Nicotinic acetylcholine receptor β2 subunit gene implicated in a systems-based candidate gene study of smoking cessation
Bibliographic record
Abstract
Although the efficacy of pharmacotherapy for tobacco dependence has been previously demonstrated, there is substantial variability among individuals in treatment response. We performed a systems-based candidate gene study of 1295 single nucleotide polymorphisms (SNPs) in 58 genes within the neuronal nicotinic receptor and dopamine systems to investigate their role in smoking cessation in a bupropion placebo-controlled randomized clinical trial. Putative functional variants were supplemented with tagSNPs within each gene. We used global tests of main effects and treatment interactions, adjusting the P-values for multiple correlated tests. An SNP (rs2072661) in the 3' UTR region of the beta2 nicotinic acetylcholine receptor subunit (CHRNB2) has an impact on abstinence rates at the end of treatment (adjusted P = 0.01) and after a 6-month follow-up period (adjusted P = 0.0002). This latter P-value is also significant with adjustment for the number of genes tested. Independent of treatment at 6-month follow-up, individuals carrying the minor allele have substantially decreased the odds of quitting (OR = 0.31; 95% CI 0.18-0.55). Effect of estimates indicate that the treatment is more effective for individuals with the wild-type (OR = 2.14, 95% CI 1.20-3.81) compared with individuals carrying the minor allele (OR = 0.83, 95% CI 0.32-2.19), although this difference is only suggestive (P = 0.10). Furthermore, this SNP demonstrated a role in the time to relapse (P = 0.0002) and an impact on withdrawal symptoms at target quit date (TQD) (P = 0.0009). Overall, while our results indicate strong evidence for CHRNB2 in ability to quit smoking, these results require replication in an independent sample.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".