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

The presence of linkage disequilibrium violates the underlying assumption of linkage equilibrium in most traditional multipoint linkage approaches. Studies have shown that such violation leads to bias in qualitative trait linkage analysis when parental genotypes are unavailable. Appropriate handling of marker linkage disequilibrium can avoid such false positive evidence. Using the rheumatoid arthritis simulated data from Genetic Analysis Workshop 15, we examined and compared the following three approaches to handle linkage disequilibrium among dense markers in both qualitative and quantitative trait linkage analyses: a simple algorithm; SNPLINK, methods for marker selection; and MERLIN-LD, a method for modeling linkage disequilibrium by creating marker clusters. In analysis ignoring linkage disequilibrium between markers, we observed LOD score inflation only in the affected sib-pair linkage analysis without parental genotypes; no such inflation was present in the quantitative trait locus linkage analysis with severity as our phenotype with or without parental genotypes. Using methods to model or adjust for linkage disequilibrium, we found a substantial reduction of inflation of LOD score in affected sib-pair linkage analysis. Greater LOD score reduction was observed by decreasing the amount of tolerable linkage disequilibrium among markers selected or marker clusters using MERLIN-LD; the latter approach showed most reduction. SNPLINK performed better with selected markers based on the D' measure of linkage disequilibrium as opposed to the r2 measure and outperformed the simple algorithm. Our findings reiterate the necessity of properly handling dense markers in linkage analysis, especially when parental genotypes are unavailable.
BMC Proc 2007
PMID:Handling linkage disequilibrium in linkage analysis using dense single-nucleotide polymorphisms. 1846 7

A number of autoimmune and other diseases have well established HLA associations; in many cases there is strong evidence for the direct involvement of the HLA class II peptide-presenting antigens, e.g., HLA DR-DQ for type 1 diabetes (T1D) and HLA-DR for rheumatoid arthritis (RA). The involvement of additional HLA region genes in the disease process is implicated in these diseases. We have developed a model-free approach to detect these additional disease genes using genotype data; the conditional genotype method (CGM) and overall conditional genotype method (OCGM) use all patient and control data and do not require haplotype estimation. Genotypes at marker genes in the HLA region are stratified and their expected values are determined in a way that removes the effects of linkage disequilibrium (LD) with the peptide-presenting HLA genes directly involved in the disease. A statistic has been developed under the null hypothesis of no additional disease genes in the HLA region for the OCGM method and was applied to the Genetic Analysis Workshop 15 simulated data set of Problem 3, which mimics RA (answers were known). In addition to the primary effect of the HLA DR locus, the effects of the other two HLA region simulated genes involved in disease were detected (gene C, 0 cM from DR, increases RA risk only in women; and gene D, 5.12 cM from DR, rare allele increases RA risk five-fold). No false negatives were found. Power calculations were performed.
BMC Proc 2007
PMID:Conditional genotype analysis: detecting secondary disease loci in linkage disequilibrium with a primary disease locus. 1846 9

We examined the potential gene x gene interactions and gene x smoking interactions in rheumatoid arthritis (RA) using the candidate gene data sets provided by Genetic Analysis Workshop 15 Problem 2. The multifactor dimensionality reduction (MDR) method was used to test gene x gene interactions among candidate genes. The case-only sample was used to test gene x smoking interactions. The best predictive model was the single-locus model with single-nucleotide polymorphism (SNP) rs2476601 in gene PTPN22. However, no clear gene x gene interaction was identified. Substantial departure from multiplicativity was observed between smoking and SNPs in genes CTLA4, PADI4, MIF, and SNPs on chromosome 5 and one haplotype of PTPN22. The strongest evidence of association was identified between the PTPN22 gene and RA status, which was consistently detected in single SNP association, gene x gene interaction and gene x smoking interaction analyses.
BMC Proc 2007
PMID:Evaluating gene x gene and gene x smoking interaction in rheumatoid arthritis using candidate genes in GAW15. 1846 13

Rheumatoid arthritis (RA) is an autoimmune disease with a moderately strong genetic component. Previous linkage and candidate gene studies have identified several regions that predispose to RA, including the HLA-DRB1 and PTPN22. We conducted genome-wide linkage analysis with 128 affected individuals from 60 families in a Canadian cohort that were genotyped using the Illumina linkage panel and genome-wide association analysis with 158 affected individuals from the same cohort that were genotyped using the Affymetrix 100 K platform. Multipoint nonparametric linkage scan revealed three linkage peaks with LOD scores greater than 1.5. We also identified 13 significantly associated SNPs at the genome-wide level of 0.05 after Bonferroni adjustment for multiple testing. Several of the significantly associated SNPs are located close to previously identified linkage regions, but not in the linkage peaks identified in the same cohort. We could not replicate association with HLA-DRB1 and PTPN22. Our results indicate that high coverage and sufficient sample size are crucial for the success of genome-wide association studies.
BMC Proc 2007
PMID:Genome-wide linkage and association analysis of rheumatoid arthritis in a Canadian population. 1846 15

Although single chi-square analysis of the North American Rheumatoid Arthritis Consortium (NARAC) data identifies many single-nucleotide polymorphisms (SNPs) with p-values less than 0.05, none remain significant after Bonferroni correction. In contrast, CHROMSCAN evades heavy Bonferroni correction and auto-correlation between SNPs by using composite likelihood to model association across all markers in a region and permutation to assess significance. Analysis by CHROMSCAN identifies a 36-kb interval that includes the most significant SNP (msSNP) observed in a 10-Mb target suggested by linkage. Unexpectedly, stratification by gender and age of onset shows that association evidence comes almost entirely from females with age of onset less than 40. Combining evidence from a meta-analysis of linkage studies and three subsets of the NARAC data provides significant evidence for a determinant of rheumatoid arthritis in a 36-kb interval and illustrates the principle that estimates of location and its information are more powerful than estimates of p-values alone.
BMC Proc 2007
PMID:Mapping a gene for rheumatoid arthritis on chromosome 18q21. 1846 14

Rheumatoid arthritis (RA) is a disorder with important public health implications. It is possible that there are clinically distinctive subtypes of the disorder with different genetic etiologies. We used the data provided to the participants in the Genetic Analysis Workshop 15 to evaluate and describe clinically based subgroups and their genetic associations with single-nucleotide polymorphisms (SNPs) on chromosome 6, which harbors the HLA region. Detailed two- and three-SNP haplotype analyses were conducted in the HLA region. We used demographic, clinical self-report, and biomarker data from the entire sample (n = 8477) to identify and characterize the subgroups. We did not use the RA diagnosis itself in the identification of the subgroups. Nuclear families (715 families, 1998 individuals) were used to examine the genetic association with the HLA region. We found five distinct subgroups in the data. The first comprised unaffected family members. Cluster 2 was a mix of affected and unaffected in which patients endorsed symptoms not corroborated by physicians. Clusters 3 through 5 represented a severity continuum in RA. Cluster 5 was characterized by early onset severe disease. Cluster 2 showed no association on chromosome 6. Clusters 3 through 5 showed association with 17 SNPs on chromosome 6. In the HLA region, Cluster 3 showed single-, two-, and three-SNP association with the centromeric side of the region in an area of linkage disequilibrium. Cluster 5 showed both single- and two-SNP association with the telomeric side of the region in a second area of linkage disequilibrium. It will be important to replicate the subgroup structure and the association findings in an independent sample.
BMC Proc 2007
PMID:Empirically derived subgroups in rheumatoid arthritis: association with single-nucleotide polymorphisms on chromosome 6. 1846 17

Haplotype association analysis based on arbitrarily chosen markers might lower statistical power because of the larger number of degrees of freedom caused by irrelevant makers.On the other hand, an exhaustive search for all possible combinations of markers for haplotype analysis is computationally expensive for genome-wide association analysis.To improve power, we applied our recently developed sequential haplotype scan method to case-control data for rheumatoid arthritis, including the PTPN22 candidate gene on chromosome 1p and the association mapping data on chromosome 18q, from the Genetic Analysis Workshop 15. The results showed that our new approach is at least as powerful as the traditional single-locus analysis and sometimes can be more powerful.
BMC Proc 2007
PMID:Application of sequential haplotype scan methods to case-control data. 1846 18

Clustering of related haplotypes in haplotype-based association mapping has the potential to improve power by reducing the degrees of freedom without sacrificing important information about the underlying genetic structure. We have modified a generalized linear model approach for association analysis by incorporating a density-based clustering algorithm to reduce the number of coefficients in the model. Using the GAW 15 Problem 3 simulated data, we show that our novel method can substantially enhance power to detect association with the binary rheumatoid arthritis (RA) phenotype at the HLA-DRB1 locus on chromosome 6. In contrast, clustering did not appreciably improve performance at locus D, perhaps a consequence of a rare susceptibility allele and of the overwhelming effect of HLA-DRB1/locus C, 5 cM distal. Optimization of parameters governing the clustering algorithm identified a set of parameters that delivered nearly ideal performance in a variety of situations. The cluster-based score test was valid over a wide range of haplotype diversity, and was robust to severe departures from Hardy-Weinberg equilibrium encountered near HLA-DRB1 in RA case-control samples.
BMC Proc 2007
PMID:Density-based clustering in haplotype analysis for association mapping. 1846 24

Related cases may be included in case-control association studies if correlations between related individuals due to identity-by-descent (IBD) sharing are taken into account. We derived a framework to test for association in a case-control design including affected sibships and unrelated controls. First, a corrected variance for the allele frequency difference between cases and controls was directly calculated or estimated in two ways on the basis of the fixation index FST and the inbreeding coefficient. Then the correlation-corrected association test including controls and affected sibs was carried out. We applied the three strategies to 20 candidate genes on the Genetic Analysis Workshop 15 rheumatoid arthritis data and to 9187 single-nucleotide polymorphisms of replicate one of the Genetic Analysis Workshop 15 simulated data with knowledge of the "answers". The three strategies used to correct for correlation give only minor differences in the variance estimates and yield an almost correct type I error rate for the association tests. Thus, all strategies considered to correct the variance performed quite well.
BMC Proc 2007
PMID:Case-control studies with affected sibships. 1846 26

For Genetic Analysis Workshop 15 Problem 2, we organized data from several ongoing studies designed to identify genetic and environmental risk factors for rheumatoid arthritis. Data were derived from the North American Rheumatoid Arthritis Consortium (NARAC), collaboration among Canadian researchers, the European Consortium on Rheumatoid Arthritis Families (ECRAF), and investigators from Manchester, England. All groups used a common standard for defining rheumatoid arthritis, but NARAC also further selected for a more severe phenotype in the probands. Genotyping and family structures for microsatellite-based linkage analysis were provided from all centers. In addition, all centers but ECRAF have genotyped families for linkage analysis using SNPs and these data were additionally provided. NARAC also had additional data from a dense genotyping analysis of a region of chromosome 18 and results from candidate gene studies, which were provided. Finally, smoking influences risk for rheumatoid arthritis, and data were provided from the NARAC study on this behavior as well as some additional phenotypes measuring severity. Several questions could be evaluated using the data that were provided. These include comparing linkage analysis using single-nucleotide polymorphisms versus microsatellites and identifying credible regions of linkage outside the HLA region on chromosome 6p13, which has been extensively documented; evaluating the joint effects of smoking with genetic factors; and identifying more homogenous subsets of families for whom genetic susceptibility might be stronger, so that linkage and association studies may be more efficiently conducted.
BMC Proc 2007
PMID:Data for Genetic Analysis Workshop (GAW) 15 Problem 2, genetic causes of rheumatoid arthritis and associated traits. 1846 27


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