Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes

TY - JOUR

T1 - Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes

AU - Mavaddat, Nasim

AU - Michailidou, Kyriaki

AU - Dennis, Joe

AU - Lush, Michael

AU - Fachal, Laura

AU - Lee, Andrew

AU - Tyrer, Jonathan P

AU - Chen, Ting-Huei

AU - Wang, Qin

AU - Bolla, Manjeet K

AU - Yang, Xin

AU - Adank, Muriel A

AU - Ahearn, Thomas

AU - Aittomäki, Kristiina

AU - Allen, Jamie

AU - Andrulis, Irene L

AU - Anton-Culver, Hoda

AU - Antonenkova, Natalia N

AU - Arndt, Volker

AU - Aronson, Kristan J

AU - Auer, Paul L

AU - Auvinen, Päivi

AU - Barrdahl, Myrto

AU - Beane Freeman, Laura E

AU - Beckmann, Matthias W

AU - Behrens, Sabine

AU - Benitez, Javier

AU - Bermisheva, Marina

AU - Bernstein, Leslie

AU - Blomqvist, Carl

AU - Bogdanova, Natalia V

AU - Bojesen, Stig E

AU - Bonanni, Bernardo

AU - Børresen-Dale, Anne-Lise

AU - Brauch, Hiltrud

AU - Bremer, Michael

AU - Brenner, Hermann

AU - Brentnall, Adam

AU - Brock, Ian W

AU - Brooks-Wilson, Angela

AU - Brucker, Sara Y

AU - Brüning, Thomas

AU - Burwinkel, Barbara

AU - Campa, Daniele

AU - Carter, Brian D

AU - Castelao, Jose E

AU - Chanock, Stephen J

AU - Chlebowski, Rowan

AU - Christiansen, Hans

AU - Clarke, Christine L

AU - Collée, J Margriet

AU - Cordina-Duverger, Emilie

AU - Cornelissen, Sten

AU - Couch, Fergus J

AU - Cox, Angela

AU - Cross, Simon S

AU - Czene, Kamila

AU - Daly, Mary B

AU - Devilee, Peter

AU - Dörk, Thilo

AU - Dos-Santos-Silva, Isabel

AU - Dumont, Martine

AU - Durcan, Lorraine

AU - Dwek, Miriam

AU - Eccles, Diana M

AU - Ekici, Arif B

AU - Eliassen, A Heather

AU - Ellberg, Carolina

AU - Engel, Christoph

AU - Eriksson, Mikael

AU - Evans, D Gareth

AU - Fasching, Peter A

AU - Figueroa, Jonine

AU - Fletcher, Olivia

AU - Flyger, Henrik

AU - Försti, Asta

AU - Fritschi, Lin

AU - Gabrielson, Marike

AU - Gago-Dominguez, Manuela

AU - Gapstur, Susan M

AU - García-Sáenz, José A

AU - Gaudet, Mia M

AU - Georgoulias, Vassilios

AU - Giles, Graham G

AU - Gilyazova, Irina R

AU - Glendon, Gord

AU - Goldberg, Mark S

AU - Goldgar, David E

AU - González-Neira, Anna

AU - Grenaker Alnæs, Grethe I

AU - Grip, Mervi

AU - Gronwald, Jacek

AU - Grundy, Anne

AU - Guénel, Pascal

AU - Haeberle, Lothar

AU - Hahnen, Eric

AU - Haiman, Christopher A

AU - Håkansson, Niclas

AU - Hamann, Ute

AU - Hankinson, Susan E

AU - Harkness, Elaine F

AU - Hart, Steven N

AU - He, Wei

AU - Hein, Alexander

AU - Heyworth, Jane

AU - Hillemanns, Peter

AU - Hollestelle, Antoinette

AU - Hooning, Maartje J

AU - Hoover, Robert N

AU - Hopper, John L

AU - Howell, Anthony

AU - Huang, Guanmengqian

AU - Humphreys, Keith

AU - Hunter, David J

AU - Jakimovska, Milena

AU - Jakubowska, Anna

AU - Janni, Wolfgang

AU - John, Esther M

AU - Johnson, Nichola

AU - Jones, Michael E

AU - Jukkola-Vuorinen, Arja

AU - Jung, Audrey

AU - Kaaks, Rudolf

AU - Kaczmarek, Katarzyna

AU - Kataja, Vesa

AU - Keeman, Renske

AU - Kerin, Michael J

AU - Khusnutdinova, Elza

AU - Kiiski, Johanna I

AU - Knight, Julia A

AU - Ko, Yon-Dschun

AU - Kosma, Veli-Matti

AU - Koutros, Stella

AU - Kristensen, Vessela N

AU - Krüger, Ute

AU - Kühl, Tabea

AU - Lambrechts, Diether

AU - Le Marchand, Loic

AU - Lee, Eunjung

AU - Lejbkowicz, Flavio

AU - Lilyquist, Jenna

AU - Lindblom, Annika

AU - Lindström, Sara

AU - Lissowska, Jolanta

AU - Lo, Wing-Yee

AU - Loibl, Sibylle

AU - Long, Jirong

AU - Lubiński, Jan

AU - Lux, Michael P

AU - MacInnis, Robert J

AU - Maishman, Tom

AU - Makalic, Enes

AU - Maleva Kostovska, Ivana

AU - Mannermaa, Arto

AU - Manoukian, Siranoush

AU - Margolin, Sara

AU - Martens, John W M

AU - Martinez, Maria Elena

AU - Mavroudis, Dimitrios

AU - McLean, Catriona

AU - Meindl, Alfons

AU - Menon, Usha

AU - Middha, Pooja

AU - Miller, Nicola

AU - Moreno, Fernando

AU - Mulligan, Anna Marie

AU - Mulot, Claire

AU - Muñoz-Garzon, Victor M

AU - Neuhausen, Susan L

AU - Nevanlinna, Heli

AU - Neven, Patrick

AU - Newman, William G

AU - Nielsen, Sune F

AU - Nordestgaard, Børge G

AU - Norman, Aaron

AU - Offit, Kenneth

AU - Olson, Janet E

AU - Olsson, Håkan

AU - Orr, Nick

AU - Pankratz, V Shane

AU - Park-Simon, Tjoung-Won

AU - Perez, Jose I A

AU - Pérez-Barrios, Clara

AU - Peterlongo, Paolo

AU - Peto, Julian

AU - Pinchev, Mila

AU - Plaseska-Karanfilska, Dijana

AU - Polley, Eric C

AU - Prentice, Ross

AU - Presneau, Nadege

AU - Prokofyeva, Darya

AU - Purrington, Kristen

AU - Pylkäs, Katri

AU - Rack, Brigitte

AU - Radice, Paolo

AU - Rau-Murthy, Rohini

AU - Rennert, Gad

AU - Rennert, Hedy S

AU - Rhenius, Valerie

AU - Robson, Mark

AU - Romero, Atocha

AU - Ruddy, Kathryn J

AU - Ruebner, Matthias

AU - Saloustros, Emmanouil

AU - Sandler, Dale P

AU - Sawyer, Elinor J

AU - Schmidt, Daniel F

AU - Schmutzler, Rita K

AU - Schneeweiss, Andreas

AU - Schoemaker, Minouk J

AU - Schumacher, Fredrick

AU - Schürmann, Peter

AU - Schwentner, Lukas

AU - Scott, Christopher

AU - Scott, Rodney J

AU - Seynaeve, Caroline

AU - Shah, Mitul

AU - Sherman, Mark E

AU - Shrubsole, Martha J

AU - Shu, Xiao-Ou

AU - Slager, Susan

AU - Smeets, Ann

AU - Sohn, Christof

AU - Soucy, Penny

AU - Southey, Melissa C

AU - Spinelli, John J

AU - Stegmaier, Christa

AU - Stone, Jennifer

AU - Swerdlow, Anthony J

AU - Tamimi, Rulla M

AU - Tapper, William J

AU - Taylor, Jack A

AU - Terry, Mary Beth

AU - Thöne, Kathrin

AU - Tollenaar, Rob A E M

AU - Tomlinson, Ian

AU - Truong, Thérèse

AU - Tzardi, Maria

AU - Ulmer, Hans-Ulrich

AU - Untch, Michael

AU - Vachon, Celine M

AU - van Veen, Elke M

AU - Vijai, Joseph

AU - Weinberg, Clarice R

AU - Wendt, Camilla

AU - Whittemore, Alice S

AU - Wildiers, Hans

AU - Willett, Walter

AU - Winqvist, Robert

AU - Wolk, Alicja

AU - Yang, Xiaohong R

AU - Yannoukakos, Drakoulis

AU - Zhang, Yan

AU - Zheng, Wei

AU - Ziogas, Argyrios

AU - Dunning, Alison M

AU - Thompson, Deborah J

AU - Chenevix-Trench, Georgia

AU - Chang-Claude, Jenny

AU - Schmidt, Marjanka K

AU - Hall, Per

AU - Milne, Roger L

AU - Pharoah, Paul D P

AU - Antoniou, Antonis C

AU - Chatterjee, Nilanjan

AU - Kraft, Peter

AU - García-Closas, Montserrat

AU - Simard, Jacques

AU - Easton, Douglas F

AU - ABCTB Investigators

N1 - Copyright © 2018 American Society of Human Genetics. All rights reserved.

PY - 2019/1/3

Y1 - 2019/1/3

N2 - Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.

AB - Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.

KW - Journal Article

U2 - 10.1016/j.ajhg.2018.11.002

DO - 10.1016/j.ajhg.2018.11.002

M3 - SCORING: Journal article

C2 - 30554720

VL - 104

SP - 21

EP - 34

JO - AM J HUM GENET

JF - AM J HUM GENET

SN - 0002-9297

IS - 1

ER -