Accuracy and applications of sequencing and genotyping approaches for CYP2A6 and homologous genes
Bibliographic record
Abstract
OBJECTIVES: We evaluated multiple genotyping/sequencing approaches in a homologous region of chromosome 19, and investigated associations of two common 3'-UTR CYP2A6 variants with activity in vivo. METHODS: Individuals (n = 1704) of European and African ancestry were phenotyped for the nicotine metabolite ratio (NMR), an index of CYP2A6 activity, and genotyped/sequenced using deep amplicon exon sequencing, SNP array, genotype imputation and targeted capture sequencing. Amplicon exon sequencing was the gold standard to which other methods were compared within-individual for CYP2A6, CYP2A7, CYP2A13, and CYP2B6 exons to identify highly discordant positions. Linear regression models evaluated the association of CYP2A6*1B and rs8192733 genotypes (coded additively) with logNMR. RESULTS: All approaches were ≤2.6% discordant with the gold standard; discordant calls were concentrated at few positions. Fifteen positions were discordant in >10% of individuals, with 12 appearing in regions of high identity between homologous genes (e.g. CYP2A6 and CYP2A7). For six, allele frequencies in our study and online databases were discrepant, suggesting errors in online sources. In the European-ancestry group (n = 935), CYP2A6*1B and rs8192733 were associated with logNMR (P < 0.001). A combined model found main effects of both variants on increasing logNMR. Similar trends were found in those of African ancestry (n = 506). CONCLUSION: Multiple genotyping/sequencing approaches used in this chromosome 19 region contain genotyping/sequencing errors, as do online databases. Gene-specific primers and SNP array probes must consider gene homology; short-read sequencing of related genes in a single reaction should be avoided. Using improved sequencing approaches, we characterized two gain-of-function 3'-UTR variants, including the relatively understudied rs8192733.
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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 | 0.057 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".