Potential Misrepresentation of Inherited Breast Cancer Risk by Common Germline Alleles
Bibliographic record
Abstract
Abstract Hundreds of common variants have been found to confer small but significant differences in breast cancer risk, supporting the polygenic additive model of inherited risk. This widely accepted model is at odds with twin data indicating highly elevated risk in a subgroup of women. Using a novel closed-pattern-mining algorithm, we provide evidence that rare variants or haplotypes may underlie the association of breast cancer risk with common germline alleles. Our method, called Chromosome Overlap, consists in iteratively pairing chromosomes from affected individuals and looking for noncontiguous patterns of shared alleles without exhaustive enumeration. We applied Chromosome Overlap to haplotypes of genotyped SNPs from 9,011 female breast cancer cases from the UK Biobank (UKBB) at three topologically associating domains containing well-established common-allele “hits” for breast cancer. A total of 181,034 UKBB women of “white British” ancestry were used to assess the discovered haplotypes, and 55,346 cases and controls of European ancestry in the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) case-control study were used for replication. Out of twenty rare (frequency < ∼0.1%) risk haplotypes of large effect identified in UKBB at P < 1.0 × 10 −5 , four (hazard ratio: 4.22–20.2) were subsequently replicated in DRIVE (odds ratio: 2.13–11.9) at P < 0.05. Our results support the genetic heterogeneity and rare-variant/haplotype basis of breast cancer risk and suggest a novel type of “synthetic association” wherein common risk alleles on a rare risk haplotype may misrepresent disease risk through their tagging of many “false positive” haplotypes. Significance Chromosome Overlap reveals that common alleles identified by GWAS may be poor surrogates for underlying high-risk haplotypes, necessitating a reappraisal of the polygenic model of disease 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".