Associations of Multiple<i>NOTCH4</i>Exonic Variants with Systemic Sclerosis
Bibliographic record
Abstract
Objective. Findings from previous genome-wide association studies indicated an association of theNOTCH4gene with systemic sclerosis (SSc). This is a followup study to fine-map exonic variants ofNOTCH4in SSc. Methods. All exons ofNOTCH4were sequenced and analyzed in a total of 1006 patients with SSc and 1004 controls of US white ancestry with the Ion Torrent system. Identified SSc-associated variants were confirmed with Sanger sequencing, and then examined in a Chinese Han cohort consisting of 576 patients with SSc and 574 controls. TheNOTCH4variants were analyzed for association with SSc as a whole and with SSc clinical and autoantibody subtypes with and without the influence of specific HLA-class II alleles that had been previously identified as major genetic factors in SSc. Results. A total of 12 SSc-associated and SSc subtype–associated exonic variants ofNOTCH4were identified in the US cohort. Three of them are nonsynonymous single-nucleotide polymorphisms and 1 is a CTG tandem repeat that encodes for a poly-leucine, all of which are located in theNOTCH4extracellular domain (NECD). Conditional logistic regression analysis on SSc-associated HLA-class II alleles indicated an independent association of theNOTCH4variants with SSc autoantibody subtypes. Analysis of the Chinese cohort supported a genetic contribution ofNOTCH4to SSc and its subtypes. Conclusion. MultipleNOTCH4exonic variants were associated with SSc and/or SSc subtypes. Several of these variants encode nonsynonymous sequence changes occurring in the NECD, which implicates a potentially functional effect ofNOTCH4.
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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 | 0.000 | 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".