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Dareng EO, Tyrer JP, Barnes DR, Jones MR, Yang X, Aben KKH, Adank MA, Agata S, Andrulis IL, Anton-Culver H, Antonenkova NN, Aravantinos G, Arun BK, Augustinsson A, Balmaña J, Bandera EV, Barkardottir RB, Barrowdale D, Beckmann MW, Beeghly-Fadiel A, Benitez J, Bermisheva M, Bernardini MQ, Bjorge L, Black A, Bogdanova NV, Bonanni B, Borg A, Brenton JD, Budzilowska A, Butzow R, Buys SS, Cai H, Caligo MA, Campbell I, Cannioto R, Cassingham H, Chang-Claude J, Chanock SJ, Chen K, Chiew YE, Chung WK, Claes KBM, Colonna S, Cook LS, Couch FJ, Daly MB, Dao F, Davies E, De la Hoya M, De Putter R, Dennis J, DePersia A, Devilee P, Diez O, Ding YC, Doherty JA, Domchek SM, Dörk T, Du Bois A, Dürst M, Eccles DM, Eliassen HA, Engel C, Evans GD, Fasching PA, Flanagan JM, Fortner RT, Machackova E, Friedman E, Ganz PA, Garber J, Gensini F, Giles GG, Glendon G, Godwin AK, Goodman MT, Greene MH, Gronwald J, Hahnen E, Haiman CA, Håkansson N, Hamann U, Hansen TVO, Harris HR, Hartman M, Heitz F, Hildebrandt MAT, Høgdall E, Høgdall CK, Hopper JL, Huang RY, Huff C, Hulick PJ, Huntsman DG, Imyanitov EN, Isaacs C, Jakubowska A, James PA, Janavicius R, Jensen A, Johannsson OT, John EM, Jones ME, Kang D, Karlan BY, Karnezis A, Kelemen LE, Khusnutdinova E, Kiemeney LA, Kim BG, Kjaer SK, Komenaka I, Kupryjanczyk J, Kurian AW, Kwong A, Lambrechts D, Larson MC, Lazaro C, Le ND, Leslie G, Lester J, Lesueur F, Levine DA, Li L, Li J, Loud JT, Lu KH, Lubiński J, Mai PL, Manoukian S, Marks JR, Matsuno RK, Matsuo K, May T, McGuffog L, McLaughlin JR, McNeish IA, Mebirouk N, Menon U, Miller A, Milne RL, Minlikeeva A, Modugno F, Montagna M, Moysich KB, Munro E, Nathanson KL, Neuhausen SL, Nevanlinna H, Yie JNY, Nielsen HR, Nielsen FC, Nikitina-Zake L, Odunsi K, Offit K, Olah E, Olbrecht S, Olopade OI, Olson SH, Olsson H, Osorio A, Papi L, Park SK, Parsons MT, Pathak H, Pedersen IS, Peixoto A, Pejovic T, Perez-Segura P, Permuth JB, Peshkin B, Peterlongo P, Piskorz A, Prokofyeva D, Radice P, Rantala J, Riggan MJ, Risch HA, Rodriguez-Antona C, Ross E, Rossing MA, Runnebaum I, Sandler DP, Santamariña M, Soucy P, Schmutzler RK, Setiawan VW, Shan K, Sieh W, Simard J, Singer CF, Sokolenko AP, Song H, Southey MC, Steed H, Stoppa-Lyonnet D, Sutphen R, Swerdlow AJ, Tan YY, Teixeira MR, Teo SH, Terry KL, Terry MB, Thomassen M, Thompson PJ, Thomsen LCV, Thull DL, Tischkowitz M, Titus L, Toland AE, Torres D, Trabert B, Travis R, Tung N, Tworoger SS, Valen E, Van Altena AM, Van der Hout AH, Van Nieuwenhuysen E, Van Rensburg EJ, Vega A, Edwards DV, Vierkant RA, Wang F, Wappenschmidt B, Webb PM, Weinberg CR, Weitzel JN, Wentzensen N, White E, Whittemore AS, Winham SJ, Wolk A, Woo YL, Wu AH, Yan L, Yannoukakos D, Zavaglia KM, Zheng W, Ziogas A, Zorn KK, Kleibl Z, Easton D, Lawrenson K, DeFazio A, Sellers TA, Ramus SJ, Pearce CL, Monteiro AN, Cunningham J, Goode EL, Schildkraut JM, Berchuck A, Chenevix-Trench G, Gayther SA, Antoniou AC, Pharoah PDP. Polygenic risk modeling for prediction of epithelial ovarian cancer risk. European journal of human genetics : EJHG. 2022 Dec;30(30). 349-362.
Abstract
Polygenic 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 ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. 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 ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, 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.