MétaCan
Menu
Back to cohort
Record W2092779039 · doi:10.1002/art.38585

A159: The Autoimmune Genetic Architecture of Childhood Onset Rheumatoid Arthritis

2014· article· en· W2092779039 on OpenAlexaff
Sampath Prahalad, Miranda C. Marion, Joanna Cobb, Marc Sudman, Anne Hinks, Mina Pichavant, Lori Ponder, Ann M. Reed, Carol A. Wallace, Mara L. Becker, Rae S. M. Yeung, Alan Rosenberg, Marilynn Punaro, Elizabeth Mellins, J. Lee Nelson, Vibeke Videm, Marite Rygg, Ellen Nordal, Matthew A. Brown, David J. Cutler, John F. Bohnsack, Wendy Thomson, Susan D. Thompson, Carl D. Langefeld

Bibliographic record

VenueArthritis & Rheumatology · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsUniversity of SaskatchewanHospital for Sick ChildrenUniversity of Toronto
FundersMarcus Foundation
KeywordsRheumatoid arthritisGenetic architectureMedicineAutoimmune diseaseImmunologyPediatrics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.003
GPT teacher head0.184
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueArthritis & RheumatologySame topicImmunodeficiency and Autoimmune DisordersFrench-language works237,207