Inherited Genetic Variants Associated with Melanoma BRAF/NRAS Subtypes
Bibliographic record
Abstract
BRAF and NRAS mutations arise early in melanoma development, but their associations with low-penetrance melanoma susceptibility loci remain unknown. In the Genes, Environment and Melanoma Study, 1,223 European-origin participants had their incident invasive primary melanomas screened for BRAF / NRAS mutations and germline DNA genotyped for 47 single-nucleotide polymorphisms identified as low-penetrant melanoma-risk variants. We used multinomial logistic regression to simultaneously examine each single-nucleotide polymorphism's relationship to BRAF V600E, BRAF V600K, BRAF other, and NRAS + relative to BRAF–/NRAS– melanoma adjusted for study features. IRF4 rs12203592*T was associated with BRAF V600E (odds ratio [OR] = 0.59, 95% confidence interval [CI] = 0.43–0.79) and V600K (OR = 0.65, 95% CI = 0.41–1.03), but not BRAF other or NRAS+ melanoma. A global test of etiologic heterogeneity ( P global = 0.001) passed false discovery ( P global = 0.0026). PLA2G6 rs132985*T was associated with BRAF V600E (OR = 1.32, 95% CI = 1.05–1.67) and BRAF other (OR = 1.82, 95% CI = 1.11–2.98), but not BRAF V600K or NRAS+ melanoma. The test for etiologic heterogeneity ( P global ) was 0.005. The IRF4 rs12203592 associations were slightly attenuated after adjustment for melanoma-risk phenotypes. The PLA2G6 rs132985 associations were independent of phenotypes. IRF4 and PLA2G6 inherited genotypes may influence melanoma BRAF/NRAS subtype development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.002 |
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".