Transferability of Ancestry‐Specific and Cross‐Ancestry CYP2A6 Activity Genetic Risk Scores in African and European Populations
Bibliographic record
Abstract
The Nicotine Metabolite Ratio (NMR; 3-hydroxycotinine/cotinine), a highly heritable index of nicotine metabolic inactivation by the CYP2A6 enzyme, is associated with numerous smoking behaviors and diseases, as well as unique cessation outcomes. However, the NMR cannot be measured in nonsmokers, former smokers, or intermittent smokers, for example, in evaluating tobacco-related disease risk. Traditional pharmacogenetic groupings based on CYP2A6 * alleles capture a modest portion of NMR variation. We previously created a CYP2A6 weighted genetic risk score (wGRS) for European (EUR)-ancestry populations by incorporating independent signals from genome-wide association studies to capture a larger proportion of NMR variation. However, CYP2A6 genetic architecture is unique to ancestral populations. In this study, we developed and replicated an African-ancestry (AFR) wGRS, which captured 30-35% of the variation in NMR. We demonstrated model robustness against known environmental sources of NMR variation. Furthermore, despite the vast diversity within AFR populations, we showed that the AFR wGRS was consistent between different US geographical regions and unaltered by fine AFR population substructure. The AFR and EUR wGRSs can distinguish slow from normal metabolizers in their respective populations, and were able to reflect unique smoking cessation pharmacotherapy outcomes previously observed for the NMR. Additionally, we evaluated the utility of a cross-ancestry wGRS, and the capacity of EUR, AFR, and cross-ancestry wGRSs to predict the NMR within stratified or admixed AFR-EUR populations. Overall, our findings establish the clinical benefit of applying ancestry-specific wGRSs, demonstrating superiority of the AFR wGRS in AFRs.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".