Polygenic risk modeling for prediction of epithelial ovarian cancer risk.

EO, Dareng; JP, Tyrer; DR, Barnes; MR, Jones; X, Yang; KKH, Aben; MA, Adank; S, Agata; IL, Andrulis; H, Anton-Culver; NN, Antonenkova; G, Aravantinos; BK, Arun; A, Augustinsson; J, Balmaña; EV, Bandera; RB, Barkardottir; D, Barrowdale; MW, Beckmann; A, Beeghly-Fadiel; J, Benitez; M, Bermisheva; MQ, Bernardini; L, Bjorge; A, Black; NV, Bogdanova; B, Bonanni; A, Borg; JD, Brenton; A, Budzilowska; R, Butzow; SS, Buys; H, Cai; MA, Caligo; I, Campbell; R, Cannioto; H, Cassingham; J, Chang-Claude; SJ, Chanock; K, Chen; YE, Chiew; WK, Chung; KBM, Claes; S, Colonna; LS, Cook; FJ, Couch; MB, Daly; F, Dao; E, Davies; la Hoya M, de; Putter R, de; J, Dennis; A, DePersia; P, Devilee; O, Diez; YC, Ding; JA, Doherty; SM, Domchek; T, Dörk; Bois A, du; M, Dürst; DM, Eccles; HA, Eliassen; C, Engel; GD, Evans; PA, Fasching; JM, Flanagan; RT, Fortner; E, Machackova; E, Friedman; PA, Ganz; J, Garber; F, Gensini; GG, Giles; G, Glendon; AK, Godwin; MT, Goodman; MH, Greene; J, Gronwald; E, Hahnen; CA, Haiman; N, Håkansson; U, Hamann; TVO, Hansen; HR, Harris; M, Hartman; F, Heitz; MAT, Hildebrandt; E, Høgdall; CK, Høgdall; JL, Hopper; RY, Huang; C, Huff; PJ, Hulick; DG, Huntsman; EN, Imyanitov; C, Isaacs; A, Jakubowska; PA, James; R, Janavicius; A, Jensen; OT, Johannsson; EM, John; ME, Jones; D, Kang; BY, Karlan; A, Karnezis; LE, Kelemen; E, Khusnutdinova; LA, Kiemeney; BG, Kim; SK, Kjaer; I, Komenaka; J, Kupryjanczyk; AW, Kurian; A, Kwong; D, Lambrechts; MC, Larson; C, Lazaro; ND, Le; G, Leslie; J, Lester; F, Lesueur; DA, Levine; L, Li; J, Li; JT, Loud; KH, Lu; J, Lubiński; PL, Mai; S, Manoukian; JR, Marks; RK, Matsuno; K, Matsuo; T, May; L, McGuffog; JR, McLaughlin; IA, McNeish; N, Mebirouk; U, Menon; A, Miller; RL, Milne; A, Minlikeeva; F, Modugno; M, Montagna; KB, Moysich; E, Munro; KL, Nathanson; SL, Neuhausen; H, Nevanlinna; JNY, Yie; HR, Nielsen; FC, Nielsen; L, Nikitina-Zake; K, Odunsi; K, Offit; E, Olah; S, Olbrecht; OI, Olopade; SH, Olson; H, Olsson; A, Osorio; L, Papi; SK, Park; MT, Parsons; H, Pathak; IS, Pedersen; A, Peixoto; T, Pejovic; P, Perez-Segura; JB, Permuth; B, Peshkin; P, Peterlongo; A, Piskorz; D, Prokofyeva; P, Radice; J, Rantala; MJ, Riggan; HA, Risch; C, Rodriguez-Antona; E, Ross; MA, Rossing; I, Runnebaum; DP, Sandler; M, Santamariña; P, Soucy; RK, Schmutzler; VW, Setiawan; K, Shan; W, Sieh; J, Simard; CF, Singer; AP, Sokolenko

doi:10.1038/s41431-021-00987-7

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Polygenic risk modeling for prediction of epithelial ovarian cancer risk.

Shared by NobleBlocks on Jan 14, 2022 • 12:00 AM UTC

Authors:

Dareng EO

Tyrer JP

Barnes DR

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 comput...

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