Phenotype-Haplotype Correlation of <i>IRF5</i> in Systemic Sclerosis: Role of 2 Haplotypes in Disease Severity
Bibliographic record
Abstract
OBJECTIVE: Identification of an association between IRF5 rs2004640 and systemic sclerosis (SSc) has highlighted a key role for type 1 interferon (IFN). Additional functional IRF5 variants have been identified as autoimmune susceptibility factors. Our aim was to investigate whether IRF5 haplotypes confer susceptibility to SSc, and to perform genotype haplotype-phenotype correlation analyses. METHODS: We genotyped IRF5 rs377385, rs2004640, and rs10954213 in 1623 individuals of French European Caucasian origin. SSc patient subphenotypes were analyzed according to cutaneous subsets and for SSc-related pulmonary fibrosis. RESULTS: Case-control studies of single markers revealed an association between IRF5 rs3757385, rs2004640, and rs10954213 variants and SSc. We identified an IRF5 risk haplotype "R" (p(adj) = 0.024, OR 1.23, 95% CI 1.07-1.40) and a mirrored protective haplotype "P" (p(adj) = 8.8 x 10(-3), OR 0.78, 95% CI 0.68-0.90) for SSc susceptibility. Genotype-phenotype correlation analyses failed to detect any association with a single marker. By contrast, phenotype-haplotype correlation analysis was able to detect intra-cohort association and to discriminate SSc patients with from those without the following clinical traits: "R" and/or "P" haplotypes identified diffuse cutaneous SSc (p = 0.0081) and fibrosing alveolitis (p = 0.018). CONCLUSION: IRF5 haplotypes are more informative than single markers, suggesting that they could be helpful for risk stratification of SSc patients. Our study provides further evidence of a key role of IRF5 in SSc 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".