Independent Replication and Metaanalysis of Association Studies Establish TNFSF4 as a Susceptibility Gene Preferentially Associated with the Subset of Anticentromere-positive Patients with Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Independent replication with large cohorts and metaanalysis of genetic associations are necessary to validate genetic susceptibility factors. The known tumor necrosis factor (ligand) superfamily, member 4 gene (TNFSF4) systemic lupus erythematosus (SLE) risk locus has been found to be associated with systemic sclerosis (SSc) in 2 studies, but with discrepancies between them for genotype-phenotype correlation. Our objective was to validate TNFSF4 association with SSc and determine the subset with the higher risk. METHODS: Known SLE and SSc TNFSF4 susceptibility variants (rs2205960, rs1234317, rs12039904, rs10912580, and rs844648) were genotyped in 1031 patients with SSc and 1014 controls of French white ancestry. Genotype-phenotype association analysis and meta analysis of available data were performed, providing a population study of 4989 patients with SSc and 4661 controls, all of European white ancestry. RESULTS: Allelic and genotypic associations were observed for the 5 single-nucleotide polymorphisms (SNP) with the subset of patients with SSc who are positive for anticentromere antibodies (ACA) and only a trend for association with SSc and limited cutaneous SSc. Rs2205960 exhibited the strongest allelic association in ACA+ patients with SSc [p = 0.0015; OR 1.37 (1.12-1.66)], with significant intra-cohort association when compared to patients with SSc positive for ACA. Metaanalysis confirmed overall association with SSc but also raised preferential association with the ACA+ subset and strongest effect with rs2205960 [T allele p = 0.00013; OR 1.33 (1.15-1.54) and TT genotype p = 0.00046; OR 2.02 (1.36-2.98)]. CONCLUSION: We confirm TNFSF4 as an SSc susceptibility gene and rs2205960 as a putative causal variant with preferential association in the ACA+ SSc subphenotype.
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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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".