Identification of novel epithelial ovarian cancer loci in women of African ancestry
Bibliographic record
Abstract
Women of African ancestry have lower incidence of epithelial ovarian cancer (EOC) yet worse survival compared to women of European ancestry. We conducted a genome‐wide association study in African ancestry women with 755 EOC cases, including 537 high‐grade serous ovarian carcinomas (HGSOC) and 1,235 controls. We identified four novel loci with suggestive evidence of association with EOC ( p < 1 × 10 −6 ), including rs4525119 (intronic to AKR1C3 ), rs7643459 (intronic to LOC101927394 ), rs4286604 (12 kb 3′ of UGT2A2 ) and rs142091544 (5 kb 5′ of WWC1 ). For HGSOC, we identified six loci with suggestive evidence of association including rs37792 (132 kb 5′ of follistatin [ FST ]), rs57403204 (81 kb 3′ of MAGEC1 ), rs79079890 ( LOC105376360 intronic), rs66459581 (5 kb 5′ of PRPSAP1 ), rs116046250 ( GABRG3 intronic) and rs192876988 (32 kb 3′ of GK2 ). Among the identified variants, two are near genes known to regulate hormones and diseases of the ovary ( AKR1C3 and FST ), and two are linked to cancer ( AKR1C3 and MAGEC1 ). In follow‐up studies of the 10 identified variants, the GK2 region SNP, rs192876988, showed an inverse association with EOC in European ancestry women ( p = 0.002), increased risk of ER positive breast cancer in African ancestry women ( p = 0.027) and decreased expression of GK2 in HGSOC tissue from African ancestry women ( p = 0.004). A European ancestry‐derived polygenic risk score showed positive associations with EOC and HGSOC in women of African ancestry suggesting shared genetic architecture. Our investigation presents evidence of variants for EOC shared among European and African ancestry women and identifies novel EOC risk loci in women of African ancestry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".