Evidence map›Paper›PMID 33247094›Full record

ArticleNature communications2020

Pan-cancer analysis demonstrates that integrating polygenic risk scores with modifiable risk factors improves risk prediction.

Linda Kachuri, Rebecca E Graff, Karl Smith-Byrne, Travis J Meyers, Sara R Rashkin, Elad Ziv, John S Witte, Mattias Johansson

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
109citing papers in PubMed, 5 pooled it
20.9field-weighted citation impact, top 1% 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

109 citing papers in PubMed, 5 syntheses or guidelines pooled it, 188 citations in OpenAlex.

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  13. Polygenic risk scores in cancer.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2026
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49 more citing papers are in PubMed but not listed here.

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 2 institutions in 2 countries.

Linda KachuriDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
Rebecca E GraffDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.ORCID 0000-0003-0316-8303
Karl Smith-ByrneGenetic Epidemiology Group, Section of Genetics, International Agency for Research on Cancer, Lyon, France.
Travis J MeyersDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
Sara R RashkinDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.ORCID 0000-0001-5542-6891
Elad ZivDepartment of Medicine, University of California, San Francisco, San Francisco, CA, USA.
John S WitteDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA. jwitte@ucsf.edu.
Mattias JohanssonGenetic Epidemiology Group, Section of Genetics, International Agency for Research on Cancer, Lyon, France. JohanssonM@iarc.fr.ORCID 0000-0002-3116-5081
University of California, San Francisco · USCentre international de recherche sur le cancer · FR

Funding

Training in Molecular& Genetic Epidemiology of CancerR25CA112355 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI WITTE, JOHN S. · 2005 to 2018
$6.5M
Genome-wide Pleiotropy Scan across Multiple CancersR01CA201358 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GRAFF, REBECCA ELIZABETH, SAKODA, LORI · 2016 to 2019
$2.5M
Cancer Research UK 19169Cancer Research UK 29019Cancer Research UK C18281/A19169Cancer Research UK C18281/A29019NCI NIH HHS R01 CA201358NCI NIH HHS R25 CA112355World Health Organization 001
6 · The paper itself

Abstract

Cancer risk is determined by a complex interplay of environmental and heritable factors. Polygenic risk scores (PRS) provide a personalized genetic susceptibility profile that may be leveraged for disease prediction. Using data from the UK Biobank (413,753 individuals; 22,755 incident cancer cases), we quantify the added predictive value of integrating cancer-specific PRS with family history and modifiable risk factors for 16 cancers. We show that incorporating PRS measurably improves prediction accuracy for most cancers, but the magnitude of this improvement varies substantially. We also demonstrate that stratifying on levels of PRS identifies significantly divergent 5-year risk trajectories after accounting for family history and modifiable risk factors. At the population level, the top 20% of the PRS distribution accounts for 4.0% to 30.3% of incident cancer cases, exceeding the impact of many lifestyle-related factors. In summary, this study illustrates the potential for improving cancer risk assessment by integrating genetic risk scores.

Indexed as

Genetic Predisposition to DiseaseRisk AssessmentAdultAgedHumansMiddle AgedMultifactorial InheritanceProportional Hazards ModelsRisk Factors

Identifiers

PMID33247094
PMCPMC7695829
OpenAlexW3106839128

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.