Characterizing Genotyping Methods for CYP2A6, Their Impacts on Prediction of Nicotine Metabolism, and Applications for the Discovery of Novel Associations
Bibliographic record
Abstract
Smoking remains a major health issue worldwide, with cessation difficult to achieve. CYP2A6 isthe principal nicotine-inactivating enzyme. Genetic variation in CYP2A6 is associated with altered smoking behaviours, response to smoking cessation pharmacotherapies and risk for tobacco related diseases. The potential for more effective personalized medicine approaches to smoking cessation provides an impetus for gaining a full understanding of CYP2A6. In this thesis, we investigated, compared and developed methods for characterizing CYP2A6 variation. First, we compared genotype calls in CYP2A6, CYP2A7, CYP2A13, and CYP2B6 from SNParray genotyping (and imputation), targeted short-read sequencing, and CYP2A6-specific amplicon sequencing, the gold standard. We found that discordant genotype calls were common, and occurred principally in regions of high identity between CYP2A6 and other CYP2A genes. We achieved accurate genotype calls for CYP2A6*46, a CYP2A7 conversion variant, from amplicon sequencing by masking CYP2A7 sequence during alignment; CYP2A6*46 was associated with increased CYP2A6 activity in European-ancestry (EUR) individuals. Next, we developed a method for unambiguous genotype calling of structural variants (SVs)using commercial TaqMan copy number assays and characterized SVs in EUR and AFR. We leveraged SNP array genotype data to create an SV imputation reference panel; cross-validation implied that most SV alleles are accurately imputed with a low false positive rate. Finally, we applied our reference panel to genotype CYP2A6 SVs in the UK Biobank toinvestigate recent implications of an association between SVs and risk for ovarian cancer. Lung cancer was used as a positive control because it is known to have lower risk in those with deleterious CYP2A6 SVs. We found a strong association between deleterious SVs and decreased risk of lung cancer, confirming the validity of SV imputation, but found no association between SVs and ovarian cancer risk. Our findings clarified important heterogeneity in CYP2A6 genotyping calls, which is importantto account for in clinical applications, as well as improved accuracy of genotyping and our knowledge of highly functionally impactful SVs. Future work should expand SV characterization to other ancestries and further characterize the causal impact of genetic variants on CYP2A6 activity.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".