Polygenic risk modeling for prediction of epithelial ovarian cancer risk.
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Date
2022-03-01ICR Author
Author
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, Y-E
Chung, WK
Claes, KBM
Colonna, S
GEMO Study Collaborators,
GC-HBOC Study Collaborators,
EMBRACE Collaborators,
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
OPAL Study Group,
AOCS Group,
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, R-Y
Huff, C
Hulick, PJ
Huntsman, DG
Imyanitov, EN
KConFab Investigators,
HEBON Investigators,
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, B-G
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
OCAC Consortium,
CIMBA Consortium,
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, Y-L
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
Type
Journal Article
Metadata
Show full item recordAbstract
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.
Collections
Subject
GEMO Study Collaborators
GC-HBOC Study Collaborators
EMBRACE Collaborators
OPAL Study Group
AOCS Group
KConFab Investigators
HEBON Investigators
OCAC Consortium
CIMBA Consortium
Research team
Aetiological Epidemiology
Language
eng
Date accepted
2021-09-27
Citation
European journal of human genetics : EJHG, 2022, 30 (3), pp. 349 - 362
Publisher
SPRINGERNATURE
Except where otherwise noted, this item's license is described
as
http://creativecommons.org/licenses/by/4.0/
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