Susceptibility to chronic mucus hypersecretion: A genome-wide association study
Bibliographic record
Abstract
Background: Patients with COPD with chronic mucus hypersecretion (CMH) have a significantly increased FEV 1 decline and a higher risk of hospitalization than those without CMH. Moreover, although individuals with CMH are more likely to be ex- or current smokers, so far it is unclear why some smokers develop CMH and others do not. A plausible explanation for this phenomenon would be a genetic predisposition. Methods: We performed a genome wide association study (GWAs) using the Nelson study (a population-based lung cancer screening study from Groningen and Utrecht, the Netherlands) including 717 subjects with and 1,795 without CMH, all ex or current heavy smokers (>20 pack years). Lung function results and information about sputum expectoration during the previous year were collected at the start of the study. To enhance power we added 590 blood bank controls. Genotyping data were analyzed using PLINK with adjustment for center (Groningen/Utrecht). We aimed to replicate the results by evaluating the top single nucleotide polymorphisms (SNPs) in 7 European cohorts contributing 487 cases and 1,118 controls. Results: We identified 77 SNPs associated with CMH with a p-value <10 -4 , of which 5 SNPs had a p-value <10 -5 . Meta-analysis of selected top SNPs in the initial and replication cohorts identified 3 SNPs with a p-value <10 -6 and 3 with a p-value <10 -5 . Some genes close to these SNPs have been reported to be associated with epithelial changes. Conclusion: This study suggests that susceptibility to CMH is associated with genetic predisposition. To confirm these data replication will be extended to 3 additional cohorts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".