Association Study of <i>ITGAM, ITGAX,</i> and <i>CD58</i> Autoimmune Risk Loci in Systemic Sclerosis: Results from 2 Large European Caucasian Cohorts
Bibliographic record
Abstract
OBJECTIVE: Accumulating evidence shows that shared autoimmunity is critical for the pathogenesis of many autoimmune diseases. Systemic sclerosis (SSc) belongs to the connective tissue disorders, and recent data have highlighted strong associations with autoimmunity genes shared with other autoimmune diseases. To determine whether novel risk loci associated with systemic lupus erythematosus or multiple sclerosis may confer susceptibility to SSc, we tested single-nucleotide polymorphisms (SNP) from ITGAM, ITGAX, and CD58 for associations. METHODS: SNP harboring associations with autoimmune diseases, ITGAM rs9937837, ITGAX rs11574637, and CD58 rs12044852, were genotyped in 2 independent cohorts of European Caucasian ancestry: 1031 SSc patients and 1014 controls from France and 1038 SSc patients and 691 controls from the USA, providing a combined study population of 3774 individuals. ITGAM rs1143679 was additionally genotyped in the French cohort. RESULTS: The 4 polymorphisms were in Hardy-Weinberg equilibrium in the 2 control populations, and allelic frequencies were similar to those expected in European Caucasian populations. Allelic and genotypic frequencies for these 3 SNP were found to be statistically similar in SSc patients and controls. Subphenotype analyses for subgroups having diffuse cutaneous subtype disease, specific autoantibodies, or fibrosing alveolitis did not reveal any difference between SSc patients and controls. CONCLUSION: These results obtained through 2 large cohorts of SSc patients of European Caucasian ancestry do not support the implication of ITGAM, ITGAX, and CD58 genes in the genetic susceptibility of SSc, although they were recently identified as autoimmune disease risk genes.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".