TY - JOUR
T1 - Polygenic risk modeling for prediction of epithelial ovarian cancer risk
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 - The OCAC Consortium
AU - The CIMBA Consortium
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 - Cook, Linda S.
AU - Couch, Fergus J.
AU - Daly, Mary B.
AU - Dao, Fanny
AU - Davies, Eleanor
AU - Ross, Eric
N1 - Publisher Copyright:
© 2021, The Author(s).
PY - 2022/2
Y1 - 2022/2
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.
KW - Bayes Theorem
KW - Breast Neoplasms
KW - Carcinoma, Ovarian Epithelial/genetics
KW - Female
KW - Genetic Predisposition to Disease
KW - Humans
KW - Male
KW - Ovarian Neoplasms/epidemiology
KW - Polymorphism, Single Nucleotide
KW - Prospective Studies
KW - Risk Factors
UR - http://www.scopus.com/inward/record.url?scp=85126235376&partnerID=8YFLogxK
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=purepublist2023&SrcAuth=WosAPI&KeyUT=WOS:000742272300002&DestLinkType=FullRecord&DestApp=WOS
U2 - 10.1038/s41431-021-00987-7
DO - 10.1038/s41431-021-00987-7
M3 - Article
C2 - 35027648
SN - 1018-4813
VL - 30
SP - 349
EP - 362
JO - European Journal of Human Genetics
JF - European Journal of Human Genetics
IS - 3
ER -