Cigarette smoking reduces DNA methylation levels at multiple genomic loci but the effect is partially reversible upon cessation
Bibliographic record
Abstract
Smoking is a major risk factor in many diseases. Genome wide association studies have linked genes for nicotine dependence and smoking behavior to increased risk of cardiovascular, pulmonary, and malignant diseases. We conducted an epigenome wide association study in peripheral-blood DNA in 464 individuals (22 current smokers and 263 ex-smokers), using the Human Methylation 450 K array. Upon replication in an independent sample of 356 twins (41 current and 104 ex-smokers), we identified 30 probes in 15 distinct loci, all of which reached genome-wide significance in the combined analysis P < 5 × 10(-8). All but one probe (cg17024919) remained significant after adjusting for blood cell counts. We replicated all 9 known loci and found an independent signal at CPOX near GPR15. In addition, we found 6 new loci at PRSS23, AVPR1B, PSEN2, LINC00299, RPS6KA2, and KIAA0087. Most of the lead probes (13 out of 15) associated with cigarette smoking, overlapped regions of open chromatin (FAIRE and DNaseI hypersensitive sites) or/and H3K27Ac peaks (ENCODE data set), which mark regulatory elements. The effect of smoking on DNA methylation was partially reversible upon smoking cessation for longer than 3 months. We report the first statistically significant interaction between a SNP (rs2697768) and cigarette smoking on DNA methylation (cg03329539). We provide evidence that the metSNP for cg03329539 regulates expression of the CHRND gene located circa 95 Kb downstream of the methylation site. Our findings suggest the existence of dynamic, reversible site-specific methylation changes in response to cigarette smoking , which may contribute to the extended health risks associated with cigarette smoking.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".