Genome-Wide Association Analysis of Blood Biomarkers in Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
RATIONALE: A genome-wide association study (GWAS) for circulating chronic obstructive pulmonary disease (COPD) biomarkers could identify genetic determinants of biomarker levels and COPD susceptibility. OBJECTIVES: To identify genetic variants of circulating protein biomarkers and novel genetic determinants of COPD. METHODS: GWAS was performed for two pneumoproteins, Clara cell secretory protein (CC16) and surfactant protein D (SP-D), and five systemic inflammatory markers (C-reactive protein, fibrinogen, IL-6, IL-8, and tumor necrosis factor-α) in 1,951 subjects with COPD. For genome-wide significant single nucleotide polymorphisms (SNPs) (P < 1 × 10(-8)), association with COPD susceptibility was tested in 2,939 cases with COPD and 1,380 smoking control subjects. The association of candidate SNPs with mRNA expression in induced sputum was also elucidated. MEASUREMENTS AND MAIN RESULTS: Genome-wide significant susceptibility loci affecting biomarker levels were found only for the two pneumoproteins. Two discrete loci affecting CC16, one region near the CC16 coding gene (SCGB1A1) on chromosome 11 and another locus approximately 25 Mb away from SCGB1A1, were identified, whereas multiple SNPs on chromosomes 6 and 16, in addition to SNPs near SFTPD, had genome-wide significant associations with SP-D levels. Several SNPs affecting circulating CC16 levels were significantly associated with sputum mRNA expression of SCGB1A1 (P = 0.009-0.03). Several SNPs highly associated with CC16 or SP-D levels were nominally associated with COPD in a collaborative GWAS (P = 0.001-0.049), although these COPD associations were not replicated in two additional cohorts. CONCLUSIONS: Distant genetic loci and biomarker-coding genes affect circulating levels of COPD-related pneumoproteins. A subset of these protein quantitative trait loci may influence their gene expression in the lung and/or COPD susceptibility. Clinical trial registered with www.clinicaltrials.gov (NCT 00292552).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".