Inference on the Genetic Architecture of Breast Cancer Risk
Bibliographic record
Abstract
BACKGROUND: What are the major determinants of women's breast cancer risk? Rare mutations such as those in the BRCA1/2 genes, polygenic scores of common alleles identified by genome-wide association studies, or nongenetic factors? METHODS: The population-based Nordic Twin Study of Cancer, with 3,933 breast cancer cases among 21,054 monozygotic (MZ) and 30,939 dizygotic (DZ) female twin pairs, provides three key clues to this question: (i) the average lifetime risk, approximately 8%, does not differ by twin zygosity; (ii) the mean time interval between diagnoses when both twins develop disease (i.e., disease concordance) also does not differ by zygosity; but, (iii) conditioning on one twin having developed disease, the incidence rate in the co-twin is approximately 1% per year if the pair is MZ and 0.5% per year if DZ. RESULTS: Assuming that nongenetic risk factors are shared similarly between twins regardless of zygosity, we can draw two conclusions from (i) to (iii). CONCLUSIONS: First, (i) and (iii) imply that the chief determinant of risk is in the germline DNA, because the conditional incidence rate is several-fold higher than the average risk (8% lifetime) in MZ twins but only half as much in DZ twins. Second, the seeming inconsistency between the two-fold conditional incidence rate (iii) and the equality of the mean inter-twin disease intervals in disease concordance (ii) can be resolved if the risk factors in the germline DNA are rare variants, not common variants. IMPACT: This paper details simple deductive reasoning for these conclusions and draws a critical inference regarding breast cancer etiology. See related In the Spotlight, p. 1477.
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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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".