Disease Phenotypes and Gender Association of FCRL3 Single-Nucleotide Polymorphism −169T/C in Taiwanese Patients with Systemic Lupus Erythematosus and Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To investigate the association of the functional FCRL3 single-nucleotide polymorphism (SNP) -169T/C with disease phenotypes and susceptibility to systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) in Taiwanese. METHODS: FCRL3 SNP -169T/C was genotyped in 573 patients with SLE, 670 patients with RA, and 758 controls. Genotype distributions and allele frequencies were compared among the 3 groups as aggregates or as stratified by clinical characteristics, autoantibody profile, and sex within patient groups. RESULTS: Overall, FCRL3 SNP -169T/C was not associated with susceptibility to either SLE or RA. However, -169CC genotype was significantly reduced in leukopenia-positive SLE patients as compared to the leukopenia-negative SLE patients (CC vs CT+TT, p = 6 × 10(-4), OR 0.444, 95% CI 0.279-0.708) and controls (p = 6.1 × 10(-3), OR 0.583, 95% CI 0.396-0.857). On the other hand, -169TT genotypes were significantly more numerous in RA patients with non-destructive disease as compared with patients with destructive disease (CC+CT vs TT: p = 0.007, OR 1.672, 95% CI 1.149-2.432). The -169T allele frequency was also significantly increased in non-destructive RA compared with patients with destructive disease (C vs T: p = 0.010, OR 1.423, 95% CI 1.089-1.859). FCRL3 SNP -169TT homozygous donors were significantly more numerous among female cyclic citrullinated peptide (CCP)-negative RA patients versus female CCP-positive RA patients (CC+CT vs TT: p = 0.019, OR 1.64, 95% CI 1.085-2.479). CONCLUSION: The functional FCRL3 SNP -169T/C appears to play important roles in the development of certain phenotypes such as SLE leukopenia and RA disease severity in Taiwanese patients with SLE and RA.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".