Population-Based Study of Natural Variation in the <i>Melanocortin-1 Receptor</i> Gene and Melanoma
Bibliographic record
Abstract
Natural variation in the coding region of the melanocortin-1 receptor (MC1R) gene is associated with constitutive pigmentation phenotypes and development of melanoma and nonmelanoma skin cancers. We investigated the effect of MC1R variants on melanoma using a large, international population-based study design with complete determination of all MC1R coding region variants. Direct sequencing was completed for 2,202 subjects with a single primary melanoma (controls) and 1,099 subjects with second or higher-order primary melanomas (cases) from Australia, the United States, Canada, and Italy. We observed 85 different MC1R variants, 10 of which occurred at a frequency >1%. Compared with controls, cases were more likely to carry two previously identified red hair ("R") variants [D84E, R151C, R160W, and D294H; odds ratio (OR), 1.6; 95% confidence interval (95% CI), 1.1-2.2]. This effect was similar among individuals carrying one R variant and one r variant (defined as any non-R MC1R variant; OR, 1.6; 95% CI, 1.3-2.2) and among those carrying only one R variant (OR, 1.5; 95% CI, 1.1-1.9). There was no statistically significant association among those carrying only one or two r variants. Effects were similar across geographic regions and categories of pigmentation characteristics or number of moles. Our results confirm that MC1R is a low-penetrance susceptibility locus for melanoma, show that pigmentation characteristics may not modify the relationship of MC1R variants and melanoma risk, and suggest that associations may be smaller than previously reported in part due to the study design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".