Polygenic Risk Score Analysis of 37 SNPs Associated with Melanoma Risk in Colombian Population
Bibliographic record
Abstract
Melanoma incidence is increasing, with distinct genetic and clinical patterns observed in the Latin American population. This study aimed to evaluate melanoma risk in a Colombian cohort through polygenic risk analysis using 37 variants across nine genes previously associated with melanoma. We performed polygenic risk score (PRS) analysis on 85 melanoma patients and 165 controls. Genotyping was performed for 37 melanoma-associated SNPs, and on the basis of previous GWAS reports, individual PRSs were calculated for each participant. The participants were then stratified into quartiles to examine risk gradients. In addition, phenotypic features such as eye and hair color were evaluated, and genetic models and haplotype analyses were performed, adjusting for sex and family history of cancer. PRS quartile stratification revealed a clear risk gradient. Notably, 31.8% of the melanoma cases were clustered in the highest-risk quartile (Q4), with a maximum PRS of 1.04. Variants in TYR, TYRP1, CDKN2A, and HERC2 significantly contributed to risk, and light brown eye and hair colors were strongly associated with increased melanoma risk. Moreover, a protective haplotype in the OCA2-HERC2 region was identified among males. The integration of the PRS with clinical and phenotypic factors has potential for improving melanoma risk stratification in the Colombian population, warranting further investigation in larger, diverse cohorts.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".