Genome‐wide association study identifies an early onset pancreatic cancer risk locus
Bibliographic record
Abstract
Abstract Early onset pancreatic cancer (EOPC) is a rare disease with a very high mortality rate. Almost nothing is known on the genetic susceptibility of EOPC, therefore, we performed a genome‐wide association study (GWAS) to identify novel genetic variants specific for patients diagnosed with pancreatic ductal adenocarcinoma (PDAC) at younger ages. In the first phase, conducted on 821 cases with age of onset ≤60 years, of whom 198 with age of onset ≤50, and 3227 controls from PanScan I‐II, we observed four SNPs (rs7155613, rs2328991, rs4891017 and rs12610094) showing an association with EOPC risk (P < 1 × 10−4). We replicated these SNPs in the PANcreatic Disease ReseArch (PANDoRA) consortium and used additional in silico data from PanScan III and PanC4. Among these four variants rs2328991 was significant in an independent set of 855 cases with age of onset ≤60 years, of whom 265 with age of onset ≤50, and 4142 controls from the PANDoRA consortium while in the in silico data, we observed no statistically significant association. However, the resulting meta‐analysis supported the association (P = 1.15 × 10−4). In conclusion, we propose a novel variant rs2328991 to be involved in EOPC risk. Even though it was not possible to find a mechanistic link between the variant and the function, the association is supported by a solid statistical significance obtained in the largest study on EOPC genetics present so far in the literature.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".