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@ARTICLE{Jiang:305609,
      author       = {S. J. Jiang and M. Thomas and E. A. Rosenthal and A. I.
                      Phipps and L. C. Sakoda and F. J. B. van Duijnhoven and A.
                      J. Pellatt and C. L. Avery and S. I. Berndt and D. T. Bishop
                      and S. Castellví-Bel and A. T. Chan and R. C. Grant and C.
                      Gignoux and A. Gsur and M. J. Gunter and C. A. Haiman and M.
                      Hoffmeister$^*$ and G. P. Jarvik and M. A. Jenkins and T. O.
                      Keku and S. Küry and J. K. Lee and L. L. Marchand and V.
                      Moreno and P. A. Newcomb and C. C. Newton and S. Ogino and
                      J. R. Palmer and R. Pearlman and C. Qu and R. E. Schoen and
                      C. Y. Um and B. Van Guelpen and K. Visvanathan and V.
                      Vymetalkova and E. White and M. O. Woods and E. A. Platz and
                      H. Brenner$^*$ and D. A. Corley and I. L. Vogelaar and L.
                      Hsu and U. Peters},
      title        = {{M}ultiple polygenic score approach in colorectal cancer
                      risk prediction.},
      journal      = {Scientific reports},
      volume       = {15},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {DKFZ-2025-02259},
      pages        = {38006},
      year         = {2025},
      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.},
      keywords     = {Humans / Colorectal Neoplasms: genetics / Colorectal
                      Neoplasms: epidemiology / Multifactorial Inheritance:
                      genetics / Genetic Predisposition to Disease / Female / Male
                      / Machine Learning / Aged / Middle Aged / Risk Factors /
                      Risk Assessment / Genome-Wide Association Study / ROC Curve
                      / Polymorphism, Single Nucleotide / Case-Control Studies /
                      Colorectal cancer (Other) / Multi-trait PRS (Other) /
                      Polygenic risk score (Other)},
      cin          = {C070 / HD01},
      ddc          = {600},
      cid          = {I:(DE-He78)C070-20160331 / I:(DE-He78)HD01-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41168411},
      pmc          = {pmc:PMC12575652},
      doi          = {10.1038/s41598-025-21956-w},
      url          = {https://inrepo02.dkfz.de/record/305609},
}