Bibliographic record
Abstract
Smoking continues to be the leading preventable contributor to death worldwide. Twin studies have suggested a significant genetic contribution underlying most smoking behaviors (40-70% heritability estimates). Candidate gene studies of smoking phenotypes have identified several pharmacogenes implicated in nicotine's pharmacokinetics (CYP2A6, CYP2B6, CYP2A13, FMOs, UGTs, and OCT2), and nicotine's pharmacodynamic response in the central nervous system (nicotinic acetylcholine receptors, as well as through the dopaminergic and serotonergic systems). Subsequent genome-wide association studies (GWAS) have confirmed the role of certain pharmacogenes through hypothesis-free approaches. Furthermore, pharmacogenes that alter the efficacy of smoking cessation pharmacotherapies, including nicotine replacement therapies, bupropion, and varenicline, may also impact quitting success. In this brief review we highlight the role of pharmacogenes in smoking behaviors, such as smoking status, consumption, nicotine dependence, spontaneous quitting, and altered abstinence to pharmacotherapies; We provide examples from initial candidate gene associations and subsequent GWAS. The genes CYP2A6 and the CHRNA5-A3-B4 confer the most replicated sources of genetic variation in smoking behaviors, likely due to their importance in nicotine's pharmacology. We will also provide examples of genetic scoring approaches, and the role of rare variants in explaining a portion of the missing heritability in smoking behaviors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".