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Data for Genetic Analysis Workshop 16 Problem 1, association analysis of rheumatoid arthritis data.

ABSTRACT: For Genetic Analysis Workshop 16 Problem 1, we provided data for genome-wide association analysis of rheumatoid arthritis. Single-nucleotide polymorphism (SNP) genotype data were provided for 868 cases and 1194 controls that had been assayed using an Illumina 550 k platform. In addition, phenotypic data were provided from genotyping DRB1 alleles, which were classified according to the rheumatoid arthritis shared epitope, levels of anti-cyclic citrullinated peptide, and levels of rheumatoid factor IgM. Several questions could be addressed using the data, including analysis of genetic associations using single SNPs or haplotypes, as well as gene-gene and genetic analysis of SNPs for qualitative and quantitative factors.

SUBMITTER: Amos CI

PROVIDER: S-EPMC2795916 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature

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Data for Genetic Analysis Workshop 16 Problem 1, association analysis of rheumatoid arthritis data.

Amos Christopher I CI Chen Wei Vivien WV Seldin Michael F MF Remmers Elaine F EF Taylor Kimberly E KE Criswell Lindsey A LA Lee Annette T AT Plenge Robert M RM Kastner Daniel L DL Gregersen Peter K PK

BMC proceedings 20091215

For Genetic Analysis Workshop 16 Problem 1, we provided data for genome-wide association analysis of rheumatoid arthritis. Single-nucleotide polymorphism (SNP) genotype data were provided for 868 cases and 1194 controls that had been assayed using an Illumina 550 k platform. In addition, phenotypic data were provided from genotyping DRB1 alleles, which were classified according to the rheumatoid arthritis shared epitope, levels of anti-cyclic citrullinated peptide, and levels of rheumatoid facto ...[more]

PMID: 20018009

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A pathway analysis applied to Genetic Analysis Workshop 16 genome-wide rheumatoid arthritis data.

Project description:The identification of several hundred genomic regions affecting disease risk has proven the ability of genome-wide association studies have proven their ability to identify genetic contributors to disease. Currently, single-nucleotide polymorphism (SNP) association analysis is the most widely used method of genome-wide association data, but recent research shows that multi-marker tests of association may provide greater power, especially when more than one mutation is present within a gene and the mutations are in low linkage disequilibrium with each other. Here we use a multi-marker association test based on regression to SNPs located within known genes to obtain a gene-level score of association. We then perform pathway analysis using this score as a measure of gene importance. We use two tests of pathway enrichment - a binomial test and a random set method. By utilizing publicly available gene and pathway information, we identify B cell, cytokine and inflammation response, and antigen presentation pathways as being associated with rheumatoid arthritis. These results confirm known biological mechanisms for auto-immunity disorders, of which rheumatoid arthritis is one.

| S-EPMC2795995 | biostudies-literature

Data for Genetic Analysis Workshop (GAW) 15 Problem 2, genetic causes of rheumatoid arthritis and associated traits.

Project description: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.

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A combinatorial approach for detecting gene-gene interaction using multiple traits of Genetic Analysis Workshop 16 rheumatoid arthritis data.

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Replication of recently identified associated single-nucleotide polymorphisms from six autoimmune diseases in Genetic Analysis Workshop 16 rheumatoid arthritis data.

Project description:Many autoimmune diseases share similar underlying pathology and have a tendency to cluster within families, giving rise to the concept of shared susceptibility genes among them. In the Genetic Analysis Workshop 16 rheumatoid arthritis (RA) data we sought to replicate the genetic association between single-nucleotide polymorphisms (SNPs) identified in recent genome-wide association studies (GWAS) on RA and five other autoimmune diseases. We identified 164 significantly associated non-HLA SNPs (p < 10-5) from 16 GWAS and 13 candidate gene studies on six different autoimmune diseases, including RA, systemic lupus erythematosus, type 1 diabetes, Crohn disease, multiple sclerosis, and celiac disease. Using both direct and imputation-based association test, we replicated 16 shared susceptibility regions involving RA and at least one of the other autoimmune diseases. We also identified hidden population structure within cases and controls in Genetic Analysis Workshop 16 RA data and assessed the effect of population structure on the shared autoimmunity regions. Because multiple autoimmune diseases share common genetic origin, these could be areas of immense interest for further genetic and clinical association studies.

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Case-control genome-wide association study of rheumatoid arthritis from Genetic Analysis Workshop 16 using penalized orthogonal-components regression-linear discriminant analysis.

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| S-EPMC2795913 | biostudies-literature

A comparison of case-only designs for detecting gene x gene interaction in rheumatoid arthritis using genome-wide case-control data in Genetic Analysis Workshop 16.

Project description:We compare and contrast case-only designs for detecting gene x gene (G x G) interaction in rheumatoid arthritis (RA) using the genome-wide data provided by Genetic Analysis Workshop 16 Problem 1. Logistic as well as novel multinomial and proportional odds models that do not depend on the specification of additive or dominant models for susceptibility loci were applied to the case-only sample. We identified 519 significant interactions (p < 1 x 10-4 in at least one test). All methods detected unique significant interactions; 169 were common to more than one model and only 21 were common to all models. Results emphasize that categorization of the genetic variables and choice of regression model are critical and hugely influential in the identification of G x G. Porportional odds and multinomial methods provide new tools for identification of G x G interactions.

| S-EPMC2795975 | biostudies-literature

Genetic association analysis using weighted false discovery rate approach on Genetic Analysis Workshop 18 data.

Project description:In a genome-wide association study, association between disease trait and hundreds of thousands of genetic markers are tested. Several methods have been proposed to control the false discovery rate in such high-throughput data to adjust for multiple hypotheses testing. For Genetic Analysis Workshop 18, we applied the method of false discovery rate control with p value weighting on family-based association tests on quantitative trait to detect association between single-nucleotide polymorphisms (SNPs) and mean arterial pressure. This method can improve statistical power by incorporating independent but relevant information about the research objective. Using the real genetic and phenotype data of chromosome 3 from Genetic Analysis Workshop 18, 1 SNP from gene CACNA2D3 was found to have significant association with mean arterial pressure.

| S-EPMC4143671 | biostudies-literature

Multistage analysis strategies for genome-wide association studies: summary of group 3 contributions to Genetic Analysis Workshop 16.

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Discovery of new candidate genes for rheumatoid arthritis through integration of genetic association data with expression pathway analysis

Project description:Purpose: The goal of the study was to integrate verified signals from previous genetic association studies with gene expression and pathway analysis for discovery of new candidate genes and signalling networks, relevant for rheumatoid arthritis (RA). Method:RNA-seq based expression analysis of 377 genes from previously verified RA-associated loci was performed in blood cells from 5 newly diagnosed, non-treated RA patients, 7 patients with treated RA and 12 healthy controls. Differentially expressed genes sharing a similar expression pattern in treated and untreated RA sub-groups were selected for pathway analysis. A set of “connector” genes derived from pathway analysis was then tested for differential expression in the initial discovery cohort. Results: 11 qualifying genes were selected for pathway analysis and grouped into 2 evidence-based functional networks, containing 29 and 27 additional “connector” molecules. The expression of genes, corresponding to connector molecules was then tested in the initial RNA-seq data. 3 genes showed similar expression difference in both treated and non-treated RA patients and additional nine genes were differentially expressed in at least one patients’ group compared to healthy controls. Conclusion: Integration of RNA-seq data with findings from association studies, and consequent pathway analysis implicate new candidate genes in the pathogenesis of RA.

2016-11-24 | GSE90081 | GEO

Discovery of new candidate genes for rheumatoid arthritis through integration of genetic association data with expression pathway analysis.

Project description:BackgroundHere we integrate verified signals from previous genetic association studies with gene expression and pathway analysis for discovery of new candidate genes and signaling networks, relevant for rheumatoid arthritis (RA).MethodRNA-sequencing-(RNA-seq)-based expression analysis of 377 genes from previously verified RA-associated loci was performed in blood cells from 5 newly diagnosed, non-treated patients with RA, 7 patients with treated RA and 12 healthy controls. Differentially expressed genes sharing a similar expression pattern in treated and untreated RA sub-groups were selected for pathway analysis. A set of "connector" genes derived from pathway analysis was tested for differential expression in the initial discovery cohort and validated in blood cells from 73 patients with RA and in 35 healthy controls.ResultsThere were 11 qualifying genes selected for pathway analysis and these were grouped into two evidence-based functional networks, containing 29 and 27 additional connector molecules. The expression of genes, corresponding to connector molecules was then tested in the initial RNA-seq data. Differences in the expression of ERBB2, TP53 and THOP1 were similar in both treated and non-treated patients with RA and an additional nine genes were differentially expressed in at least one group of patients compared to healthy controls. The ERBB2, TP53. THOP1 expression profile was successfully replicated in RNA-seq data from peripheral blood mononuclear cells from healthy controls and non-treated patients with RA, in an independent collection of samples.ConclusionIntegration of RNA-seq data with findings from association studies, and consequent pathway analysis implicate new candidate genes, ERBB2, TP53 and THOP1 in the pathogenesis of RA.

| S-EPMC5288892 | biostudies-literature

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