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@ARTICLE{Thomas:157420,
      author       = {M. Thomas and L. C. Sakoda and M. Hoffmeister$^*$ and E. A.
                      Rosenthal and J. K. Lee and F. J. B. van Duijnhoven and E.
                      A. Platz and A. H. Wu and C. H. Dampier and A. de la
                      Chapelle and A. Wolk and A. D. Joshi and A. Burnett-Hartman
                      and A. Gsur and A. Lindblom and A. Castells and A. K. Win
                      and B. Namjou and B. Van Guelpen and C. M. Tangen and Q. He
                      and C. I. Li and C. Schafmayer and C. E. Joshu and C. M.
                      Ulrich and D. T. Bishop and D. D. Buchanan and D. Schaid and
                      D. A. Drew and D. C. Muller and D. Duggan and D. R. Crosslin
                      and D. Albanes and E. L. Giovannucci and E. Larson and F. Qu
                      and F. Mentch and G. G. Giles and H. Hakonarson and H.
                      Hampel and I. B. Stanaway and J. C. Figueiredo and J. R.
                      Huyghe and J. Minnier and J. Chang-Claude$^*$ and J. Hampe
                      and J. B. Harley and K. Visvanathan and K. R. Curtis and K.
                      Offit and L. Li and L. Le Marchand and L. Vodickova and M.
                      J. Gunter and M. A. Jenkins and M. L. Slattery and M. Lemire
                      and M. O. Woods and M. Song and N. Murphy and N. M. Lindor
                      and O. Dikilitas and P. D. P. Pharoah and P. T. Campbell and
                      P. A. Newcomb and R. L. Milne and R. J. MacInnis and S.
                      Castellví-Bel and S. Ogino and S. I. Berndt and S. Bézieau
                      and S. N. Thibodeau and S. J. Gallinger and S. H. Zaidi and
                      T. A. Harrison and T. O. Keku and T. J. Hudson and V.
                      Vymetalkova and V. Moreno and V. Martín and V. Arndt$^*$
                      and W.-Q. Wei and W. Chung and Y.-R. Su and R. B. Hayes and
                      E. White and P. Vodicka and G. Casey and S. B. Gruber and R.
                      E. Schoen and A. T. Chan and J. D. Potter and H. Brenner$^*$
                      and G. P. Jarvik and D. A. Corley and U. Peters and L. Hsu},
      title        = {{G}enome-wide {M}odeling of {P}olygenic {R}isk {S}core in
                      {C}olorectal {C}ancer {R}isk.},
      journal      = {The American journal of human genetics},
      volume       = {107},
      number       = {3},
      issn         = {0002-9297},
      address      = {New York, NY},
      publisher    = {Elsevier},
      reportid     = {DKFZ-2020-01615},
      pages        = {432-444},
      year         = {2020},
      note         = {2020 Sep 3;107(3):432-444},
      abstract     = {Accurate colorectal cancer (CRC) risk prediction models are
                      critical for identifying individuals at low and high risk of
                      developing CRC, as they can then be offered targeted
                      screening and interventions to address their risks of
                      developing disease (if they are in a high-risk group) and
                      avoid unnecessary screening and interventions (if they are
                      in a low-risk group). As it is likely that thousands of
                      genetic variants contribute to CRC risk, it is clinically
                      important to investigate whether these genetic variants can
                      be used jointly for CRC risk prediction. In this paper, we
                      derived and compared different approaches to generating
                      predictive polygenic risk scores (PRS) from genome-wide
                      association studies (GWASs) including 55,105 CRC-affected
                      case subjects and 65,079 control subjects of European
                      ancestry. We built the PRS in three ways, using (1) 140
                      previously identified and validated CRC loci; (2) SNP
                      selection based on linkage disequilibrium (LD) clumping
                      followed by machine-learning approaches; and (3) LDpred, a
                      Bayesian approach for genome-wide risk prediction. We tested
                      the PRS in an independent cohort of 101,987 individuals with
                      1,699 CRC-affected case subjects. The discriminatory
                      accuracy, calculated by the age- and sex-adjusted area under
                      the receiver operating characteristics curve (AUC), was
                      highest for the LDpred-derived PRS (AUC = 0.654) including
                      nearly 1.2 M genetic variants (the proportion of causal
                      genetic variants for CRC assumed to be 0.003), whereas the
                      PRS of the 140 known variants identified from GWASs had the
                      lowest AUC (AUC = 0.629). Based on the LDpred-derived PRS,
                      we are able to identify $30\%$ of individuals without a
                      family history as having risk for CRC similar to those with
                      a family history of CRC, whereas the PRS based on known GWAS
                      variants identified only top $10\%$ as having a similar
                      relative risk. About $90\%$ of these individuals have no
                      family history and would have been considered average risk
                      under current screening guidelines, but might benefit from
                      earlier screening. The developed PRS offers a way for
                      risk-stratified CRC screening and other targeted
                      interventions.},
      cin          = {C070 / C020 / C120 / HD01},
      ddc          = {570},
      cid          = {I:(DE-He78)C070-20160331 / I:(DE-He78)C020-20160331 /
                      I:(DE-He78)C120-20160331 / I:(DE-He78)HD01-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:32758450},
      doi          = {10.1016/j.ajhg.2020.07.006},
      url          = {https://inrepo02.dkfz.de/record/157420},
}