Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk

Minta Thomas, Lori C. Sakoda, Michael Hoffmeister, Elisabeth A. Rosenthal, Jeffrey K. Lee, Franzel J.B. van Duijnhoven, Elizabeth A. Platz, Anna H. Wu, Christopher H. Dampier, Albert de la Chapelle, Alicja Wolk, Amit D. Joshi, Andrea Burnett-Hartman, Andrea Gsur, Annika Lindblom, Antoni Castells, Aung Ko Win, Bahram Namjou, Bethany Van Guelpen, Catherine M. TangenQianchuan He, Christopher I. Li, Clemens Schafmayer, Corinne E. Joshu, Cornelia M. Ulrich, D. Timothy Bishop, Daniel D. Buchanan, Daniel Schaid, David A. Drew, David C. Muller, David Duggan, David R. Crosslin, Demetrius Albanes, Edward L. Giovannucci, Eric Larson, Flora Qu, Frank Mentch, Graham G. Giles, Hakon Hakonarson, Heather Hampel, Ian B. Stanaway, Jane C. Figueiredo, Jeroen R. Huyghe, Jessica Minnier, Jenny Chang-Claude, Jochen Hampe, John B. Harley, Kala Visvanathan, Keith R. Curtis, Kenneth Offit, Li Li, Loic Le Marchand, Ludmila Vodickova, Marc J. Gunter, Mark A. Jenkins, Martha L. Slattery, Mathieu Lemire, Michael O. Woods, Mingyang Song, Neil Murphy, Noralane M. Lindor, Ozan Dikilitas, Paul D.P. Pharoah, Peter T. Campbell, Polly A. Newcomb, Roger L. Milne, Robert J. MacInnis, Sergi Castellví-Bel, Shuji Ogino, Sonja I. Berndt, Stéphane Bézieau, Stephen N. Thibodeau, Steven J. Gallinger, Syed H. Zaidi, Tabitha A. Harrison, Temitope O. Keku, Thomas J. Hudson, Veronika Vymetalkova, Victor Moreno, Vicente Martín, Volker Arndt, Wei Qi Wei, Wendy Chung, Yu Ru Su, Richard B. Hayes, Emily White, Pavel Vodicka, Graham Casey, Stephen B. Gruber, Robert E. Schoen, Andrew T. Chan, John D. Potter, Hermann Brenner, Gail P. Jarvik, Douglas A. Corley, Ulrike Peters, Li Hsu

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)432-444
Number of pages13
JournalAmerican journal of human genetics
Volume107
Issue number3
DOIs
StatePublished - Sep 3 2020

Keywords

  • cancer risk prediction
  • colorectal cancer
  • machine learning
  • polygenic risk score

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

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