Impact of <i>BRCA</i> mutations on female fertility and offspring sex ratio
Bibliographic record
Abstract
Positive selection for inherited mutations in breast and ovarian cancer predisposing genes, BRCA1 and BRCA2, may contribute to the high frequency of BRCA mutations among the Ashkenazi Jewish population. Impact of BRCA mutations on fertility has not been generally explored in epidemiologic studies. There are reports of distorted sex ratios in BRCA carrier families but these findings have been attributed to bias. We investigated the effect of BRCA mutations on female fertility and offspring sex ratio in a study of 260 Ashkenazi Jewish women with ovarian cancer and 331 controls, unselected for age or family history of the disease. Pregnancy success was similar for 96 mutation carrier (0.84) and 164 noncarrier cases (0.87) and controls (0.83). After adjusting for covariates, there were no significant differences between BRCA carrier and noncarrier cases and controls with regards to fertility, despite lower pregnancy rates among all cases compared to controls (P = 0.0049). Male/female sex ratios were significantly lower among offspring of carriers (0.71) than offspring of noncarriers (0.95) or those of the controls (0.99). Comparisons among the three groups yielded statistically significant distortion against males among the offspring of known and obligate BRCA carriers compared to noncarriers (OR = 0.74, 95% CI:0.55-0.99) and controls (OR = 0.71, 95% CI:0.54-0.94). In conclusion, we did not find evidence for an effect of BRCA mutations on female fertility. We found a significant excess of females among the offspring of female carriers of BRCA1 and BRCA2 mutations. Potential contribution of observed sex ratio distortions to positive selection for BRCA mutations may warrant further investigation.
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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 | 0.004 |
| 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) | 0.001 | 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".