Multi-ancestry meta-analyses of lung cancer in the Million Veteran Program reveal novel risk loci and elucidate smoking-independent genetic risk
Bibliographic record
Abstract
Abstract Lung cancer remains the leading cause of cancer mortality, despite declines in smoking rates. Previous lung cancer genome-wide association studies (GWAS) have identified numerous loci, but separating the genetic risks of lung cancer and smoking behavioral susceptibility remains challenging. We performed multi-ancestry GWAS meta-analyses of lung cancer using the Million Veteran Program (MVP) cohort and a previous study of European-ancestry individuals, comprising 42,102 cases and 181,270 controls, followed by replication in an independent cohort of 19,404 cases and 17,378 controls. We further performed conditional meta-analyses on cigarettes per day and identified two novel, replicated loci, including the 19p13.11 pleiotropic cancer locus in LUSC. Overall, we report twelve novel risk loci for overall lung cancer, lung adenocarcinoma (LUAD), and squamous cell lung carcinoma (LUSC), nine of which were externally replicated. Finally, we performed phenome-wide association studies (PheWAS) on polygenic risk scores (PRS) for lung cancer, with and without conditioning on smoking. The unconditioned lung cancer PRS was associated with smoking status in controls, illustrating reduced predictive utility in non-smokers. Additionally, our PRS demonstrates smoking-independent pleiotropy of lung cancer risk across neoplasms and metabolic traits.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.011 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".