Genetic Variation at 9p22.2 and Ovarian Cancer Risk for BRCA1 and BRCA2 Mutation Carriers
Bibliographic record
Abstract
BACKGROUND: Germline mutations in the BRCA1 and BRCA2 genes are associated with increased risks of breast and ovarian cancers. Although several common variants have been associated with breast cancer susceptibility in mutation carriers, none have been associated with ovarian cancer susceptibility. A genome-wide association study recently identified an association between the rare allele of the single-nucleotide polymorphism (SNP) rs3814113 (ie, the C allele) at 9p22.2 and decreased risk of ovarian cancer for women in the general population. We evaluated the association of this SNP with ovarian cancer risk among BRCA1 or BRCA2 mutation carriers by use of data from the Consortium of Investigators of Modifiers of BRCA1/2. METHODS: We genotyped rs3814113 in 10,029 BRCA1 mutation carriers and 5837 BRCA2 mutation carriers. Associations with ovarian and breast cancer were assessed with a retrospective likelihood approach. All statistical tests were two-sided. RESULTS: The minor allele of rs3814113 was associated with a reduced risk of ovarian cancer among BRCA1 mutation carriers (per-allele hazard ratio of ovarian cancer = 0.78, 95% confidence interval = 0.72 to 0.85; P = 4.8 × 10(-9)) and BRCA2 mutation carriers (hazard ratio of ovarian cancer = 0.78, 95% confidence interval = 0.67 to 0.90; P = 5.5 × 10(-4)). This SNP was not associated with breast cancer risk among either BRCA1 or BRCA2 mutation carriers. BRCA1 mutation carriers with the TT genotype at SNP rs3814113 were predicted to have an ovarian cancer risk to age 80 years of 48%, and those with the CC genotype were predicted to have a risk of 33%. CONCLUSION: Common genetic variation at the 9p22.2 locus was associated with decreased risk of ovarian cancer for carriers of a BRCA1 or BRCA2 mutation.
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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".