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A159: The Autoimmune Genetic Architecture of Childhood Onset Rheumatoid Arthritis
Author(s) -
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.
Publication year - 2014
Publication title -
arthritis and rheumatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.106
H-Index - 314
eISSN - 2326-5205
pISSN - 2326-5191
DOI - 10.1002/art.38585
Subject(s) - rheumatoid arthritis , genetic architecture , medicine , autoimmune disease , immunology , pediatrics , population , environmental health , antibody , quantitative trait locus
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 (P FDR ) are reported to account for the actual number of tests computed. Results: SNP rs3129769, near HLA DRB1 was the most significantly associated (P FDR <3×10 ‐25 ), and is in linkage disequilibrium with the HLA DRB1 SNP reported in RA (rs660895, P FDR <1×10 ‐24 ). Outside the HLA region, 28 regions had ≥1 SNP meeting genome‐wide significance (P FDR <0.05). The best signal of association was on 22q13 (rs9610687, OR = 0.57, P FDR < 0.0002) near IL2RB , followed by the PTPN22 locus (rs6679677, OR = 1.83, P FDR < 0.0005), an intergenic SNP on chromosome 4 (rs970036, OR = 1.78, P FDR < 0.002) and an intronic SNP (19p13, rs3787016 P FDR < 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 P FDR < 0.003), IRF5 (rs4731531 P FDR < 0.006) and CCR6 (rs11575078 P FDR < 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.