based approach to an analogous single-stage method, calculating the empirical type I error rates and power for both methods using fully simulated data sets modeled on nuclear families with rheumatoid arthritis, and data sets of real single-nucleotide polymorphism genotypes from Centre d'Etude du Polymorphisme Humain pedigrees with simulated traits. In these analyses performed in the absence of population stratification, the single-stage method was consistently more powerful than the two-stage method for a given type I error rate. To explore the sources of this difference, we performed a case study comparing the individual steps of two-stage designs, the two-stage design itself, and the analogous one-stage design.
BMC Proc 2007
PMID:One-stage design is empirically more powerful than two-stage design for family-based genome-wide association studies. 1846 80

The incorporation of disease-associated covariates into studies aiming to identify susceptibility genes for complex human traits is a challenging problem. Accounting for such covariates in genetic linkage and association analyses may help reduce the genetic heterogeneity inherent in these complex phenotypes. For Genetic Analysis Workshop 15 (GAW15) Problem 3 simulated data, our goal was to compare the power of several two-stage study designs to identify rheumatoid arthritis-related genes on chromosome 9 (disease severity), 11 (IgM), and 18 (anti-cyclic citrinullated protein), with knowledge of the answers. Five study designs incorporating an initial linkage step, followed by a case-selection scheme and case-control association analysis by logistic regression, were considered. The linkage step was either qualitative-trait linkage analysis as implemented in MERLIN-nonparametric linkage (NPL), or quantitative-trait locus analysis as implemented in MERLIN-REGRESS. A set of cases representing either one case from each available family, one case per linked family (NPL >/= 0), or one case from each family identified by ordered-subset analysis was chosen for comparison with the full set of 2000 simulated controls. As expected, the performance of these study designs depended on the disease model used to generate the data, especially the simulated allele frequency difference between cases and controls. The quantitative trait loci analysis performed well in identifying these loci, and the power to identify disease-associated alleles was increased by using ordered-subset analysis as a case selection tool.
BMC Proc 2007
PMID:Two-stage study designs for analyzing disease-associated covariates: linkage thresholds and case-selection strategies. 1846 81

In the present paper, we used the North American Rheumatoid Arthritis Consortium data provided for Genetic Analysis Workshop 15 Problem 2 to: 1) estimate the penetrances of PTPN22 and HLA-DRB1 and, 2) test the selected model of PTPN22 conditional on the rheumatoid factor status. To achieve these aims, we used the marker association segregation chi-square method, fitting simultaneously both genotype frequency and identical by descent distributions in a sample of 3690 White individuals from 604 nuclear families. A co-dominant model fitted the rs2476601 (R620W) single-nucleotide polymorphism (SNP) of the PTPN22 gene well, whereas a lack of fit for all models was observed for the HLA-DRB1 locus. Testing genetic models of rheumatoid arthritis that include the PTPN22 SNP in addition to the HLA-DRB1 locus did not affect the results, nor did subgroup analysis of PTPN22 conditional on the rheumatoid factor status. In conclusion, PTPN22 R620W SNP is a risk factor for rheumatoid arthritis. The genetic architecture of the HLA-DRB1 locus is highly complex, and more elaborate modeling of this locus is required.
BMC Proc 2007
PMID:Modeling of PTPN22 and HLA-DRB1 susceptibility to rheumatoid arthritis. 1846 83

We analyzed a case-control data set for chromosome 18q from the Genetic Analysis Workshop 15 to detect susceptibility loci for rheumatoid arthritis (RA). A total number of 460 cases and 460 unaffected controls were genotyped on 2300 single-nucleotide polymorphisms (SNPs) by the North American Rheumatoid Arthritis Consortium. Using a multimarker approach for association mapping under the framework of the Malecot model and composite likelihood, we identified a region showing significant association with RA (p < 0.002) and the predicted disease locus was at a genomic location of 53,306 kb with a 95% confidence interval (CI) of 53,295-53,331 kb. A common haplotype in this region was protective against RA (p = 0.002). In another region showing nominal significant association (51,585 kb, 95% CI: 51,541-51,628 kb, p = 0.037), a haplotype was also protective (p = 0.002). We further demonstrated that reducing SNP density decreased power and accuracy of association mapping. SNP selection based on equal linkage disequilibrium (LD) distance generally produced higher accuracy than that based on equal kilobase distance or tagging.
BMC Proc 2007
PMID:Association mapping of susceptibility loci for rheumatoid arthritis. 1846 94

As genome-wide association studies grow in popularity for the identification of genetic factors for common and rare diseases, analytical methods to comb through large numbers of genetic variants efficiently to identify disease association are increasingly in demand. We have developed a pattern-based data-mining approach to discover unlinked multilocus genetic effects for complex disease and to detect genotype x phenotype/genotype x environment interactions. On a densely mapped chromosome 18 data set for rheumatoid arthritis that was made available by Genetic Analysis Workshop 15, this method detected two potential two-locus associations as well as a putative two-locus gene x gender interaction.
BMC Proc 2007
PMID:Pattern-based mining strategy to detect multi-locus association and gene x environment interaction. 1846 5

We performed a multipoint linkage analysis for rheumatoid arthritis (RA) using high-density single-nucleotide polymorphism (SNP) data for chromosome 6 and chromosome 21 using Genetic Analysis Workshop 15 (GAW15) data. These regions were previously shown to have high LOD scores, not accounting for linkage disequilibrium (LD). We propose three novel methods to control for LD in a linkage analysis: allow for LD between markers using graphical modeling, eliminate high-LD markers by principal-component analysis (PCA) using haplotype data, and eliminate high-LD markers by PCA using genotype data. All three novel methods were compared to the previously published SNPLINK high-LD elimination method. Although all four methods verified the previous results, differences in linkage peak height and position were observed across methods. Additional work is required to further understand the effects of LD on linkage results and explore LD control methodology.
BMC Proc 2007
PMID:Analysis of high-density single-nucleotide polymorphism data: three novel methods that control for linkage disequilibrium between markers in a linkage analysis. 1846 6


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