Evaluation of a weighted genetic risk score for the prediction of biomarkers of CYP2A6 activity
Bibliographic record
Abstract
The nicotine metabolite ratio (NMR; 3-hydroxycotinine/cotinine) is an index of CYP2A6 activity. CYP2A6 is responsible for nicotine's metabolic inactivation and variation in the NMR/CYP2A6 is associated with several smoking behaviors. Our aim was to integrate established alleles and novel genome-wide association studies (GWAS) signals to create a weighted genetic risk score (wGRS) for the CYP2A6 gene for European-ancestry populations. The wGRS was compared with a previous CYP2A6 gene scoring approach designed for an alternative phenotype (C2/N2; cotinine-d2/(nicotine-d2 + cotinine-d2)). CYP2A6 genotypes and the NMR were assessed in European-ancestry participants. The wGRS training set included N = 933 smokers recruited to the Pharmacogenetics of Nicotine Addiction and Treatment clinical trial [NCT01314001]. The replication cohort included N = 196 smokers recruited to the Quit 2 Live clinical trial [NCT01836276]. Comparisons between the two CYP2A6 phenotypes and with fractional clearance were made in a laboratory-based pharmacokinetic study (N = 92 participants). In both the training and replication sets, the wGRS, which included seven CYP2A6 variants, explained 33.8% (P < 0.001) of the variance in NMR, providing improved predictive power to the NMR phenotype when compared with other CYP2A6 gene scoring approaches. NMR and C2/N2 were strongly correlated to nicotine clearance (ρ = 0.70 and ρ = 0.79, respectively; P < 0.001), and to one another (ρ = 0.82; P < 0.001); however reduced function genotypes occurred in slow NMR but throughout C2/N2. The wGRS was able to predict smoking quantity and nicotine intake, to discriminate between NMR slow and normal metabolizers (AUC = 0.79; P < 0.001), and to replicate previous NMR-stratified cessation outcomes showing unique treatment outcomes between metabolizer groups.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".