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

Authors
Nasim Mavaddat,Kyriaki Michailidou
Joe Dennis,Michael Lush,Laura Fachal,Andrew Lee,Jonathan Tyrer,Ting‐Huei Chen,Qin Wang,Manjeet Bolla,Xin Yang,Muriel Adank,Thomas Ahearn,Conxi Lázaro,Jamie Allen,Irene Andrulis,Hoda Anton‐Culver,Natalia Antonenkova,Volker Arndt,Kristan Aronson,Paul Auer,Päivi Auvinen,Myrto Barrdahl,Laura Freeman,Matthias Beckmann,Sabine Behrens,Javier Benı́tez,Marina Bermisheva,Leslie Bernstein,Carl Blomqvist,Natalia Bogdanova,Stig Bojesen,Bernardo Bonanni,Anne‐Lise Børresen‐Dale,Hiltrud Brauch,Michael Bremer,Hermann Brenner,Adam Brentnall,Simon Cross,Angela Brooks‐Wilson,Sara Brucker,Thomas Brüning,Barbara Burwinkel,Daniele Campa,Brian Carter,Jose Castelao,Stephen Chanock,Rowan Chlebowski,Hans Christiansen,Christine Clarke,Nicoline Hoogerbrugge,F. Ménégaux,Sten Cornelissen,Fergus Couch,Angela Cox,Kamila Czene,Mary Terry,Peter Devilee,Thilo Dörk,Isabel dos‐Santos‐Silva,Martine Dumont,Lorraine Durcan,Miriam Dwek,Rosalind Eeles,Arif Ekici,A. Eliassen,Dimitrios Mavroudis,Christoph Engel,Mikael Eriksson,D. Evans,Peter Fasching,Jonine Figueroa,Olivia Fletcher,Daehee Kang,Asta Försti,Lin Fritschi,Marike Gabrielson,Manuela Gago-Domínguez,William Tapper,José García-Sáenz,Mia Gaudet,V. Georgoulias,Graham Giles,I. Gilyazova,Anna Mulligan,Mark Goldberg,David Bowtell,Anna González‐Neira,Grethe Alnæs,Mervi Grip,Jacek Gronwald,Anne Grundy,Pascal Guénel,Lothar Häberle,Eric Hahnen,Graham Mann,Torben Ørntoft,Ute Hamann,Susan Hankinson,Elaine Harkness,Steven Hart,Wei He,Alexander Hein,Jane Heyworth,Peter Hillemanns,Antoinette Hollestelle,Maartje Hooning,Robert Hoover,John Hopper,Anthony Howell,Guanmengqian Huang,Keith Humphreys,David Wyld,Camilla Wendt,Anna Jakubowska,Wolfgang Janni,Esther John,Nichola Johnson,Michael Jones,Arja Jukkola‐Vuorinen,Audrey Jung,Rudolf Kaaks,Katarzyna Białkowska,Vesa Kataja,Renske Keeman,Michael Kerin,Э. Хуснутдинова,Jonathan Beesley,Julia Knight,Yon‐Dschun Ko,Veli‐Matti Kosma,Stella Koutros,Vessela Kristensen,Ute Krüger,Tabea Kühl,Diether Lambrechts,Loı̈c Marchand,Eunjung Lee,Flavio Lejbkowicz,Jenna Lilyquist,Annika Lindblom,Jingmei Li,Jolanta Lissowska,Wing‐Yee Lo,Sibylle Loibl,Jirong Long,Jan Lubiński,Michael Lux,Robert MacInnis,Tom Maishman,Enes Makalic,Ivana Kostovska,Siranoush Manoukian,John Martens,Marı́a Martı́nez,Catriona McLean,Alfons Meindl,Usha Menon,Pooja Kapoor,Austin Miller,Fernando Moreno,Claire Mulot,Victor Muñoz-Garzon,Susan Neuhausen,Heli Nevanlinna,Patrick Neven,William Newman,Sune Nielsen,Børge Nordestgaard,Aaron Norman,Janet Olson,Håkan Olsson,Nick Orr,V. Pankratz,Tjoung‐Won Park‐Simon,José Pérez,Clara Pérez-Barrios,Paolo Peterlongo,Julian Peto,Barbara Pardini,Dijana Plaseska‐Karanfilska,Eric Polley,Ross Prentice,Nadège Presneau,Darya Prokofyeva,Kristen Purrington,Katri Pylkäs,Brigitte Rack,Paolo Radice,Rohini Rau‐Murthy,Gad Rennert,Valerie Rhenius,Mark Robson,Atocha Romero,Kathryn Ruddy,Matthias Ruebner,Emmanouil Saloustros,Dale Sandler,Elinor Sawyer,Daniel Schmidt,David O’Malley,Andreas Schneeweiß,Mary Rossing,Fredrick Schumacher,Peter Schürmann,Lukas Schwentner,Christopher Scott,Rodney Scott,Caroline Seynaeve,Mitul Shah,Mark Sherman,Martha Shrubsole,Xiao‐Ou Shu,Ann Smeets,Christof Sohn,Penny Soucy,Melissa Southey,John Spinelli,Christa Stegmaier,Jennifer Stone,Anthony Swerdlow,Rulla Tamimi,Jack Taylor,Kathrin Thöne,Rob Tollenaar,Ian Tomlinson,Thérèse Truong,Maria Tzardi,Hans-Ulrich Ulmer,Michael Untch,Celine Vachon,Elke Veen,Joseph Vijai,Carlo Vecchia,Alice Whittemore,Hans Wildiers,Walter Willett,Robert Winqvist,Alicja Wolk,Xiaohong Yang,Drakoulis Yannoukakos,Yan Zhang,Wei Zheng,Argyrios Ziogas,Alison Dunning,Deborah Thompson,Georgia Chenevix‐Trench,Jenny Chang‐Claude,Marjanka Schmidt,Per Hall,Roger Milne,Paul Pharoah,Antonis Antoniou,Nilanjan Chatterjee,Peter Kraft,Montserrat García‐Closas,Jacques Simard,Douglas Easton,Arto Mannermaa,C. Clarke,Florence Ménégaux
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,Isabel Silva
Published
Dec 13, 2018
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Abstract

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. 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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