A multi-phenotype analysis reveals 19 novel susceptibility loci for basal cell carcinoma and 15 for squamous cell carcinoma
Bibliographic record
Abstract
ABSTRACT Basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) are the most common forms of skin cancer. There is genetic overlap between skin cancers, pigmentation traits, and autoimmune diseases. We use linkage disequilibrium score regression to identify 20 traits (melanoma, pigmentation traits, autoimmune diseases, and blood biochemistry biomarkers) with a high genetic correlation ( r g > 10%, P < 0.05) with BCC (20,791 cases and 286,893 controls in the UK Biobank) and SCC (7,402 cases and 286,892 controls in the UK Biobank), and use a multi-trait genetic analysis to identify 78 and 69 independent genome-wide significant (P < 5 × 10 -8 ) susceptibility loci for BCC and SCC respectively; 19 BCC and 15 SCC loci are both novel and replicated (P < 0.05) in a large independent cohort; 23andMe, Inc (BCC: 251,963 cases and 2,271,667 controls, and SCC: 134,700 cases and 2,394,699 controls. Novel loci are implicated in BCC/SCC development and progression (e.g. CDKL1 ), pigmentation (e.g. DSTYK ), cardiometabolic pathways (e.g. FADS2 ), and immune-regulatory pathways including; innate immunity against coronaviruses (e.g. IFIH1 ), and HIV-1 viral load modulation and disease progression (e.g. CCR5 ). We also report a powerful and optimised BCC polygenic risk score that enables effective risk stratification for keratinocyte cancer in a large prospective Canadian Longitudinal Study of Aging (794 cases and 18139 controls); e.g. percentage of participants reclassified; MTAG PRS = 36.57%, 95% CI = 35.89-37.26% versus UKB PRS = 33.23%, 95% CI=32.56-33.91%).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".