Dissecting the genetics of chronic mucus hypersecretion in smokers with and without COPD
Bibliographic record
Abstract
Smoking is a notorious risk factor for chronic mucus hypersecretion (CMH). CMH frequently occurs in chronic obstructive pulmonary disease (COPD). The question arises whether the same single-nucleotide polymorphisms (SNPs) are related to CMH in smokers with and without COPD. We performed two genome-wide association studies of CMH under an additive genetic model in male heavy smokers (≥20 pack-years) with COPD (n=849, 39.9% CMH) and without COPD (n=1348, 25.4% CMH), followed by replication and meta-analysis in comparable populations, and assessment of the functional relevance of significantly associated SNPs. Genome-wide association analysis of CMH in COPD and non-COPD subjects yielded no genome-wide significance after replication. In COPD, our top SNP (rs10461985, p=5.43×10(-5)) was located in the GDNF-AS1 gene that is functionally associated with the GDNF gene. Expression of GDNF in bronchial biopsies of COPD patients was significantly associated with CMH (p=0.007). In non-COPD subjects, four SNPs had a p-value <10(-5) in the meta-analysis, including a SNP (rs4863687) in the MAML3 gene, the T-allele showing modest association with CMH (p=7.57×10(-6), OR 1.48) and with significantly increased MAML3 expression in lung tissue (p=2.59×10(-12)). Our data suggest the potential for differential genetic backgrounds of CMH in individuals with and without COPD.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".