Role of <i>BANK1</i> Gene Polymorphisms in Biopsy-proven Giant Cell Arteritis
Bibliographic record
Abstract
OBJECTIVE: Giant cell arteritis (GCA) is a complex polygenic disease in which more than 1 genetic locus is likely to contribute to disease susceptibility and clinical expression. BANK, an adaptor molecule, has been suggested to participate in the B cell antigen receptors-mediated calcium homeostasis. We assessed for the first time the implication of BANK1 functional variants in susceptibility to GCA. METHODS: Two hundred twenty-two patients with biopsy-proven GCA and 534 matched controls were assessed. DNA from patients and controls was obtained from peripheral blood. Samples were genotyped for 3 putative functional BANK1 gene polymorphisms (rs17266594 T/C, rs10516487 G/A, rs3733197 G/A) using a TaqMan allele discrimination assay. RESULTS: No significant differences were observed in genotype distribution between patients with biopsy-proven GCA and controls for these 3 gene polymorphisms. A trend for a decreased risk of having GCA was observed in individuals carrying the BANK1 rs3733197 GG genotype (patients with GCA 43.9% compared to 51.6% in controls; p = 0.06, OR 0.73, 95% CI 0.53-1.02). The frequency of BANK1 rs3733197 allele G was marginally decreased in patients with biopsy-proven GCA compared to controls (p = 0.09, OR 0.82, 95% CI 0.64-1.04). Haplotype analysis of 3-single-nucleotide polymorphisms found no statistically significant differences between patients with GCA and controls. No significant differences for the BANK1 gene polymorphisms were found when patients were stratified according to specific clinical features of the disease. CONCLUSION: Our results do not support a major implication of the BANK1 locus in susceptibility to GCA.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".