Copy Number Variation of <i>HLA-DQA1</i> and <i>APOBEC3A/3B</i> Contribute to the Susceptibility of Systemic Sclerosis in the Chinese Han Population
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc) is a systemic connective tissue disease caused by a genetic aberrant. The involvement of the copy number variations (CNV) in the pathogenesis of SSc is unclear. We tried to identify some CNV that are involved with the susceptibility to SSc. METHODS: A genome-wide CNV screening was performed in 20 patients with SSc. Five SSc-associated common CNV that included HLA-DRB5, HLA-DQA1, IRGM, CDC42EP3, and APOBEC3A/3B were identified from the screening and were then validated in 365 patients with SSc and 369 matched healthy controls. RESULTS: Three hundred forty-four CNV (140 gains and 204 losses) and 2 CNV hotspots (6q21.3 and 22q11.2) were found in the SSc genomes (covering 24.2 megabases), suggesting that CNV were ubiquitous in the SSc genome and played important roles in the pathogenesis of SSc. The high copy number of HLA-DQA1 was a significantly protective factor for SSc (OR 0.07, p = 2.99 × 10(-17)), while the high copy number of APOBEC3A/B was a significant risk factor (OR 3.45, p = 6.4 × 10(-18)), adjusted with sex and age. The risk prediction model based on genetic factors in logistic regression showed moderate prediction ability, with area under the curve = 0.80 (95% CI 0.77-0.83), which demonstrated that APOBEC3A/B and HLA-DQA1 were powerful biomarkers for SSc risk evaluation and contributed to the susceptibility to SSc. CONCLUSION: CNV of HLA-DQA1 and APOBEC3A/B contribute to the susceptibility to SSc in a Chinese Han population.
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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 | 0.000 |
| 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) | 0.002 | 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".