Investigation of Rheumatoid Arthritis Genetic Susceptibility Markers in the Early Rheumatoid Arthritis Study Further Replicates the <i>TRAF1</i> Association with Radiological Damage
Bibliographic record
Abstract
OBJECTIVE: The TRAF1 genetic region conferring susceptibility to rheumatoid arthritis (RA) has been reported to associate with radiological damage. We aimed to test RA genetic susceptibility markers for association with a continuous measure of radiological damage over time using longitudinal modeling techniques. METHODS: Sixty-seven RA susceptibility variants were genotyped in 474 patients in the Early Rheumatoid Arthritis Study (ERAS) using Sequenom MassArray technology. Correlation between genetic markers and Larsen score was assessed longitudinally using zero-inflated negative binomial regression to include repeat measurements in the same individual at different timepoints. Genetic markers associated with radiological damage in ERAS were tested using the same modeling techniques on previously published data from the Norfolk Arthritis Register (NOAR). RESULTS: The single marker associated longitudinally with Larsen score in ERAS (p = 0.02) and in NOAR (p = 0.04) was rs2900180 at the TRAF1 locus. Analysis of individual timepoints in ERAS showed that rs2900180 displays its effect primarily on the extent of Larsen score early in the disease course. Combined longitudinal analysis of the 2 cohorts suggests further association of several loci with Larsen score (KIF5A, PTPN22, AFF3, TAGAP) and therefore a significant accumulation of RA severity markers among RA susceptibility markers (p = 0.016). CONCLUSION: The marker rs2900180 is associated with the extent of radiological damage in the ERAS cohort. This represents the second independent study correlating rs2900180 at the TRAF1 locus with radiological severity in RA. Replication in a large dataset is required to establish the role of other RA susceptibility loci in disease severity.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".