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

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

In: AM J HUM GENET, Vol. 107, No. 3, 03.09.2020, p. 432-444.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

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, W-Q, Chung, W, Su, Y-R, 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 2020, 'Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk', AM J HUM GENET, vol. 107, no. 3, pp. 432-444. https://doi.org/10.1016/j.ajhg.2020.07.006

APA

Thomas, M., Sakoda, L. C., Hoffmeister, M., Rosenthal, E. A., Lee, J. K., van Duijnhoven, F. J. B., Platz, E. A., Wu, A. H., Dampier, C. H., de la Chapelle, A., Wolk, A., Joshi, A. D., Burnett-Hartman, A., Gsur, A., Lindblom, A., Castells, A., Win, A. K., Namjou, B., Van Guelpen, B., ... Hsu, L. (2020). Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk. AM J HUM GENET, 107(3), 432-444. https://doi.org/10.1016/j.ajhg.2020.07.006

Vancouver

Thomas M, Sakoda LC, Hoffmeister M, Rosenthal EA, Lee JK, van Duijnhoven FJB et al. Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk. AM J HUM GENET. 2020 Sep 3;107(3):432-444. https://doi.org/10.1016/j.ajhg.2020.07.006

Bibtex

@article{85bba2033bec4bb79117ab62610427a9,
title = "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.",
author = "Minta Thomas and Sakoda, {Lori C} and Michael Hoffmeister and Rosenthal, {Elisabeth A} and Lee, {Jeffrey K} and {van Duijnhoven}, {Franzel J B} and Platz, {Elizabeth A} and Wu, {Anna H} and Dampier, {Christopher H} and {de la Chapelle}, Albert and Alicja Wolk and Joshi, {Amit D} and Andrea Burnett-Hartman and Andrea Gsur and Annika Lindblom and Antoni Castells and Win, {Aung Ko} and Bahram Namjou and {Van Guelpen}, Bethany and Tangen, {Catherine M} and Qianchuan He and Li, {Christopher I} and Clemens Schafmayer and Joshu, {Corinne E} and Ulrich, {Cornelia M} and Bishop, {D Timothy} and Buchanan, {Daniel D} and Daniel Schaid and Drew, {David A} and Muller, {David C} and David Duggan and Crosslin, {David R} and Demetrius Albanes and Giovannucci, {Edward L} and Eric Larson and Flora Qu and Frank Mentch and Giles, {Graham G} and Hakon Hakonarson and Heather Hampel and Stanaway, {Ian B} and Figueiredo, {Jane C} and Huyghe, {Jeroen R} and Jessica Minnier and Jenny Chang-Claude and Jochen Hampe and Harley, {John B} and Kala Visvanathan and Curtis, {Keith R} and Kenneth Offit and Li Li and {Le Marchand}, Loic and Ludmila Vodickova and Gunter, {Marc J} and Jenkins, {Mark A} and Slattery, {Martha L} and Mathieu Lemire and Woods, {Michael O} and Mingyang Song and Neil Murphy and Lindor, {Noralane M} and Ozan Dikilitas and Pharoah, {Paul D P} and Campbell, {Peter T} and Newcomb, {Polly A} and Milne, {Roger L} and MacInnis, {Robert J} and Sergi Castellv{\'i}-Bel and Shuji Ogino and Berndt, {Sonja I} and St{\'e}phane B{\'e}zieau and Thibodeau, {Stephen N} and Gallinger, {Steven J} and Zaidi, {Syed H} and Harrison, {Tabitha A} and Keku, {Temitope O} and Hudson, {Thomas J} and Veronika Vymetalkova and Victor Moreno and Vicente Mart{\'i}n and Volker Arndt and Wei-Qi Wei and Wendy Chung and Yu-Ru Su and Hayes, {Richard B} and Emily White and Pavel Vodicka and Graham Casey and Gruber, {Stephen B} and Schoen, {Robert E} and Chan, {Andrew T} and Potter, {John D} and Hermann Brenner and Jarvik, {Gail P} and Corley, {Douglas A} and Ulrike Peters and Li Hsu",
note = "Copyright {\textcopyright} 2020 American Society of Human Genetics. All rights reserved.",
year = "2020",
month = sep,
day = "3",
doi = "10.1016/j.ajhg.2020.07.006",
language = "English",
volume = "107",
pages = "432--444",
journal = "AM J HUM GENET",
issn = "0002-9297",
publisher = "Cell Press",
number = "3",

}

RIS

TY - JOUR

T1 - Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk

AU - Thomas, Minta

AU - Sakoda, Lori C

AU - Hoffmeister, Michael

AU - Rosenthal, Elisabeth A

AU - Lee, Jeffrey K

AU - van Duijnhoven, Franzel J B

AU - Platz, Elizabeth A

AU - Wu, Anna H

AU - Dampier, Christopher H

AU - de la Chapelle, Albert

AU - Wolk, Alicja

AU - Joshi, Amit D

AU - Burnett-Hartman, Andrea

AU - Gsur, Andrea

AU - Lindblom, Annika

AU - Castells, Antoni

AU - Win, Aung Ko

AU - Namjou, Bahram

AU - Van Guelpen, Bethany

AU - Tangen, Catherine M

AU - He, Qianchuan

AU - Li, Christopher I

AU - Schafmayer, Clemens

AU - Joshu, Corinne E

AU - Ulrich, Cornelia M

AU - Bishop, D Timothy

AU - Buchanan, Daniel D

AU - Schaid, Daniel

AU - Drew, David A

AU - Muller, David C

AU - Duggan, David

AU - Crosslin, David R

AU - Albanes, Demetrius

AU - Giovannucci, Edward L

AU - Larson, Eric

AU - Qu, Flora

AU - Mentch, Frank

AU - Giles, Graham G

AU - Hakonarson, Hakon

AU - Hampel, Heather

AU - Stanaway, Ian B

AU - Figueiredo, Jane C

AU - Huyghe, Jeroen R

AU - Minnier, Jessica

AU - Chang-Claude, Jenny

AU - Hampe, Jochen

AU - Harley, John B

AU - Visvanathan, Kala

AU - Curtis, Keith R

AU - Offit, Kenneth

AU - Li, Li

AU - Le Marchand, Loic

AU - Vodickova, Ludmila

AU - Gunter, Marc J

AU - Jenkins, Mark A

AU - Slattery, Martha L

AU - Lemire, Mathieu

AU - Woods, Michael O

AU - Song, Mingyang

AU - Murphy, Neil

AU - Lindor, Noralane M

AU - Dikilitas, Ozan

AU - Pharoah, Paul D P

AU - Campbell, Peter T

AU - Newcomb, Polly A

AU - Milne, Roger L

AU - MacInnis, Robert J

AU - Castellví-Bel, Sergi

AU - Ogino, Shuji

AU - Berndt, Sonja I

AU - Bézieau, Stéphane

AU - Thibodeau, Stephen N

AU - Gallinger, Steven J

AU - Zaidi, Syed H

AU - Harrison, Tabitha A

AU - Keku, Temitope O

AU - Hudson, Thomas J

AU - Vymetalkova, Veronika

AU - Moreno, Victor

AU - Martín, Vicente

AU - Arndt, Volker

AU - Wei, Wei-Qi

AU - Chung, Wendy

AU - Su, Yu-Ru

AU - Hayes, Richard B

AU - White, Emily

AU - Vodicka, Pavel

AU - Casey, Graham

AU - Gruber, Stephen B

AU - Schoen, Robert E

AU - Chan, Andrew T

AU - Potter, John D

AU - Brenner, Hermann

AU - Jarvik, Gail P

AU - Corley, Douglas A

AU - Peters, Ulrike

AU - Hsu, Li

N1 - Copyright © 2020 American Society of Human Genetics. All rights reserved.

PY - 2020/9/3

Y1 - 2020/9/3

N2 - 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.

AB - 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.

U2 - 10.1016/j.ajhg.2020.07.006

DO - 10.1016/j.ajhg.2020.07.006

M3 - SCORING: Journal article

C2 - 32758450

VL - 107

SP - 432

EP - 444

JO - AM J HUM GENET

JF - AM J HUM GENET

SN - 0002-9297

IS - 3

ER -