Shared genetic investigation of asthma and blood eosinophils in relation to chronic rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: An epidemiological association among asthma, blood eosinophil level and chronic rhinosinusitis (CRS) is well established, but whether consistent genetic relationships exist, and whether this reflects a shared genetic etiology between CRS and asthma or blood eosinophil level remains unclear. METHODS: Data from CRS patients (N = 1,255) and healthy controls (N = 1,032) were reviewed retrospectively to investigate associations between clinical characteristics and CRS. Data from white blood cells in the UK biobank (N = 173,480), asthma in the Trans-National Asthma Genetic Consortium (127,669) and CRS (N = 272,922) or nasal polyps (N = 264,107) in the FinnGen consortium were used to conduct genetic study, including linkage disequilibrium score regression analysis to detect genetic associations between aforementioned variables, Mendelian randomization (MR) analysis to investigate causal relationships of asthma and blood eosinophil levels on CRS, and Bayesian co-localization to consolidate MR findings and to identify shared genetic signals. RESULTS: We found that blood eosinophil count, blood eosinophil percentages and asthma shared positive and causal genetic correlations with CRS (all q < 0.0001) and CRS with nasal polyps (CRSwNP) (all q < 0.0001) in both our observational and genetic study. Through colocalization analysis, 4 loci are shared among asthma, CRS and CRSwNP, 7 loci are shared among blood eosinophil count, CRS and CRSwNP, 2 loci are unique to blood eosinophil count and CRS, and 3 loci are unique to blood eosinophil count and CRSwNP. CONCLUSIONS: These findings contribute to understanding CRS etiology, and provide insights for intervention and treatment target for CRS comorbid with asthma or high blood eosinophil levels.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".