| Literature DB >> 32758450 |
Minta Thomas1, Lori C Sakoda2, Michael Hoffmeister3, Elisabeth A Rosenthal4, Jeffrey K Lee5, Franzel J B van Duijnhoven6, Elizabeth A Platz7, Anna H Wu8, Christopher H Dampier9, Albert de la Chapelle10, Alicja Wolk11, Amit D Joshi12, Andrea Burnett-Hartman13, Andrea Gsur14, Annika Lindblom15, Antoni Castells16, Aung Ko Win17, Bahram Namjou18, Bethany Van Guelpen19, Catherine M Tangen20, Qianchuan He1, Christopher I Li1, Clemens Schafmayer21, Corinne E Joshu7, Cornelia M Ulrich22, D Timothy Bishop23, Daniel D Buchanan24, Daniel Schaid25, David A Drew26, David C Muller27, David Duggan28, David R Crosslin29, Demetrius Albanes30, Edward L Giovannucci31, Eric Larson32, Flora Qu1, Frank Mentch33, Graham G Giles34, Hakon Hakonarson33, Heather Hampel35, Ian B Stanaway4, Jane C Figueiredo36, Jeroen R Huyghe1, Jessica Minnier37, Jenny Chang-Claude38, Jochen Hampe39, John B Harley18, Kala Visvanathan7, Keith R Curtis1, Kenneth Offit40, Li Li41, Loic Le Marchand42, Ludmila Vodickova43, Marc J Gunter44, Mark A Jenkins17, Martha L Slattery45, Mathieu Lemire46, Michael O Woods47, Mingyang Song48, Neil Murphy44, Noralane M Lindor49, Ozan Dikilitas50, Paul D P Pharoah51, Peter T Campbell52, Polly A Newcomb53, Roger L Milne34, Robert J MacInnis54, Sergi Castellví-Bel16, Shuji Ogino55, Sonja I Berndt30, Stéphane Bézieau56, Stephen N Thibodeau57, Steven J Gallinger58, Syed H Zaidi59, Tabitha A Harrison1, Temitope O Keku60, Thomas J Hudson59, Veronika Vymetalkova43, Victor Moreno61, Vicente Martín62, Volker Arndt3, Wei-Qi Wei63, Wendy Chung64, Yu-Ru Su1, Richard B Hayes65, Emily White66, Pavel Vodicka43, Graham Casey67, Stephen B Gruber68, Robert E Schoen69, Andrew T Chan70, John D Potter71, Hermann Brenner72, Gail P Jarvik73, Douglas A Corley5, Ulrike Peters74, Li Hsu75.
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.Entities:
Keywords: cancer risk prediction; colorectal cancer; machine learning; polygenic risk score
Mesh：
Year: 2020 PMID： 32758450 PMCID： PMC7477007 DOI： 10.1016/j.ajhg.2020.07.006
Source DB: PubMed Journal: Am J Hum Genet ISSN： 0002-9297 Impact factor: 11.025