Evidence map›Paper›PMID 34230637›Full record

ArticleGenetics in medicine : official journal of the American College of Medical Genetics2021

Potential of polygenic risk scores for improving population estimates of women's breast cancer genetic risks.

Michael Wolfson, Steve Gribble, Nora Pashayan, Douglas F Easton, Antonis C Antoniou, Andrew Lee, Sasha van Katwyk, Jacques Simard

Open access · hybridAbstract read
In one paragraph

Article in Genetics in medicine : official journal of the American College of Medical Genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
2.8field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 24 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 2 countries.

Michael WolfsonSchool of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada. mwolfson@uottawa.ca.ORCID http://orcid.org/0000-0003-1941-955X
Steve GribbleSchool of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada.
Nora PashayanDepartment of Applied Health Research, University College London, London, UK.
Douglas F EastonDepartment of Public Health and Primary Care, Cambridge, UK.
Antonis C AntoniouDepartment of Oncology, University of Cambridge, Cambridge, UK.
Andrew LeeDepartment of Public Health and Primary Care, Cambridge, UK.
Sasha van KatwykSchool of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada.
Jacques SimardDepartment of Molecular Medicine, Université Laval, Quebec City, Canada.
University of Ottawa · CAUniversity of Cambridge · GBUniversité Laval · CAUniversity College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeBreast cancer risk has conventionally been assessed using family history (FH) and rare high/moderate penetrance pathogenic variants (PVs), notably in BRCA1/2, and more recently PALB2, CHEK2, and ATM. In addition to these PVs, it is now possible to use increasingly predictive polygenic risk scores (PRS) as well. The comparative population-level predictive capability of these three different indicators of genetic risk for risk stratification is, however, unknown.

methodsThe Canadian heritable breast cancer risk distribution was estimated using a novel genetic mixing model (GMM). A realistically representative sample of women was synthesized based on empirically observed demographic patterns for appropriately correlated family history, inheritance of rare PVs, PRS, and residual risk from an unknown polygenotype. Risk assessment was simulated using the BOADICEA risk algorithm for 10-year absolute breast cancer incidence, and compared to heritable risks as if the overall polygene, including its measured PRS component, and PV risks were fully known.

resultsGenerally, the PRS was most predictive for identifying women at high risk, while family history was the weakest. Only the PRS identified any women at low risk of breast cancer.

conclusionPRS information would be the most important advance in enabling effective risk stratification for population-wide breast cancer screening.

Indexed as

Breast NeoplasmsBRCA2 ProteinCanadaFemaleGenetic Predisposition to DiseaseHumansRisk AssessmentRisk FactorsBRCA2 Protein

Identifiers

PMID34230637
PMCPMC8553614
OpenAlexW3177959273

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.