Novel pleiotropic risk loci for melanoma and nevus density implicate multiple biological pathways
Bibliographic record
Abstract
Abstract The total number of acquired melanocytic nevi on the skin is strongly correlated with melanoma risk. Here we report a meta-analysis of 11 nevus GWAS from Australia, Netherlands, United Kingdom, and United States, comprising a total of 52,506 phenotyped individuals. We confirm known loci including MTAP , PLA2G6 , and IRF4 , and detect novel SNPs at a genome-wide level of significance in KITLG , DOCK8 , and a broad region of 9q32. In a bivariate analysis combining the nevus results with those from a recent melanoma GWAS meta-analysis (12,874 cases, 23,203 controls), SNPs near GPRC5A , CYP1B1 , PPARGC1B , HDAC4 , FAM208B and SYNE2 reached global significance, and other loci, including MIR146A and OBFC1 , reached a suggestive level of significance. Overall, we conclude that most nevus genes affect melanoma risk ( KITLG an exception), while many melanoma risk loci do not alter nevus count. For example, variants in TERC and OBFC1 affect both traits, but other telomere length maintenance genes seem to affect melanoma risk only. Our findings implicate multiple pathways in nevogenesis via genes we can show to be expressed under control of the MITF melanocytic cell lineage regulator.
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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) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".