%0 Journal Article
%A Thomas, Minta
%A Sakoda, Lori C
%A Hoffmeister, Michael
%A Rosenthal, Elisabeth A
%A Lee, Jeffrey K
%A van Duijnhoven, Franzel J B
%A Platz, Elizabeth A
%A Wu, Anna H
%A Dampier, Christopher H
%A de la Chapelle, Albert
%A Wolk, Alicja
%A Joshi, Amit D
%A Burnett-Hartman, Andrea
%A Gsur, Andrea
%A Lindblom, Annika
%A Castells, Antoni
%A Win, Aung Ko
%A Namjou, Bahram
%A Van Guelpen, Bethany
%A Tangen, Catherine M
%A He, Qianchuan
%A Li, Christopher I
%A Schafmayer, Clemens
%A Joshu, Corinne E
%A Ulrich, Cornelia M
%A Bishop, D Timothy
%A Buchanan, Daniel D
%A Schaid, Daniel
%A Drew, David A
%A Muller, David C
%A Duggan, David
%A Crosslin, David R
%A Albanes, Demetrius
%A Giovannucci, Edward L
%A Larson, Eric
%A Qu, Flora
%A Mentch, Frank
%A Giles, Graham G
%A Hakonarson, Hakon
%A Hampel, Heather
%A Stanaway, Ian B
%A Figueiredo, Jane C
%A Huyghe, Jeroen R
%A Minnier, Jessica
%A Chang-Claude, Jenny
%A Hampe, Jochen
%A Harley, John B
%A Visvanathan, Kala
%A Curtis, Keith R
%A Offit, Kenneth
%A Li, Li
%A Le Marchand, Loic
%A Vodickova, Ludmila
%A Gunter, Marc J
%A Jenkins, Mark A
%A Slattery, Martha L
%A Lemire, Mathieu
%A Woods, Michael O
%A Song, Mingyang
%A Murphy, Neil
%A Lindor, Noralane M
%A Dikilitas, Ozan
%A Pharoah, Paul D P
%A Campbell, Peter T
%A Newcomb, Polly A
%A Milne, Roger L
%A MacInnis, Robert J
%A Castellví-Bel, Sergi
%A Ogino, Shuji
%A Berndt, Sonja I
%A Bézieau, Stéphane
%A Thibodeau, Stephen N
%A Gallinger, Steven J
%A Zaidi, Syed H
%A Harrison, Tabitha A
%A Keku, Temitope O
%A Hudson, Thomas J
%A Vymetalkova, Veronika
%A Moreno, Victor
%A Martín, Vicente
%A Arndt, Volker
%A Wei, Wei-Qi
%A Chung, Wendy
%A Su, Yu-Ru
%A Hayes, Richard B
%A White, Emily
%A Vodicka, Pavel
%A Casey, Graham
%A Gruber, Stephen B
%A Schoen, Robert E
%A Chan, Andrew T
%A Potter, John D
%A Brenner, Hermann
%A Jarvik, Gail P
%A Corley, Douglas A
%A Peters, Ulrike
%A Hsu, Li
%T Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk.
%J The American journal of human genetics
%V 107
%N 3
%@ 0002-9297
%C New York, NY
%I Elsevier
%M DKFZ-2020-01615
%P 432-444
%D 2020
%Z 2020 Sep 3;107(3):432-444
%X 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
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:32758450
%R 10.1016/j.ajhg.2020.07.006
%U https://inrepo02.dkfz.de/record/157420