Evidence map›Paper›PMID 41168411›Full record

ArticleScientific reports2025

Multiple polygenic score approach in colorectal cancer risk prediction.

Shangqing Joyce Jiang, Minta Thomas, Elisabeth A Rosenthal, Amanda I Phipps, Lori C Sakoda, Franzel J B van Duijnhoven, Andrew J Pellatt, Christy L Avery, Sonja I Berndt, D Timothy Bishop and 34 more

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

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3 · Its place in the literature

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1 citing paper in PubMed.

  1. Review
4 · The record

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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

44 authors.

Shangqing Joyce Jiang *University of Washington, Seattle, WA, USA.
Minta Thomas *Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Elisabeth A RosenthalDepartment of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, USA.
Amanda I PhippsPublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Lori C SakodaKaiser Permanente Division of Research, Oakland, CA, USA.
Franzel J B van DuijnhovenDivision of Human Nutrition and Health, Wageningen University & Research, Wageningen, The Netherlands.
Andrew J PellattIntermountain Health, Salt Lake City, UT, USA.
Christy L AveryDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Sonja I BerndtDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
D Timothy BishopLeeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK.
Sergi Castellví-BelGastroenterology Department, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain.
Andrew T ChanDivision of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Robert C GrantPrincess Margaret Cancer Centre, University Health Network, Toronto, Canada.
Chris GignouxColorado Center for Personalized Medicine, University of Colorado - Anschutz Medical Campus, Aurora, CO, USA.
Andrea GsurCenter for Cancer Research, Medical University of Vienna, Vienna, Austria.
Marc J GunterNutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France.
Christopher A HaimanCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Michael HoffmeisterDivision of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Gail P JarvikDepartment of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, USA.
Mark A JenkinsCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia.
Temitope O KekuCenter for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC, USA.
Sébastien KüryService de Génétique médicale, Nantes Université, CHU de Nantes, Nantes, F-44000, France.
Jeffrey K LeeKaiser Permanente Division of Research, Oakland, CA, USA.
Loic Le MarchandUniversity of Hawaii Cancer Center, Honolulu, HI, USA.
Victor MorenoOncology Data Analytics Program (ODAP), Unit of Biomarkers and Suceptibility (UBS), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, 08908, Spain.
Polly A NewcombPublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Christina C NewtonDepartment of Population Science, American Cancer Society, Atlanta, Georgia.
Shuji OginoBroad Institute of Harvard and MIT, Cambridge, MA, USA.
Julie R PalmerSlone Epidemiology Center, at Boston University, Boston, MA, USA.
Rachel PearlmanDivision of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA.
Conghui QuPublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Robert E SchoenDepartments of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Caroline Y UmDepartment of Population Science, American Cancer Society, Atlanta, Georgia.
Bethany Van GuelpenDepartment of Diagnostics and Intervention, Oncology Unit, Umeå University, Umeå, Sweden.
Kala VisvanathanDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Veronika VymetalkovaDepartment of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic.
Emily WhitePublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Michael O WoodsDiscipline of Genetics, Memorial University of Newfoundland, St. John's, Canada.
Elizabeth A PlatzDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Hermann BrennerDivision of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Douglas A CorleyKaiser Permanente Division of Research, Oakland, CA, USA.
Iris Landorp VogelaarDepartment of Public Health, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
Li HsuPublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Ulrike PetersPublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA. upeters@fredhutch.org.

Funding

Molecular pathological epidemiology of colorectal cancerU01CA137088 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI PETERS, ULRIKE · 2009 to 2018
$21.8M
Variation, Function, and Disease Supplement ProgramU01HG008657 · NHGRI · UNIVERSITY OF WASHINGTON · PI David Russell Crosslin, Gail Pairitz Jarvik · 2015 to 2026
$13.4M
Ceramides as novel drivers of metabolic dysfunction and colorectal cancerU01CA272529 · NCI · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI Mary Christine Playdon, SCOTT A SUMMERS · 2022 to 2026
$4.7M
NCI NIH HHS U01 CA137088NCI NIH HHS U01 CA164973, R01 CA126895, R01 CA060987, R01 CA072520, U24 CA074806NCI NIH HHS U01 CA272529NHGRI NIH HHS U01HG008657World Health Organization 001
6 · The paper itself

Abstract

Recent studies have demonstrated that for various diseases, incorporating polygenic risk scores (PRSs) for other traits and diseases into the PRS-based risk prediction model may improve predictive performance - known as Multiple Polygenic Score (MPS) approach. We aimed to examine whether the MPS approach improves colorectal cancer (CRC) risk prediction. We included 2,187 non-CRC PRSs from the polygenic Score (PGS) Catalog and used machine learning (ML) models to select the most predictive non-CRC PRSs, utilizing individual-level data from 31,257 CRC cases and 33,408 controls. An independent dataset from the Genetic Epidemiology Research in Adult Health and Aging (GERA) cohort (4,852 cases and 67,939 controls) was randomly split into subsets for model estimation and validation. The model combined MPS with two existing CRC-PRSs based on known loci and genome-wide genotyping. We then assessed model performance by calculating the area under the receiver operating curve (AUC) in the validation set and performed 1,000 bootstrapped iterations to evaluate AUC improvements. The ML model selected 337 non-CRC PRSs predictive of CRC risk. Adding MPS to the CRC-PRSs significantly improved AUC by 0.017 (95% CI: 0.011-0.022, p < 0.0001) when combined with known-loci CRC-PRS, 0.005 (95% CI: 0.002-0.007, p = 0.0005) with genome-wide CRC-PRS, and 0.004 (95% CI: 0.002-0.006, p = 0.0005) with both the known loci and genome-wide CRC-PRSs. These findings demonstrate MPS's potential to refine CRC risk prediction models and highlight opportunities for further advancements in risk prediction.

Indexed as

Colorectal NeoplasmsGenetic Predisposition to DiseaseMultifactorial InheritanceAgedCase-Control StudiesFemaleGenome-Wide Association StudyHumansMachine LearningMaleMiddle AgedPolymorphism, Single NucleotideRisk AssessmentRisk FactorsROC CurveColorectal cancerMulti-trait PRSPolygenic risk score

Identifiers

PMID41168411
PMCPMC12575652

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