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Sökning: WFRF:(Natividad MD)

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1.
  • Bossini-Castillo, Lara, et al. (författare)
  • Confirmation of TNIP1 but not RHOB and PSORS1C1 as systemic sclerosis risk factors in a large independent replication study
  • 2013
  • Ingår i: Annals of the Rheumatic Diseases. - British Medical Association. - 1468-2060. ; 72:4, s. 602-607
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction A recent genome-wide association study in European systemic sclerosis (SSc) patients identified three loci (PSORS1C1, TNIP1 and RHOB) as novel genetic risk factors for the disease. The aim of this study was to replicate the previously mentioned findings in a large multicentre independent SSc cohort of Caucasian ancestry. Methods 4389 SSc patients and 7611 healthy controls from different European countries and the USA were included in the study. Six single nucleotide polymorphisms (SNP): rs342070, rs13021401 (RHOB), rs2233287, rs4958881, rs3792783 (TNIP1) and rs3130573 (PSORS1C1) were analysed. Overall significance was calculated by pooled analysis of all the cohorts. Haplotype analyses and conditional logistic regression analyses were carried out to explore further the genetic structure of the tested loci. Results Pooled analyses of all the analysed SNPs in TNIP1 revealed significant association with the whole disease (rs2233287 p(MH) = 1.94 x 10(-4), OR 1.19; rs4958881 p(MH) = 3.26 x 10(-5), OR 1.19; rs3792783 p(MH) = 2.16 x 10(-4), OR 1.19). These associations were maintained in all the subgroups considered. PSORS1C1 comparison showed association with the complete set of patients and all the subsets except for the anti-centromere-positive patients. However, the association was dependent on different HLA class II alleles. The variants in the RHOB gene were not associated with SSc or any of its subsets. Conclusions These data confirmed the influence of TNIP1 on an increased susceptibility to SSc and reinforced this locus as a common autoimmunity risk factor.
2.
  • López-Isac, Elena, et al. (författare)
  • Brief Report : IRF4 Newly Identified as a Common Susceptibility Locus for Systemic Sclerosis and Rheumatoid Arthritis in a Cross-Disease Meta-Analysis of Genome-Wide Association Studies
  • 2016
  • Ingår i: Arthritis & Rheumatology. - John Wiley and Sons Inc.. - 2326-5191. ; 68:9, s. 2338-2344
  • Tidskriftsartikel (refereegranskat)abstract
    • Objective: Systemic sclerosis (SSc) and rheumatoid arthritis (RA) are autoimmune diseases that have similar clinical and immunologic characteristics. To date, several shared SSc–RA genetic loci have been identified independently. The aim of the current study was to systematically search for new common SSc–RA loci through an interdisease meta–genome-wide association (meta-GWAS) strategy. Methods: The study was designed as a meta-analysis combining GWAS data sets of patients with SSc and patients with RA, using a strategy that allowed identification of loci with both same-direction and opposite-direction allelic effects. The top single-nucleotide polymorphisms were followed up in independent SSc and RA case–control cohorts. This allowed an increase in the sample size to a total of 8,830 patients with SSc, 16,870 patients with RA, and 43,393 healthy controls. Results: This cross-disease meta-analysis of the GWAS data sets identified several loci with nominal association signals (P < 5 × 10−6) that also showed evidence of association in the disease-specific GWAS scans. These loci included several genomic regions not previously reported as shared loci, as well as several risk factors that were previously found to be associated with both diseases. Follow-up analyses of the putatively new SSc–RA loci identified IRF4 as a shared risk factor for these 2 diseases (Pcombined = 3.29 × 10−12). Analysis of the biologic relevance of the known SSc–RA shared loci identified the type I interferon and interleukin-12 signaling pathways as the main common etiologic factors. Conclusion: This study identified a novel shared locus, IRF4, for the risk of SSc and RA, and highlighted the usefulness of a cross-disease GWAS meta-analysis strategy in the identification of common risk loci.
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