Lack of Association for Reported Endocrine Pancreatic Cancer Risk Loci in the PANDoRA Consortium
Bibliographic record
Abstract
Abstract Background: Pancreatic neuroendocrine tumors (PNETs) are rare neoplasms for which very little is known about either environmental or genetic risk factors. Only a handful of association studies have been performed so far, suggesting a small number of risk loci. Methods: To replicate the best findings, we have selected 16 SNPs suggested in previous studies to be relevant in PNET etiogenesis. We genotyped the selected SNPs (rs16944, rs1052536, rs1059293, rs1136410, rs1143634, rs2069762, rs2236302, rs2387632, rs3212961, rs3734299, rs3803258, rs4962081, rs7234941, rs7243091, rs12957119, and rs1800629) in 344 PNET sporadic cases and 2,721 controls in the context of the PANcreatic Disease ReseArch (PANDoRA) consortium. Results: After correction for multiple testing, we did not observe any statistically significant association between the SNPs and PNET risk. We also used three online bioinformatic tools (HaploReg, RegulomeDB, and GTEx) to predict a possible functional role of the SNPs, but we did not observe any clear indication. Conclusions: None of the selected SNPs were convincingly associated with PNET risk in the PANDoRA consortium. Impact: We can exclude a major role of the selected polymorphisms in PNET etiology, and this highlights the need for replication of epidemiologic findings in independent populations, especially in rare diseases such as PNETs. Cancer Epidemiol Biomarkers Prev; 26(8); 1349–51. ©2017 AACR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".