TY  - JOUR
AU  - Jiang, Shangqing Joyce
AU  - Thomas, Minta
AU  - Rosenthal, Elisabeth A
AU  - Phipps, Amanda I
AU  - Sakoda, Lori C
AU  - van Duijnhoven, Franzel J B
AU  - Pellatt, Andrew J
AU  - Avery, Christy L
AU  - Berndt, Sonja I
AU  - Bishop, D Timothy
AU  - Castellví-Bel, Sergi
AU  - Chan, Andrew T
AU  - Grant, Robert C
AU  - Gignoux, Chris
AU  - Gsur, Andrea
AU  - Gunter, Marc J
AU  - Haiman, Christopher A
AU  - Hoffmeister, Michael
AU  - Jarvik, Gail P
AU  - Jenkins, Mark A
AU  - Keku, Temitope O
AU  - Küry, Sébastien
AU  - Lee, Jeffrey K
AU  - Marchand, Loic Le
AU  - Moreno, Victor
AU  - Newcomb, Polly A
AU  - Newton, Christina C
AU  - Ogino, Shuji
AU  - Palmer, Julie R
AU  - Pearlman, Rachel
AU  - Qu, Conghui
AU  - Schoen, Robert E
AU  - Um, Caroline Y
AU  - Van Guelpen, Bethany
AU  - Visvanathan, Kala
AU  - Vymetalkova, Veronika
AU  - White, Emily
AU  - Woods, Michael O
AU  - Platz, Elizabeth A
AU  - Brenner, Hermann
AU  - Corley, Douglas A
AU  - Vogelaar, Iris Landorp
AU  - Hsu, Li
AU  - Peters, Ulrike
TI  - Multiple polygenic score approach in colorectal cancer risk prediction.
JO  - Scientific reports
VL  - 15
IS  - 1
SN  - 2045-2322
CY  - [London]
PB  - Springer Nature
M1  - DKFZ-2025-02259
SP  - 38006
PY  - 2025
AB  - 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
KW  - Humans
KW  - Colorectal Neoplasms: genetics
KW  - Colorectal Neoplasms: epidemiology
KW  - Multifactorial Inheritance: genetics
KW  - Genetic Predisposition to Disease
KW  - Female
KW  - Male
KW  - Machine Learning
KW  - Aged
KW  - Middle Aged
KW  - Risk Factors
KW  - Risk Assessment
KW  - Genome-Wide Association Study
KW  - ROC Curve
KW  - Polymorphism, Single Nucleotide
KW  - Case-Control Studies
KW  - Colorectal cancer (Other)
KW  - Multi-trait PRS (Other)
KW  - Polygenic risk score (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:41168411
C2  - pmc:PMC12575652
DO  - DOI:10.1038/s41598-025-21956-w
UR  - https://inrepo02.dkfz.de/record/305609
ER  -