Epigenome-wide association study of serum cotinine in current smokers reveals novel genetically driven loci
Bibliographic record
Abstract
BACKGROUND: DNA methylation alteration extensively associates with smoking and is a plausible link between smoking and adverse health. We examined the association between epigenome-wide DNA methylation and serum cotinine levels as a proxy of nicotine exposure and smoking quantity, assessed the role of SNPs in these associations, and evaluated molecular mediation by methylation in a sample of biochemically verified current smokers (N = 310). RESULTS: DNA methylation at 50 CpG sites was associated (FDR < 0.05) with cotinine levels, 17 of which are novel associations. As cotinine levels are influenced not only by nicotine intake but also by CYP2A6-mediated nicotine metabolism rate, we performed secondary analyses adjusting for genetic risk score of nicotine metabolism rate and identified five additional novel associations. We further assessed the potential role of genetic variants in the detected association between methylation and cotinine levels observing 124 cis and 3898 trans methylation quantitative trait loci (meQTLs). Nineteen of these SNPs were also associated with cotinine levels (FDR < 0.05). Further, at seven CpG sites, we observed a trend (P < 0.05) that altered DNA methylation mediates the effect of SNPs on nicotine exposure rather than a direct consequence of smoking. Finally, we performed replication of our findings in two independent cohorts of biochemically verified smokers (N = 450 and N = 79). CONCLUSIONS: Using cotinine, a biomarker of nicotine exposure, we replicated and extended identification of novel epigenetic associations in smoking-related genes. We also demonstrated that DNA methylation in some of the identified loci is driven by the underlying genotype and may mediate the causal effect of genotype on cotinine 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".