Dataset Information

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

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.

SUBMITTER: Thomas M

PROVIDER: S-EPMC7477007 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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

Thomas Minta M Sakoda Lori C LC Hoffmeister Michael M Rosenthal Elisabeth A EA Lee Jeffrey K JK van Duijnhoven Franzel J B FJB Platz Elizabeth A EA Wu Anna H AH Dampier Christopher H CH de la Chapelle Albert A Wolk Alicja A Joshi Amit D AD Burnett-Hartman Andrea A Gsur Andrea A Lindblom Annika A Castells Antoni A Win Aung Ko AK Namjou Bahram B Van Guelpen Bethany B Tangen Catherine M CM He Qianchuan Q Li Christopher I CI Schafmayer Clemens C Joshu Corinne E CE Ulrich Cornelia M CM Bishop D Timothy DT Buchanan Daniel D DD Schaid Daniel D Drew David A DA Muller David C DC Duggan David D Crosslin David R DR Albanes Demetrius D Giovannucci Edward L EL Larson Eric E Qu Flora F Mentch Frank F Giles Graham G GG Hakonarson Hakon H Hampel Heather H Stanaway Ian B IB Figueiredo Jane C JC Huyghe Jeroen R JR Minnier Jessica J Chang-Claude Jenny J Hampe Jochen J Harley John B JB Visvanathan Kala K Curtis Keith R KR Offit Kenneth K Li Li L Le Marchand Loic L Vodickova Ludmila L Gunter Marc J MJ Jenkins Mark A MA Slattery Martha L ML Lemire Mathieu M Woods Michael O MO Song Mingyang M Murphy Neil N Lindor Noralane M NM Dikilitas Ozan O Pharoah Paul D P PDP Campbell Peter T PT Newcomb Polly A PA Milne Roger L RL MacInnis Robert J RJ Castellví-Bel Sergi S Ogino Shuji S Berndt Sonja I SI Bézieau Stéphane S Thibodeau Stephen N SN Gallinger Steven J SJ Zaidi Syed H SH Harrison Tabitha A TA Keku Temitope O TO Hudson Thomas J TJ Vymetalkova Veronika V Moreno Victor V Martín Vicente V Arndt Volker V Wei Wei-Qi WQ Chung Wendy W Su Yu-Ru YR Hayes Richard B RB White Emily E Vodicka Pavel P Casey Graham G Gruber Stephen B SB Schoen Robert E RE Chan Andrew T AT Potter John D JD Brenner Hermann H Jarvik Gail P GP Corley Douglas A DA Peters Ulrike U Hsu Li L

American journal of human genetics 20200805 3

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 ge ...[more]

PMID: 32758450

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Dataset Information

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

Publications

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

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