Common Genetic Variation at <i>BARD1</i> Is Not Associated with Breast Cancer Risk in <i>BRCA1</i> or <i>BRCA2</i> Mutation Carriers
Bibliographic record
Abstract
BACKGROUND: Inherited BRCA1 and BRCA2 (BRCA1/2) mutations confer elevated breast cancer risk. Knowledge of factors that can improve breast cancer risk assessment in BRCA1/2 mutation carriers may improve personalized cancer prevention strategies. METHODS: A cohort of 5,546 BRCA1 and 2,865 BRCA2 mutation carriers was used to evaluate risk of breast cancer associated with BARD1 Cys557Ser. In a second nonindependent cohort of 1,537 of BRCA1 and 839 BRCA2 mutation carriers, BARD1 haplotypes were also evaluated. RESULTS: The BARD1 Cys557Ser variant was not significantly associated with risk of breast cancer from single SNP analysis, with a pooled effect estimate of 0.90 (95% CI: 0.71-1.15) in BRCA1 carriers and 0.87 (95% CI: 0.59-1.29) in BRCA2 carriers. Further analysis of haplotypes at BARD1 also revealed no evidence that additional common genetic variation not captured by Cys557Ser was associated with breast cancer risk. CONCLUSION: Evidence to date does not support a role for BARD1 variation, including the Cy557Ser variant, as a modifier of risk in BRCA1/2 mutation carriers. IMPACT: Interactors of BRCA1/2 have been implicated as modifiers of BRCA1/2-associated cancer risk. Our finding that BARD1 does not contribute to this risk modification may focus research on other genes that do modify BRCA1/2-associated cancer risk.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".