Deciphering the Genomic Architecture of Three Major Cancers in African-Ancestry Populations
Bibliographic record
Abstract
Abstract Genomic studies of cancer risk have disproportionately focused on populations of European ancestry, limiting biological insight and risk prediction in African-ancestry populations that experience a high burden of disease. Here, we analysed breast, colorectal, and prostate cancers in African-ancestry participants from the UK Biobank using ancestry-aware genome-wide association studies (GWAS), SNP-based heritability estimation, fine-mapping, transcriptome-wide association studies (TWAS), and polygenic risk scoring (PRS). SNP-based heritability analyses revealed a comparatively high point estimate of common-variant heritability for colorectal cancer risk in African-ancestry individuals, alongside more modest estimates for breast and prostate cancer. Five loci reached genome-wide significance ( p < 5×10−□), including four colorectal cancer loci (notably rs111448231 in RYR2 ) and one novel breast cancer locus (rs78768133). Gene-based burden testing identified eight prostate cancer-associated genes ( MRPL45, PSMD8, GGN, SPRED3, FAM98C, BCLAF1, MTFR2, and NELL2 ) with FDR-significant associations, clustering within biologically plausible chromosomal regions on chr19q13 and chr6q23. Transcriptome-wide association analysis identified CYTH2 (ENSG00000105443.13) as a significant gene for prostate cancer. Polygenic risk scores incorporating African-ancestry linkage disequilibrium demonstrated heterogeneous predictive performance across cancers, with modest discrimination for colorectal and breast cancer and substantially stronger performance for prostate cancer (AUC = 0.89). Together, these findings delineate ancestry-relevant cancer genetic architectures and demonstrate the importance of population-matched genomic approaches for equitable precision oncology.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".