Polygenic risk score is associated with increased disease risk in 52 Finnish breast cancer families
Bibliographic record
Abstract
The risk of developing breast cancer is increased in women with family history of breast cancer and particularly in families with multiple cases of breast or ovarian cancer. Nevertheless, many women with a positive family history never develop the disease. Polygenic risk scores (PRSs) based on the risk effects of multiple common genetic variants have been proposed for individual risk assessment on a population level. We investigate the applicability of the PRS for risk prediction within breast cancer families. We studied the association between breast cancer risk and a PRS based on 75 common genetic variants in 52 Finnish breast cancer families including 427 genotyped women and pedigree information on ~4000 additional individuals by comparing the affected to healthy family members, as well as in a case-control dataset comprising 1272 healthy population controls and 1681 breast cancer cases with information on family history. Family structure was summarized using the BOADICEA risk prediction model. The PRS was associated with increased disease risk in women with family history of breast cancer as well as in women within the breast cancer families. The odds ratio (OR) for breast cancer within the family dataset was 1.55 [95 % CI 1.26-1.91] per unit increase in the PRS, similar to OR in unselected breast cancer cases of the case-control dataset (1.49 [1.38-1.62]). High PRS-values were informative for risk prediction in breast cancer families, whereas for the low PRS-categories the results were inconclusive. The PRS is informative in women with family history of breast cancer and should be incorporated within pedigree-based clinical risk assessment.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".