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      A genome-wide association study suggests contrasting associations in ACPA-positive versus ACPA-negative rheumatoid arthritis

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      1 , 2 , 3 , 2 , 4 , 5 , 1 , 4 , 1 , the Epidemiological Investigation of Rheumatoid Arthritis (EIRA) Study Group
      Annals of the Rheumatic Diseases
      BMJ Group

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          Abstract

          Background

          Rheumatoid arthritis (RA) can be divided into two major subsets based on the presence or absence of antibodies to citrullinated peptide antigens (ACPA). Until now, data from genome-wide association studies (GWAS) have only been published from ACPA-positive subsets of RA or from studies that have not separated the two subsets. The aim of the current study is to provide and compare GWAS data for both subsets.

          Methods

          and results GWAS using the Illumina 300K chip was performed for 774 ACPA-negative patients with RA, 1147 ACPA-positive patients with RA and 1079 controls from the Swedish population-based case–control study EIRA. Imputation was performed which allowed comparisons using 1 723 056 single nucleotide polymorphisms (SNPs). No SNP achieved genome-wide significance (2.9 × 10 –8) in the comparison between ACPA-negative RA and controls. A case–case association study was then performed between ACPA-negative and ACPA-positive RA groups. The major difference in this analysis was in the HLA region where 768 HLA SNPs passed the threshold for genome-wide significance whereas additional contrasting SNPs did not reach genome-wide significance. However, one SNP close to the RPS12P4 locus in chromosome 2 reached a p value of 2 × 10 6 and this locus can thus be considered as a tentative candidate locus for ACPA-negative RA.

          Conclusions

          ACPA-positive and ACPA-negative RA display significant risk allele frequency differences which are mainly confined to the HLA region. The data provide further support for distinct genetic aetiologies of RA subsets and emphasise the need to consider them separately in genetic as well as functional studies of this disease.

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          Most cited references26

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          Characterizing the quantitative genetic contribution to rheumatoid arthritis using data from twins.

          Twin concordance data for rheumatoid arthritis (RA) on their own provide only limited insight into the relative genetic and environmental contribution to the disease. We applied quantitative genetic methods to assess the heritability of RA and to examine for evidence of differences in the genetic contribution according to sex, age, and clinical disease characteristics. Data were analyzed from 2 previously published nationwide studies of twins with RA conducted in Finland and the United Kingdom. Heritability was assessed by variance components analysis. Differences in the genetic contribution by sex, age, age at disease onset, and clinical characteristics were examined by stratification. The power of the twin study design to detect these differences was examined through simulation. The heritability of RA was 65% (95% confidence interval [95% CI] 50-77) in the Finnish data and 53% (95% CI 40-65) in the UK data. There was no significant difference in the strength of the genetic contribution according to sex, age, age at onset, or disease severity subgroup. Both study designs had power to detect a contribution of at least 40% from the common family environment, and a difference in the genetic contribution of at least 50% between subgroups. Genetic factors have a substantial contribution to RA in the population, accounting for approximately 60% of the variation in liability to disease. Although tempered by power considerations, there is no evidence in these twin data that the overall genetic contribution to RA differs by sex, age, age at disease onset, and disease severity.
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            STAT4 and the risk of rheumatoid arthritis and systemic lupus erythematosus.

            Rheumatoid arthritis is a chronic inflammatory disease with a substantial genetic component. Susceptibility to disease has been linked with a region on chromosome 2q. We tested single-nucleotide polymorphisms (SNPs) in and around 13 candidate genes within the previously linked chromosome 2q region for association with rheumatoid arthritis. We then performed fine mapping of the STAT1-STAT4 region in a total of 1620 case patients with established rheumatoid arthritis and 2635 controls, all from North America. Implicated SNPs were further tested in an independent case-control series of 1529 patients with early rheumatoid arthritis and 881 controls, all from Sweden, and in a total of 1039 case patients and 1248 controls from three series of patients with systemic lupus erythematosus. A SNP haplotype in the third intron of STAT4 was associated with susceptibility to both rheumatoid arthritis and systemic lupus erythematosus. The minor alleles of the haplotype-defining SNPs were present in 27% of chromosomes of patients with established rheumatoid arthritis, as compared with 22% of those of controls (for the SNP rs7574865, P=2.81x10(-7); odds ratio for having the risk allele in chromosomes of patients vs. those of controls, 1.32). The association was replicated in Swedish patients with recent-onset rheumatoid arthritis (P=0.02) and matched controls. The haplotype marked by rs7574865 was strongly associated with lupus, being present on 31% of chromosomes of case patients and 22% of those of controls (P=1.87x10(-9); odds ratio for having the risk allele in chromosomes of patients vs. those of controls, 1.55). Homozygosity of the risk allele, as compared with absence of the allele, was associated with a more than doubled risk for lupus and a 60% increased risk for rheumatoid arthritis. A haplotype of STAT4 is associated with increased risk for both rheumatoid arthritis and systemic lupus erythematosus, suggesting a shared pathway for these illnesses. Copyright 2007 Massachusetts Medical Society.
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              • Abstract: found
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              TRAF1-C5 as a risk locus for rheumatoid arthritis--a genomewide study.

              Rheumatoid arthritis has a complex mode of inheritance. Although HLA-DRB1 and PTPN22 are well-established susceptibility loci, other genes that confer a modest level of risk have been identified recently. We carried out a genomewide association analysis to identify additional genetic loci associated with an increased risk of rheumatoid arthritis. We genotyped 317,503 single-nucleotide polymorphisms (SNPs) in a combined case-control study of 1522 case subjects with rheumatoid arthritis and 1850 matched control subjects. The patients were seropositive for autoantibodies against cyclic citrullinated peptide (CCP). We obtained samples from two data sets, the North American Rheumatoid Arthritis Consortium (NARAC) and the Swedish Epidemiological Investigation of Rheumatoid Arthritis (EIRA). Results from NARAC and EIRA for 297,086 SNPs that passed quality-control filters were combined with the use of Cochran-Mantel-Haenszel stratified analysis. SNPs showing a significant association with disease (P<1x10(-8)) were genotyped in an independent set of case subjects with anti-CCP-positive rheumatoid arthritis (485 from NARAC and 512 from EIRA) and in control subjects (1282 from NARAC and 495 from EIRA). We observed associations between disease and variants in the major-histocompatibility-complex locus, in PTPN22, and in a SNP (rs3761847) on chromosome 9 for all samples tested, the latter with an odds ratio of 1.32 (95% confidence interval, 1.23 to 1.42; P=4x10(-14)). The SNP is in linkage disequilibrium with two genes relevant to chronic inflammation: TRAF1 (encoding tumor necrosis factor receptor-associated factor 1) and C5 (encoding complement component 5). A common genetic variant at the TRAF1-C5 locus on chromosome 9 is associated with an increased risk of anti-CCP-positive rheumatoid arthritis. Copyright 2007 Massachusetts Medical Society.

                Author and article information

                Journal
                Ann Rheum Dis
                annrheumdis
                ard
                Annals of the Rheumatic Diseases
                BMJ Group (BMA House, Tavistock Square, London, WC1H 9JR )
                0003-4967
                1468-2060
                February 2011
                14 December 2010
                : 70
                : 2
                : 259-265
                Affiliations
                [1 ]Rheumatology Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
                [2 ]The Genome Institute of Singapore, Singapore
                [3 ]The Institute for Human Genetics and Department of Laboratory Medicine, University of California, San Francisco School of Medicine, San Francisco, California, USA
                [4 ]The Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
                [5 ]The Unit for Clinical Immunology, Uppsala University, Uppsala, Sweden
                Author notes
                Correspondence to Dr Seielstad, at the Genome Institute of Singapore, 60 Biopolis St, Singapore 138672; seielstadm@ 123456gis.a-star.edu.sg

                LP and MS contributed equally to this work.

                Article
                annrheumdis126821
                10.1136/ard.2009.126821
                3015094
                21156761
                8e5f7d90-0b38-4495-ae2d-189dce23d2d4
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode.

                History
                : 26 July 2010
                Categories
                Clinical and Epidemiological Research
                1506
                Extended report
                Custom metadata
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                Immunology
                Immunology

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