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@article{Dareng:2022:10.1038/s41431-021-00987-7,
author = {Dareng, EO and Tyrer, JP and Barnes, DR and Jones, MR and Yang, X and Aben, KKH and Adank, MA and Agata, S and Andrulis, IL and Anton-Culver, H and Antonenkova, NN and Aravantinos, G and Arun, BK and Augustinsson, A and Balmana, J and Bandera, E and Barkardottir, RB and Barrowdale, D and Beckmann, MW and Beeghly-Fadiel, A and Benitez, J and Bermisheva, M and Bernardini, MQ and Bjorge, L and Black, A and Bogdanova, N and Bonanni, B and Borg, A and Brenton, JD and Budzilowska, A and Butzow, R and Buys, SS and Cai, H and Caligo, MA and Campbell, I and Cannioto, R and Cassingham, H and Chang-Claude, J and Chanock, SJ and Chen, K and Chiew, Y-E and Chung, WK and Claes, KBM and Colonna, S and Cook, LS and Couch, FJ and Daly, MB and Dao, F and Davies, E and de, la Hoya M and de, Putter R and Dennis, J and DePersia, A and Devilee, P and Diez, O and Ding, YC and Doherty, JA and Domchek, SM and Dork, T and du, Bois A and Durst, M and Eccles, DM and Eliassen, HA and Engel, C and Evans, GD and Fas},
doi = {10.1038/s41431-021-00987-7},
journal = {European Journal of Human Genetics},
pages = {349--362},
title = {Polygenic risk modeling for prediction of epithelial ovarian cancer risk},
url = {http://dx.doi.org/10.1038/s41431-021-00987-7},
volume = {30},
year = {2022}
}

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TY  - JOUR
AB  - 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.
AU  - Dareng,EO
AU  - Tyrer,JP
AU  - Barnes,DR
AU  - Jones,MR
AU  - Yang,X
AU  - Aben,KKH
AU  - Adank,MA
AU  - Agata,S
AU  - Andrulis,IL
AU  - Anton-Culver,H
AU  - Antonenkova,NN
AU  - Aravantinos,G
AU  - Arun,BK
AU  - Augustinsson,A
AU  - Balmana,J
AU  - Bandera,E
AU  - Barkardottir,RB
AU  - Barrowdale,D
AU  - Beckmann,MW
AU  - Beeghly-Fadiel,A
AU  - Benitez,J
AU  - Bermisheva,M
AU  - Bernardini,MQ
AU  - Bjorge,L
AU  - Black,A
AU  - Bogdanova,N
AU  - Bonanni,B
AU  - Borg,A
AU  - Brenton,JD
AU  - Budzilowska,A
AU  - Butzow,R
AU  - Buys,SS
AU  - Cai,H
AU  - Caligo,MA
AU  - Campbell,I
AU  - Cannioto,R
AU  - Cassingham,H
AU  - Chang-Claude,J
AU  - Chanock,SJ
AU  - Chen,K
AU  - Chiew,Y-E
AU  - Chung,WK
AU  - Claes,KBM
AU  - Colonna,S
AU  - Cook,LS
AU  - Couch,FJ
AU  - Daly,MB
AU  - Dao,F
AU  - Davies,E
AU  - de,la Hoya M
AU  - de,Putter R
AU  - Dennis,J
AU  - DePersia,A
AU  - Devilee,P
AU  - Diez,O
AU  - Ding,YC
AU  - Doherty,JA
AU  - Domchek,SM
AU  - Dork,T
AU  - du,Bois A
AU  - Durst,M
AU  - Eccles,DM
AU  - Eliassen,HA
AU  - Engel,C
AU  - Evans,GD
AU  - Fasching,PA
AU  - Flanagan,JM
AU  - Fortner,R
AU  - Machackova,E
AU  - Friedman,E
AU  - Ganz,PA
AU  - Garber,J
AU  - Gensini,F
AU  - Giles,GG
AU  - Glendon,G
AU  - Godwin,AK
AU  - Goodman,MT
AU  - Greene,MH
AU  - Gronwald,J
AU  - Group,OS
AU  - AOCSGroup
AU  - Hahnen,E
AU  - Haiman,CA
AU  - Hakansson,N
AU  - Hamann,U
AU  - Hansen,TVO
AU  - Harris,HR
AU  - Hartman,M
AU  - Heitz,F
AU  - Hildebrandt,MAT
AU  - Hogdall,E
AU  - Hogdall,CK
AU  - Hopper,JL
AU  - Huang,R-Y
AU  - Huff,C
AU  - Hulick,PJ
AU  - Huntsman,DG
AU  - Imyanitov,EN
AU  - Isaacs,C
AU  - Jakubowska,A
AU  - James,PA
AU  - Janavicius,R
AU  - Jensen,A
AU  - Johannsson,OT
AU  - John,EM
AU  - Jones,ME
AU  - Kang,D
AU  - Karlan,BY
AU  - Karnezis,A
AU  - Kelemen,LE
AU  - Khusnutdinova,E
AU  - Kiemeney,LA
AU  - Kim,B-G
AU  - Kjaer,SK
AU  - Komenaka,I
AU  - Kupryjanczyk,J
AU  - Kurian,AW
AU  - Kwong,A
AU  - Lambrechts,D
AU  - Larson,MC
AU  - Lazaro,C
AU  - Le,ND
AU  - Leslie,G
AU  - Lester,J
AU  - Lesueur,F
AU  - Levine,DA
AU  - Li,L
AU  - Li,J
AU  - Loud,JT
AU  - Lu,KH
AU  - Mai,PL
AU  - Manoukian,S
AU  - Marks,JR
AU  - KimMatsuno,R
AU  - Matsuo,K
AU  - May,T
AU  - McGuffog,L
AU  - McLaughlin,JR
AU  - McNeish,IA
AU  - Mebirouk,N
AU  - Menon,U
AU  - Miller,A
AU  - Milne,RL
AU  - Minlikeeva,A
AU  - Modugno,F
AU  - Montagna,M
AU  - Moysich,KB
AU  - Munro,E
AU  - Nathanson,KL
AU  - Neuhausen,SL
AU  - Nevanlinna,H
AU  - Yie,JNY
AU  - Nielsen,HR
AU  - Nielsen,FC
AU  - Nikitina-Zake,L
AU  - Odunsi,K
AU  - Offit,K
AU  - Olah,E
AU  - Olbrecht,S
AU  - Olopade,O
AU  - Olson,SH
AU  - Olsson,H
AU  - Osorio,A
AU  - Papi,L
AU  - Park,SK
AU  - Parsons,MT
AU  - Pathak,H
AU  - Pedersen,IS
AU  - Peixoto,A
AU  - Pejovic,T
AU  - Perez-Segura,P
AU  - Permuth,JB
AU  - Peshkin,B
AU  - Peterlongo,P
AU  - Piskorz,A
AU  - Prokofyeva,D
AU  - Radice,P
AU  - Rantala
DO  - 10.1038/s41431-021-00987-7
EP  - 362
PY  - 2022///
SN  - 1018-4813
SP  - 349
TI  - Polygenic risk modeling for prediction of epithelial ovarian cancer risk
T2  - European Journal of Human Genetics
UR  - http://dx.doi.org/10.1038/s41431-021-00987-7
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000742272300002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.nature.com/articles/s41431-021-00987-7
UR  - http://hdl.handle.net/10044/1/94866
VL  - 30
ER  -

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