TY - JOUR

T1 - Polygenic risk modeling for prediction of epithelial ovarian cancer risk

AU - Dareng, Eileen O.

AU - Tyrer, Jonathan P.

AU - Barnes, Daniel R.

AU - Jones, Michelle R.

AU - Yang, Xin

AU - Aben, Katja K.H.

AU - Adank, Muriel A.

AU - Agata, Simona

AU - Andrulis, Irene L.

AU - Anton-Culver, Hoda

AU - Antonenkova, Natalia N.

AU - Aravantinos, Gerasimos

AU - Arun, Banu K.

AU - Augustinsson, Annelie

AU - Balmaña, Judith

AU - Bandera, Elisa V.

AU - Barkardottir, Rosa B.

AU - Barrowdale, Daniel

AU - Beckmann, Matthias W.

AU - Beeghly-Fadiel, Alicia

AU - Benitez, Javier

AU - Bermisheva, Marina

AU - Bernardini, Marcus Q.

AU - Bjorge, Line

AU - Black, Amanda

AU - Bogdanova, Natalia V.

AU - Bonanni, Bernardo

AU - Borg, Ake

AU - Brenton, James D.

AU - Budzilowska, Agnieszka

AU - Butzow, Ralf

AU - Buys, Saundra S.

AU - Cai, Hui

AU - Caligo, Maria A.

AU - Campbell, Ian

AU - Cannioto, Rikki

AU - Cassingham, Hayley

AU - Chang-Claude, Jenny

AU - Chanock, Stephen J.

AU - Chen, Kexin

AU - Chiew, Yoke Eng

AU - Chung, Wendy K.

AU - Claes, Kathleen B.M.

AU - Colonna, Sarah

AU - Hansen, Thomas V.O.

AU - Høgdall, Estrid

AU - Høgdall, Claus K.

AU - Jensen, Allan

AU - Kjaer, Susanne K.

AU - Nielsen, Finn C.

AU - GEMO Study Collaborators

AU - GC-HBOC Study Collaborators

AU - EMBRACE Collaborators

AU - OPAL Study Group

AU - AOCS Group

AU - kConFab Investigators

AU - HEBON Investigators

AU - OCAC Consortium

AU - CIMBA Consortium

N1 - Publisher Copyright: © 2021. The Author(s).

PY - 2022

Y1 - 2022

N2 - 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.

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.

U2 - 10.1038/s41431-021-00987-7

DO - 10.1038/s41431-021-00987-7

M3 - Journal article

C2 - 35027648

AN - SCOPUS:85126235376

VL - 30

SP - 349

EP - 362

JO - European Journal of Human Genetics

JF - European Journal of Human Genetics

SN - 1018-4813

IS - 3

ER -