Multiple polygenic score approach in colorectal cancer risk prediction
Jazyk angličtina Země Anglie, Velká Británie Médium electronic
Typ dokumentu časopisecké články
Grantová podpora
U01HG008657
NHGRI NIH HHS - United States
U01 CA164973, R01 CA126895, R01 CA060987, R01 CA072520, U24 CA074806
NCI NIH HHS - United States
U01 CA137088
NCI NIH HHS - United States
PubMed
41168411
PubMed Central
PMC12575652
DOI
10.1038/s41598-025-21956-w
PII: 10.1038/s41598-025-21956-w
Knihovny.cz E-zdroje
- Klíčová slova
- Colorectal cancer, Multi-trait PRS, Polygenic risk score,
- MeSH
- celogenomová asociační studie MeSH
- genetická predispozice k nemoci * MeSH
- hodnocení rizik MeSH
- jednonukleotidový polymorfismus MeSH
- kolorektální nádory * genetika epidemiologie MeSH
- lidé středního věku MeSH
- lidé MeSH
- multifaktoriální dědičnost * genetika MeSH
- rizikové faktory MeSH
- ROC křivka MeSH
- senioři MeSH
- strojové učení MeSH
- studie případů a kontrol MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
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.
Broad Institute of Harvard and MIT Cambridge MA USA
Center for Cancer Research Medical University of Vienna Vienna Austria
Center for Gastrointestinal Biology and Disease University of North Carolina Chapel Hill NC USA
Consortium for Biomedical Research in Epidemiology and Public Health Madrid 28029 Spain
Department of Biostatistics University of Washington Seattle WA USA
Department of Diagnostics and Intervention Oncology Unit Umeå University Umeå Sweden
Department of Epidemiology Harvard T H Chan School of Public Health Harvard University Boston MA USA
Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD USA
Department of Epidemiology University of North Carolina at Chapel Hill Chapel Hill NC USA
Department of Epidemiology University of Washington Seattle WA USA
Department of Gastroenterology Kaiser Permanente San Francisco Medical Center San Francisco CA USA
Department of Genome Sciences University of Washington School of Medicine Seattle WA USA
Department of Medicine Division of Medical Genetics University of Washington Seattle WA USA
Department of Population Science American Cancer Society Atlanta Georgia
Department of Public Health Erasmus MC University Medical Center Rotterdam The Netherlands
Departments of Medicine and Epidemiology University of Pittsburgh Medical Center Pittsburgh PA USA
Discipline of Genetics Memorial University of Newfoundland St John's Canada
Division of Gastroenterology Massachusetts General Hospital Harvard Medical School Boston MA USA
Division of Gastroenterology University of California San Francisco San Francisco CA USA
Division of Human Nutrition and Health Wageningen University and Research Wageningen The Netherlands
German Cancer Consortium Heidelberg Germany
Institute of Science Tokyo Tokyo Japan
Intermountain Health Salt Lake City UT USA
Kaiser Permanente Division of Research Oakland CA USA
Leeds Institute of Cancer and Pathology University of Leeds Leeds UK
Oncology Data Analytics Program L'Hospitalet del Llobregat Barcelona 08908 Spain
Princess Margaret Cancer Centre University Health Network Toronto Canada
Public Health Sciences Division Fred Hutchinson Cancer Center Seattle WA USA
School of Public Health University of Washington Seattle WA USA
Service de Génétique médicale Nantes Université CHU de Nantes Nantes F 44000 France
Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins Baltimore MD USA
Slone Epidemiology Center at Boston University Boston MA USA
University of Hawaii Cancer Center Honolulu HI USA
University of Washington Seattle WA USA
Wallenberg Centre for Molecular Medicine Umeå University Umeå Sweden
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