Genetic Interactions Between <i>BANK1</i> and <i>BLK</i> in Chinese Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
To the Editor: Systemic lupus erythematosus (SLE) is a complex autoimmune disease with strong genetic components, with over 40 susceptibility loci identified at present. These SLE susceptibility loci are predominantly common variants that have been confirmed among multiple ancestries, suggesting shared mechanisms in disease etiology1. However, genetic heterogeneity was also suggested, as some genetic polymorphisms are restricted to specific ethnic populations. Recent descriptions of gene–gene interactions, or epistasis, may explain some of the genetic heterogeneity and missing heritability in SLE. We previously reported potential epistasis between BLK and TNFSF4 in both Chinese and white populations, suggesting that unbalanced functions of B cell and T cell signaling may be involved synergistically in the pathogenesis of SLE2. Another large-scale association study confirmed the genetic interactions between BANK1 and BLK in Europeans, indicating B cell activity and a B cell-specific pathway were crucial in lupus pathogenesis3. No further replications were conducted in Chinese subjects or other populations with independent sets of cases … Address correspondence to Prof. H. Zhang, Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, No. 8 Xi Shi Ku Street, Xi Cheng District, Beijing 100034, China. E-mail: hongzh{at}bjmu.edu.cn
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".