Sex dependent genetic architecture of biochemically verified tobacco use
Bibliographic record
Abstract
BACKGROUND: Tobacco use differs by genetics and sex, and dose-dependently increases the risk for numerous diseases. Nicotine is metabolized to cotinine (COT) which is further metabolized to 3'hydroxycotinine (3HC). COT and COT+3HC are biomarkers which capture tobacco intake more accurately than self-reported measures such as cigarettes/day. It is currently not known whether genetic risk factors for heavier tobacco intake, measured using these biomarkers, differ by sex. METHODS: We conducted a genome-wide genotype-by-sex (GxS) interaction analysis of COT and COT+3HC measured from blood in European treatment-seeking smokers (n = 541 males, n = 389 females) (NCT01314001). Linear regression models included Genotypes (coded additively), Sex, a GxS interaction term, covariates, and all covariate-by-genotype and covariate-by-sex interaction terms. RESULTS: ). Overall, 26 genes were mapped, with 9 showing moderate to high expression in brain, and 5 showing prior associations with psychiatric traits in the GWAS Catalog. CONCLUSIONS: Our findings suggest that the genetic architecture of tobacco intake, measured accurately using biomarkers, differs between women and men. A more granular understanding of factors influencing tobacco intake in women versus men may identify risk factors for heavier use and sex-specific opportunities to promote smoking cessation and mitigate disease risk. IMPLICATIONS: This genome-wide interaction study suggested that some of the genetic influences on tobacco intake, measured accurately using biomarkers, differ by sex. The loci identified in our study could be a starting point for developing new genetic biomarkers that predict sex-specific differences in tobacco intake and disease risk.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".