Pathogenic variants reveal candidate genes for prostate cancer germline testing for men of African ancestry
Bibliographic record
Abstract
Prostate cancer (PCa) germline testing, while gaining momentum, is ancestry restrictive and African exclusive. Through whole genome sequencing for 217 African ancestral cases (186 southern African, 31 Pan representative), we identify 172 potentially pathogenic variants in 78 DNA damage repair or PCa related genes. Prevalence for reported (13/217, 5.99%) and cumulative predicted (24/217, 11.06%) variants of significance (11 genes) falls below that reported for non-Africans. Conversely, BRCA1, HOXB13, CDK12, MLH1, MSH2, and BRIP1 remain unimpacted. Through pathogenic ranking based on variant frequency and functionality, clinical presentation and tumour-matched biallelic inactivation, top-ranked candidates include PREX2, POLE, FAT1, BRCA2, POLQ, LRP1B and ATM. Besides notable impact of DNA polymerases, including POLG, Fanconi anaemia genes include FANCD2, FANCA, FANCG, ERCC4, FANCE and FANCI, while DNA mismatch repair genes MSH3 and PMS1 outranked known namesakes MSH6 and PMS2. This study provides insights into the spectrum of African-relevant potentially pathogenic PCa variants, highlighting much-needed gene candidates for ancestry-inclusive germline testing.
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 | 0.002 |
| 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) | 0.005 | 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".