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Polygenic risk modeling for prediction of epithelial ovarian cancer risk

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dc.contributor Landspitali - The National University Hospital of Iceland
dc.contributor.author GEMO Study Collaborators
dc.date.accessioned 2022-04-01T01:02:51Z
dc.date.available 2022-04-01T01:02:51Z
dc.date.issued 2022-03
dc.identifier.citation GEMO Study Collaborators 2022 , ' Polygenic risk modeling for prediction of epithelial ovarian cancer risk ' , European Journal of Human Genetics , vol. 30 , no. 3 , pp. 349-362 . https://doi.org/10.1038/s41431-021-00987-7
dc.identifier.issn 1018-4813
dc.identifier.other PURE: 46611186
dc.identifier.other PURE UUID: a314f592-9643-4616-bb87-48cf4d43a04d
dc.identifier.other PubMedCentral: PMC8904525
dc.identifier.uri https://hdl.handle.net/20.500.11815/3006
dc.description © 2021. The Author(s).
dc.description.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 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.
dc.format.extent 14
dc.format.extent 349-362
dc.language.iso en
dc.relation.ispartofseries European Journal of Human Genetics; 30(3)
dc.rights info:eu-repo/semantics/openAccess
dc.subject Náttúrufræðingar
dc.title Polygenic risk modeling for prediction of epithelial ovarian cancer risk
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article
dc.description.version Peer reviewed
dc.identifier.pmid 35027648
dc.identifier.doi https://doi.org/10.1038/s41431-021-00987-7
dc.contributor.department Clinical Laboratory Services, Diagnostics and Blood Bank


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