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. 2020 Sep 3;107(3):432-444.
doi: 10.1016/j.ajhg.2020.07.006. Epub 2020 Aug 5.

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

Minta Thomas  1 Lori C Sakoda  2 Michael Hoffmeister  3 Elisabeth A Rosenthal  4 Jeffrey K Lee  5 Franzel J B van Duijnhoven  6 Elizabeth A Platz  7 Anna H Wu  8 Christopher H Dampier  9 Albert de la Chapelle  10 Alicja Wolk  11 Amit D Joshi  12 Andrea Burnett-Hartman  13 Andrea Gsur  14 Annika Lindblom  15 Antoni Castells  16 Aung Ko Win  17 Bahram Namjou  18 Bethany Van Guelpen  19 Catherine M Tangen  20 Qianchuan He  1 Christopher I Li  1 Clemens Schafmayer  21 Corinne E Joshu  7 Cornelia M Ulrich  22 D Timothy Bishop  23 Daniel D Buchanan  24 Daniel Schaid  25 David A Drew  26 David C Muller  27 David Duggan  28 David R Crosslin  29 Demetrius Albanes  30 Edward L Giovannucci  31 Eric Larson  32 Flora Qu  1 Frank Mentch  33 Graham G Giles  34 Hakon Hakonarson  33 Heather Hampel  35 Ian B Stanaway  4 Jane C Figueiredo  36 Jeroen R Huyghe  1 Jessica Minnier  37 Jenny Chang-Claude  38 Jochen Hampe  39 John B Harley  18 Kala Visvanathan  7 Keith R Curtis  1 Kenneth Offit  40 Li Li  41 Loic Le Marchand  42 Ludmila Vodickova  43 Marc J Gunter  44 Mark A Jenkins  17 Martha L Slattery  45 Mathieu Lemire  46 Michael O Woods  47 Mingyang Song  48 Neil Murphy  44 Noralane M Lindor  49 Ozan Dikilitas  50 Paul D P Pharoah  51 Peter T Campbell  52 Polly A Newcomb  53 Roger L Milne  34 Robert J MacInnis  54 Sergi Castellví-Bel  16 Shuji Ogino  55 Sonja I Berndt  30 Stéphane Bézieau  56 Stephen N Thibodeau  57 Steven J Gallinger  58 Syed H Zaidi  59 Tabitha A Harrison  1 Temitope O Keku  60 Thomas J Hudson  59 Veronika Vymetalkova  43 Victor Moreno  61 Vicente Martín  62 Volker Arndt  3 Wei-Qi Wei  63 Wendy Chung  64 Yu-Ru Su  1 Richard B Hayes  65 Emily White  66 Pavel Vodicka  43 Graham Casey  67 Stephen B Gruber  68 Robert E Schoen  69 Andrew T Chan  70 John D Potter  71 Hermann Brenner  72 Gail P Jarvik  73 Douglas A Corley  5 Ulrike Peters  74 Li Hsu  75
Affiliations

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

Minta Thomas et al. Am J Hum Genet. .

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.

Keywords: cancer risk prediction; colorectal cancer; machine learning; polygenic risk score.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Description of Three Approaches to Derive Polygenic Risk Scores (PRS) for Colorectal Cancer
Figure 2
Figure 2
Disease Probabilities for Developing CRC and Advanced Adenoma Probabilities of developing CRC (left) and advanced neoplasia (right) by age for PRS in the top 5% and bottom 5%, based on models derived from three approaches: known GWAS variants (Approach 1), SNP selection + machine learning with ridge regression (Approach 2), and LDpred with ρ = 0.003 (Approach 3). Average is the overall age-specific CRC (left) and advanced neoplasia (right) probabilities for the GERA.
Figure 3
Figure 3
Disease Probabilities and Proportion of Cases (95% CI) Subjects Stratified by the Deciles of LDpred-Derived PRS

Comment in

  • Age dependency of the polygenic risk score for colorectal cancer.
    Li S, Hopper JL. Li S, et al. Am J Hum Genet. 2021 Mar 4;108(3):525-526. doi: 10.1016/j.ajhg.2021.02.002. Am J Hum Genet. 2021. PMID: 33667395 Free PMC article. No abstract available.
  • Response to Li and Hopper.
    Thomas M, Sakoda LC, Hoffmeister M, Rosenthal EA, Lee JK, van Duijnhoven FJB, Platz EA, Wu AH, Dampier CH, de la Chapelle A, Wolk A, Joshi AD, Burnett-Hartman A, Gsur A, Lindblom A, Castells A, Win AK, Namjou B, Van Guelpen B, Tangen CM, He Q, Li CI, Schafmayer C, Joshu CE, Ulrich CM, Bishop DT, Buchanan DD, Schaid D, Drew DA, Muller DC, Duggan D, Crosslin DR, Albanes D, Giovannucci EL, Larson E, Qu F, Mentch F, Giles GG, Hakonarson H, Hampel H, Stanaway IB, Figueiredo JC, Huyghe JR, Minnier J, Chang-Claude J, Hampe J, Harley JB, Visvanathan K, Curtis KR, Offit K, Li L, Le Marchand L, Vodickova L, Gunter MJ, Jenkins MA, Slattery ML, Lemire M, Woods MO, Song M, Murphy N, Lindor NM, Dikilitas O, Pharoah PDP, Campbell PT, Newcomb PA, Milne RL, MacInnis RJ, Castellví-Bel S, Ogino S, Berndt SI, Bézieau S, Thibodeau SN, Gallinger SJ, Zaidi SH, Harrison TA, Keku TO, Hudson TJ, Vymetalkova V, Moreno V, Martín V, Arndt V, Wei WQ, Chung W, Su YR, Hayes RB, White E, Vodicka P, Casey G, Gruber SB, Schoen RE, Chan AT, Potter JD, Brenner H, Jarvik GP, Corley DA, Peters U, Hsu L. Thomas M, et al. Am J Hum Genet. 2021 Mar 4;108(3):527-529. doi: 10.1016/j.ajhg.2021.02.003. Am J Hum Genet. 2021. PMID: 33667396 Free PMC article. No abstract available.

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