A159: The Autoimmune Genetic Architecture of Childhood Onset Rheumatoid Arthritis. Issue 11 (March 2014)

• Record Type: Journal Article
• Authors: Prahalad, Sampath; Marion, Miranda C.; Cobb, Joanna; Sudman, Marc; Hinks, Anne; Pichavant, Mina; Ponder, Lori; Reed, Ann M.; Wallace, Carol; Becker, Mara L.; Yeung, Rae S. M.; Rosenberg, Alan M.; Punaro, Marilynn G.; Mellins, Elizabeth D.; Nelson, J. Lee; Videm, Vibeke; Rygg, Marite; Nordal, Ellen; Brown, Matthew A.; Cutler, David; Bohnsack, John F.; Thomson, Wendy; Thompson, Susan D.; Langefeld, Carl D.
• Is Part Of: Arthritis & rheumatology. Volume 66:Issue 11(2014)supplement
• Page Start: S205
• Page End: S206
• Publication Date: 2014-03
• Subjects: Arthritis -- Periodicals; Rheumatism -- Periodicals; 616.72
• DOI: 10.1002/art.38585
• Languages: English
• ISSNs: 2326-5191

Abstract

Abstract : Background/Purpose: Genome‐wide association studies have identified susceptibility loci for many autoimmune diseases, including rheumatoid arthritis (RA) and juvenile idiopathic arthritis (JIA). About 5% of children with JIA have rheumatoid factor positive arthritis that is phenotypically similar to adult seropositive RA, thus representing childhood onset RA (CORA). To understand the genetic architecture of CORA risk relative to other autoimmune diseases, we genotyped CORA cases and controls on the Immunochip, a custom array designed by the Immunochip Consortium to fine map autoimmune disease‐associated loci shared across 11 autoimmune phenotypes. Methods: Genotyping was completed on 340 CORA cases (mean onset age: 10.2 ± 4.2 yrs) and 11624 controls. Standard SNP and sample QC was performed (e.g., removing samples with call rate <98%, admixture outliers). To test for SNP association with CORA a logistic regression model was computed with Caucasian admixture proportions (ADMIXTURE) as covariates (SNPLash). False discovery rate (FDR) adjusted p‐values (PFDR ) are reported to account for the actual number of tests computed. Results: SNP rs3129769, near HLA DRB1 was the most significantly associated (PFDR <3×10 ‐25 ), and is in linkage disequilibrium with the HLA DRB1 SNP reported in RA (rs660895, PFDR <1×10 ‐24 ). Outside the HLA region, 28 regions had ≥1 SNP meeting genome‐wide significance (PFDR <0.05). The best signal of association was on 22q13 (rs9610687, OR = 0.57, PFDR < 0.0002) near IL2RB, followed by the PTPN22 locus (rs6679677, OR = 1.83, PFDR < 0.0005), an intergenic SNP on chromosome 4 (rs970036, OR = 1.78, PFDR < 0.002) and an intronic SNP (19p13, rs3787016 PFDR < 0.002) in the POLR2E gene, which encodes a subunit of RNA polymerase II. Some loci which have previously been implicated in RA had evidence of association including MMEL (rs751358 PFDR < 0.003), IRF5 (rs4731531 PFDR < 0.006) and CCR6 (rs11575078 PFDR < 0.03). In addition using the FDR‐based threshold we identified several potential SNPs (rs10918214, 1q23; rs28491312, 14q31) not previously implicated in RA. Conclusion: Immunochip analysis of the largest cohort of CORA investigated to date has confirmed the HLA DRB1 association and identified several other loci associated with CORA. Comparisons among loci associated with CORA and those identified in RA and other forms of JIA are underway and will help delineate the unique genetic factors for CORA susceptibility.