Functional Variants of Fc Gamma Receptor (FCGR2A) and FCGR3A Are Not Associated with Susceptibility to Systemic Sclerosis in a Large European Study (EUSTAR)
Bibliographic record
Abstract
OBJECTIVE: To investigate the possible role of FCGR2A 519A>G and FCGR3A 559A>C functional polymorphisms in the genetic predisposition to susceptibility to systemic sclerosis (SSc) or clinical phenotype. METHODS: A total of 1566 patients with SSc and 2271 geographically matched controls were included in our study. We analyzed the genotype and allele frequencies of the FCGR2A 519A>G and FCGR3A 559A>C functional variants in 6 independent European cohorts of white patients with SSc, and white controls. The cohorts comprised 165 Dutch patients with SSc and 1326 controls, 236 Spanish patients with SSc and 257 controls, 267 German patients with SSc and 270 controls, 202 Swedish patients with SSc and 261 controls, 416 Italian patients with SSc and 157 controls, and additionally 280 English patients with SSc. Genotyping was performed using Taqman 5' allelic discrimination assay. The study reached a 99% power to detect the effect of a polymorphism at an OR of 1.3. RESULTS: Neither FCGR2A 519A>G nor FCGR3A 559A>C was significantly associated with susceptibility to SSc. We did not find an association with specific disease phenotypes, limited or diffuse cutaneous involvement, autoantibody profiles, or pulmonary involvement. CONCLUSION: Our study strongly suggests the lack of a role for the FCGR2A 519A>G and FCGR3A 559A>C polymorphisms in SSc susceptibility or clinical phenotype in 6 independent European cohorts.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".