Novel Genes for Airway Wall Thickness Identified with Combined Genome-Wide Association and Expression Analyses
Bibliographic record
Abstract
RATIONALE: Airway wall thickness (AWT) is affected by both environmental and genetic factors and is strongly associated with airflow limitation in smaller airways. OBJECTIVES: To investigate the genetic component of AWT. METHODS: AWT was measured on low-dose computed tomography scans in male heavy smokers participating in a lung cancer screening study (n = 2,640). Genome-wide association studies on AWT were performed under an additive model using linear regression (adjusted for pack-years, lung volume), followed by metaanalysis. An independent cohort was used for validation of the most strongly associated single-nucleotide polymorphisms (SNPs). The functional relevance of significant SNPs was evaluated. MEASUREMENTS AND MAIN RESULTS: Three significant loci on chromosomes 2q (rs734556; P = 6.2 × 10(-7)) and 10q (rs10794108, P = 8.6 × 10(-8); rs7078439, P = 2.3 × 10(-7)) were associated with AWT and confirmed in the metaanalysis in cohorts with comparable lung function: P values = 4.6 × 10(-8), 7.4 × 10(-8), and 7.5 × 10(-8), respectively. SNP rs734556 was associated with decreased lung tissue expression of SERPINE2, a susceptibility gene for emphysema. Two nominally significant SNPs showed effects with similar direction: rs10251504 in MAGI2 (P = 5.8 × 10(-7)) and rs4796712 in NT5C3B (P = 3.1 × 10(-6)). Higher MAGI2 expression in bronchial biopsies of patients with chronic obstructive pulmonary disease was significantly associated with fewer inflammatory cells. The presence of the NT5C3B risk allele was associated with higher lung tissue expression (P = 1.09 × 10(-41)). CONCLUSIONS: Genetic variants contribute to AWT. Among others, the identified genes are also involved in emphysema, airway obstruction, and bronchial inflammation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".