Meta‐analysis confirmed genetic susceptibility conferred by multiple risk variants from <scp>CTLA4</scp> and <scp>SERPINA1</scp> in granulomatosis with polyangiitis
Bibliographic record
Abstract
BACKGROUND: Granulomatosis with polyangiitis (GPA) is a rare systemic autoimmune disease. Smaller sample size and complex nature of the disease pathogenesis has made it challenging to perform well-powered genetic investigations. We performed a systematic review based meta-analysis in GPA to investigate the genetic susceptibility conferred by non-human leukocyte antigen (non-HLA) candidate genes. METHODS: A systematic review was performed using web-based literature search and eligible studies were included following inclusion-exclusion criteria. Studies were evaluated for their quality of evidence and study outcome was assessed using the Newcastle-Ottawa Scale and Grades of Research, Assessment, Development and Evaluation tools. Reviewer's agreement was accessed through Cohen's κ value. Meta-analyses were performed using RevMan 5 tool. Meta-odds ratio (meta-OR) and Z test P value were evaluated to estimate the genetic susceptibility for each of the variants. RESULTS: Eighteen studies were found eligible and 7 genetic variants from only 4 genes, namely CTLA4, PRTN3, SERPINA1 and PTPN22 could be studied for meta-analysis. rs231775-G (49-G) (Meta-OR = 1.42 [1.14-1.76]; P = .001) of CTLA4 and rs7151526-A (Meta-OR = 2.70 [1.51-4.85]; P = .0008) of SERPINA1 were confirmed to be predisposing alleles, and rs5742909-C (318-C) (Meta-OR = 0.65 [0.44-0.97]; P =.03) of CTLA4 was found to be protective for GPA. In concordance with the genetic association of rs7151526-A, serological marker for the same variant "Z" allele of SERPINA1 was found to be predisposing (Meta-OR = 12.60 [5.01-31.68]; P < .00001) for GPA. CONCLUSION: Genetic variants confirmed in this study play critical roles in T-cell mediated immune function and could be significantly implicated in GPA. Molecular pathology studies are warranted to confirm their role. These markers could be used for efficient patient classification and disease management.
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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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.060 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".