Novel CYP2A6 diplotypes identified through next-generation sequencing are associated with in-vitro and in-vivo nicotine metabolism
Bibliographic record
Abstract
OBJECTIVES: Smoking patterns and cessation rates vary widely across smokers and can be influenced by variation in rates of nicotine metabolism [i.e. cytochrome P450 2A6 (CYP2A6), enzyme activity]. There is high heritability of CYP2A6-mediated nicotine metabolism (60-80%) owing to known and unidentified genetic variation in the CYP2A6 gene. We aimed to identify and characterize additional genetic variants at the CYP2A6 gene locus. METHODS: A new CYP2A6-specific sequencing method was used to investigate genetic variation in CYP2A6. Novel variants were characterized in a White human liver bank that has been extensively phenotyped for CYP2A6. Linkage and haplotype structure for the novel single nucleotide polymorphisms (SNPs) were assessed. The association between novel five-SNP diplotypes and nicotine metabolism rate was investigated. RESULTS: Seven high-frequency (minor allele frequencies ≥6%) noncoding SNPs were identified as important contributors to CYP2A6 phenotypes in a White human liver bank (rs57837628, rs7260629, rs7259706, rs150298687 (also denoted rs4803381), rs56113850, rs28399453, and rs8192733), accounting for two times more variation in in-vitro CYP2A6 activity relative to the four established functional CYP2A6 variants that are frequently tested in Whites (CYP2A6*2, *4, *9, and *12). Two pairs of novel SNPs were in high linkage disequilibrium, allowing us to establish five-SNP diplotypes that were associated with CYP2A6 enzyme activity (rate of nicotine metabolism) in-vitro in the liver bank and in-vivo among smokers. CONCLUSION: The novel five-SNP diplotype may be useful to incorporate into CYP2A6 genotype models for personalized prediction of nicotine metabolism rate, cessation success, and response to pharmacotherapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".