Polygenic Risk Modelling for Prediction of Epithelial Ovarian Cancer Risk
Authors: Dareng Eileen O. , Tyrer Jonathan P. , Barnes Daniel R. , Jones Michelle R. , Yang Xin , Aben Katja K.H. , Adank Muriel A. , Agata Simona , Andrulis Irene L. , Anton-Culver Hoda , Antonenkova Natalia N. , Aravantinos Gerasimos , Arun Banu K. , Augustinsson Annelie , Balmaña Judith , Bandera Elisa V. , Barkardottir Rosa B. , Barrowdale Daniel , Beckmann Matthias W. , Beeghly-Fadiel Alicia , Benitez Javier , Bermisheva Marina , Bernardini Marcus Q. , Bjorge Line , Black Amanda , Bogdanova Natalia V. , Bonanni Bernardo , Borg Ake , Brenton James D. , Budzilowska Agnieszka , Butzow Ralf , Buys Saundra S. , Cai Hui , Caligo Maria A. , Campbell Ian , Cannioto Rikki , Cassingham Hayley , Chang-Claude Jenny , Chanock Stephen J. , Chen Kexin , Chiew Yoke-Eng , Chung Wendy K. , Claes Kathleen B.M. , Colanna Sarah ,   GEMO Study Collaborators,   GC-HBOC study Collaborators,   EMBRACE Collaborators, Cook Linda S. , Couch Fergus J. , Daly Mary B. , Dao Fanny , Davies Eleanor , de la Hoya Miguel , Putter Robin de , Dennis Joe , DePersia Allison , Devilee Peter , Diez Orland , Ding Yuan Chun , Doherty Jennifer A. , Domchek Susan M. , Dörk Thilo , Bois Andreas du , Dürst Matthias , Eccles Diana M. , Eliassen Heather A. , Engel Christoph , Evans D. Gareth , Fasching Peter A. , Flanagan James M. , Foretova Lenka , Fortner Renée T. , Friedman Eitan , Ganz Patricia A. , Garber Judy , Gensini Francesca , Giles Graham G. , Glendon Gord , Godwin Andrew K. , Goodman Marc T. , Greene Mark H. , Gronwald Jacek ,   OPAL Study Group,   AOCS Group, Hahnen Eric , Haiman Christopher A. , Håkansson Niclas , Hamann Ute , Hansen Thomas V.O. , Harris Holly R. , Hartman Mikael , Heitz Florian , Hildebrandt Michelle A.T. , Høgdall Estrid , Høgdall Claus K. , Hopper John L. , Huang Ruea-Yea , Huff Chad , Hulick Peter J. , Huntsman David G. , Imyanitov Evgeny N. ,   KConFab Investigators,   HEBON Investigators, Isaacs Claudine , Jakubowska Anna , James Paul A. , Janavicius Ramunas , Jensen Allan , Johannsson Oskar Th. , John Esther M. , Jones Michael E. , Kang Daehee , Karlan Beth Y. , Karnezis Anthony , Kelemen Linda E. , Khusnutdinova Elza , Kiemeney Lambertus A. , Kim Byoung-Gie , Kjaer Susanne K. , Komenaka Ian , Kupryjanczyk Jolanta , Kurian Allison W. , Kwong Ava , Lambrechts Diether , Larson Melissa C. , Lazaro Conxi , Le Nhu D. , Leslie Goska , Lester Jenny , Lesueur Fabienne , Levine Douglas A. , Li Lian , Li Jingmei , Loud Jennifer T. , Lu Karen H. , Lubiński Jan , Machackova Eva , Mai Phuong L. , Manoukian Siranoush , Marks Jeffrey R. , Matsuno Rayna Kim , Matsuo Keitaro , May Taymaa , McGuffog Lesley , McLaughlin John R. , McNeish Iain A. , Mebirouk Noura , Menon Usha , Miller Austin , Milne Roger L. , Minlikeeva Albina , Modugno Francesmary , Montagna Marco , Moysich Kirsten B. , Munro Elizabeth , Nathanson Katherine L. , Neuhausen Susan L. , Nevanlinna Heli , Yie Joanne Ngeow Yuen , Nielsen Henriette Roed , Nielsen Finn C. , Nikitina-Zake Liene , Odunsi Kunle , Offit Kenneth , Olah Edith , Olbrecht Siel , Olopade Olufunmilayo I. , Olson Sara H. , Olsson Håkan , Osorio Ana , Papi Laura , Park Sue K. , Parsons Michael T. , Pathak Harsha , Pedersen Inge Sokilde , Peixoto Ana , Pejovic Tanja , Perez-Segura Pedro , Permuth Jennifer B. , Peshkin Beth , Peterlongo Paolo , Piskorz Anna , Prokofyeva Darya , Radice Paolo , Rantala Johanna , Riggan Marjorie J. , Risch Harvey A. , Rodriguez-Antona Cristina , Ross Eric , Rossing Mary Anne , Runnebaum Ingo , Sandler Dale P. , Santamariña Marta , Soucy Penny , Schmutzler Rita K. , Setiawan V. Wendy , Shan Kang , Sieh Weiva , Simard Jacques , Singer Christian F. , Sokolenko Anna P , Song Honglin , Southey Melissa C. , Steed Helen , Stoppa-Lyonnet Dominique , Sutphen Rebecca , Swerdlow Anthony J. , Tan Yen Yen , Teixeira Manuel R. , Teo Soo Hwang , Terry Kathryn L. , Terry Mary Beth , Thomassen Mads , Thompson Pamela J. , Vestrheim Thomsen Liv Cecilie , Thull Darcy L. , Tischkowitz Marc , Titus Linda , Toland Amanda E. , Torres Diana , Trabert Britton , Travis Ruth , Tung Nadine , Tworoger Shelley S. , Valen Ellen , van Altena Anne M. , van der Hout Annemieke H. , Van Nieuwenhuysen Els , van Rensburg Elizabeth J. , Vega Ana , Edwards Digna Velez , Vierkant Robert A. , Wang Frances , Wappenschmidt Barbara , Webb Penelope M. , Weinberg Clarice R. , Weitzel Jeffrey N. , Wentzensen Nicolas , White Emily , Whittemore Alice S. , Winham Stacey J. , Wolk Alicja , Woo Yin-Ling , Wu Anna H. , Yan Li , Yannoukakos Drakoulis , Zavaglia Katia M. , Zheng Wei , Ziogas Argyrios , Zorn Kristin K. , Easton Douglas , Lawrenson Kate , DeFazio Anna , Sellers Thomas A. , Ramus Susan J. , Pearce Celeste L. , Monteiro Alvaro N. , Cunningham Julie , Goode Ellen L. , Schildkraut Joellen M. , Berchuck Andrew , Chenevix-Trench Georgia , Gayther Simon A. , Antoniou Antonis C. , Pharoah Paul D.P. 
Published: December 2020

AbstractPolygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally-efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestry; 7,669 women of East Asian ancestry; 1,072 women of African ancestry, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestry. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38(95%CI:1.28–1.48,AUC:0.588) per unit standard deviation, in women of European ancestry; 1.14(95%CI:1.08–1.19,AUC:0.538) in women of East Asian ancestry; 1.38(95%CI:1.21-1.58,AUC:0.593) in women of African ancestry; hazard ratios of 1.37(95%CI:1.30–1.44,AUC:0.592) in BRCA1 pathogenic variant carriers and 1.51(95%CI:1.36-1.67,AUC:0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.