Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes.
;
Martine Dumont;
Lorraine Durcan;
Miriam Dwek;
Diana M Eccles;
Arif B Ekici;
A Heather Eliassen;
Carolina Ellberg;
Christoph Engel;
Mikael Eriksson;
D Gareth Evans;
Peter A Fasching;
Jonine Figueroa;
Olivia Fletcher;
Henrik Flyger;
Asta Försti;
Lin Fritschi;
Marike Gabrielson;
Manuela Gago-Dominguez;
Susan M Gapstur;
José A García-Sáenz;
Mia M Gaudet;
Vassilios Georgoulias;
Graham G Giles;
Irina R Gilyazova;
Gord Glendon;
Mark S Goldberg;
David E Goldgar;
Anna González-Neira;
Grethe I Grenaker Alnæs;
Mervi Grip;
Jacek Gronwald;
Anne Grundy;
Pascal Guénel;
Lothar Haeberle;
Eric Hahnen;
Christopher A Haiman;
Niclas Håkansson;
Ute Hamann;
Susan E Hankinson;
Elaine F Harkness;
Steven N Hart;
Wei He;
Alexander Hein;
Jane Heyworth;
Peter Hillemanns;
Antoinette Hollestelle;
Maartje J Hooning;
Robert N Hoover;
John L Hopper;
Anthony Howell;
Guanmengqian Huang;
Keith Humphreys;
David J Hunter;
Milena Jakimovska;
Anna Jakubowska;
Wolfgang Janni;
Esther M John;
Nichola Johnson;
Michael E Jones;
Arja Jukkola-Vuorinen;
Audrey Jung;
Rudolf Kaaks;
Katarzyna Kaczmarek;
Vesa Kataja;
Renske Keeman;
Michael J Kerin;
Elza Khusnutdinova;
Johanna I Kiiski;
Julia A Knight;
Yon-Dschun Ko;
Veli-Matti Kosma;
Stella Koutros;
Vessela N Kristensen;
Ute Krüger;
Tabea Kühl;
Diether Lambrechts;
Loic Le Marchand;
Eunjung Lee;
Flavio Lejbkowicz;
Jenna Lilyquist;
Annika Lindblom;
Sara Lindström;
Jolanta Lissowska;
Wing-Yee Lo;
Sibylle Loibl;
Jirong Long;
Jan Lubiński;
Michael P Lux;
Robert J MacInnis;
Tom Maishman;
Enes Makalic;
Ivana Maleva Kostovska;
Arto Mannermaa;
Siranoush Manoukian;
Sara Margolin;
John WM Martens;
Maria Elena Martinez;
Dimitrios Mavroudis;
Catriona McLean;
Alfons Meindl;
Usha Menon;
Pooja Middha;
Nicola Miller;
Fernando Moreno;
Anna Marie Mulligan;
Claire Mulot;
Victor M Muñoz-Garzon;
Susan L Neuhausen;
Heli Nevanlinna;
Patrick Neven;
William G Newman;
Sune F Nielsen;
Børge G Nordestgaard;
Aaron Norman;
Kenneth Offit;
Janet E Olson;
Håkan Olsson;
Nick Orr;
V Shane Pankratz;
Tjoung-Won Park-Simon;
Jose IA Perez;
Clara Pérez-Barrios;
Paolo Peterlongo;
Julian Peto ;
Mila Pinchev;
Dijana Plaseska-Karanfilska;
Eric C Polley;
Ross Prentice;
Nadege Presneau;
Darya Prokofyeva;
Kristen Purrington;
Katri Pylkäs;
Brigitte Rack;
Paolo Radice;
Rohini Rau-Murthy;
Gad Rennert;
Hedy S Rennert;
Valerie Rhenius;
Mark Robson;
Atocha Romero;
Kathryn J Ruddy;
Matthias Ruebner;
Emmanouil Saloustros;
Dale P Sandler;
Elinor J Sawyer;
Daniel F Schmidt;
Rita K Schmutzler;
Andreas Schneeweiss;
Minouk J Schoemaker;
Fredrick Schumacher;
Peter Schürmann;
Lukas Schwentner;
Christopher Scott;
Rodney J Scott;
Caroline Seynaeve;
Mitul Shah;
Mark E Sherman;
Martha J Shrubsole;
Xiao-Ou Shu;
Susan Slager;
Ann Smeets;
Christof Sohn;
Penny Soucy;
Melissa C Southey;
John J Spinelli;
Christa Stegmaier;
Jennifer Stone;
Anthony J Swerdlow;
Rulla M Tamimi;
William J Tapper;
Jack A Taylor;
Mary Beth Terry;
Kathrin Thöne;
Rob AEM Tollenaar;
Ian Tomlinson;
Thérèse Truong;
Maria Tzardi;
Hans-Ulrich Ulmer;
Michael Untch;
Celine M Vachon;
Elke M van Veen;
Joseph Vijai;
Clarice R Weinberg;
Camilla Wendt;
Alice S Whittemore;
Hans Wildiers;
Walter Willett;
Robert Winqvist;
Alicja Wolk;
Xiaohong R Yang;
Drakoulis Yannoukakos;
Yan Zhang;
Wei Zheng;
Argyrios Ziogas;
ABCTB Investigators;
kConFab/AOCS Investigators;
NBCS Collaborators;
Alison M Dunning;
Deborah J Thompson;
Georgia Chenevix-Trench;
Jenny Chang-Claude;
Marjanka K Schmidt;
Per Hall;
Roger L Milne;
Paul DP Pharoah;
Antonis C Antoniou;
Nilanjan Chatterjee;
Peter Kraft;
Montserrat García-Closas;
Jacques Simard;
Douglas F Easton;
(2018)
Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes.
AMERICAN JOURNAL OF HUMAN GENETICS, 104 (1).
pp. 21-34.
ISSN 0002-9297
DOI: 10.1016/j.ajhg.2018.11.002
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.
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