A Haplotype-Based Case-Control Study of <i>BRCA1</i> and Sporadic Breast Cancer Risk
Bibliographic record
Abstract
Rare, highly penetrant germ line mutations in BRCA1 strongly predispose women to a familial form of breast and ovarian cancer. Whether common variants (either coding or noncoding) at this locus contribute to the more common form of the disease is not yet known. We tested common variation across the BRCA1 locus in African American, Native Hawaiian, Japanese, Latino, and White women in the Multiethnic Cohort Study. Specifically, 28 single nucleotide polymorphisms (SNPs) spanning the BRCA1 gene were used to define patterns of common variation in these populations. The majority of SNPs were in strong linkage disequilibrium with one another, indicating that our survey captured most of the common inherited variation across this gene. Nine tagging SNPs, including five missense SNPs, were selected to predict the common BRCA1 variants and haplotypes among the non-African American groups (five additional SNPs were required for African Americans) and genotyped in a breast cancer case-control study nested in the Multiethnic Cohort Study (cases, n = 1,715; controls, n = 2,502). We found no evidence for significant associations between common variation in BRCA1 and risk of breast cancer. Given the large size of our study population and detailed analysis of the locus, this result indicates either that common variants in BRCA1 do not substantially influence sporadic breast cancer risk, or that unmeasured heterogeneity in the breast cancer phenotype or unmeasured interactions with genetic or environmental exposures obscure our ability to detect any influence that may be present.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".