The contributions of deleterious rare alleles in NLRP12 and inflammasome-related genes to polymyalgia rheumatica
Bibliographic record
Abstract
Polymyalgia rheumatica (PMR) is a chronic inflammatory disease characterized by arthralgia and myalgia of the shoulder and hip girdles, and fever. PMR is linked to autoimmune diseases and autoinflammatory disorders. Exome sequencing has revealed the roles of rare variants in some diseases. Causative genes for monogenic autoinflammatory disorders might be candidate genes for the selective exome analysis of PMR. We investigated rare variants in the coding and boundary regions of candidate genes for PMR. Exome sequencing was performed to analyze deleterious rare variants in candidate genes, and the frequencies of the deleterious rare alleles in PMR were compared with those of Japanese population controls. Deleterious rare alleles in the NLRL12 gene were associated with PMR (P = 0.0069, Pc = 0.0415, odds ratio [OR] 4.49, 95% confidence interval [CI] 1.79-11.27). A multigene analysis demonstrated the deleterious rare allele frequency of the candidate genes for autoinflammatory disorders was also increased in PMR (P = 0.0016, OR 3.69, 95%CI 1.81-7.54). The deleterious rare allele frequencies of the candidate genes including NLRP12 were increased in PMR patients, showing links to autoinflammatory disorders in the pathogenesis of PMR.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".