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Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Polygenic risk modeling for prediction of epithelial ovarian cancer risk


Title: Polygenic risk modeling for prediction of epithelial ovarian cancer risk
Author: GEMO Study Collaborators
Barkardóttir, Rósa Björk
Date: 2022-01-14
Language: English
Scope: 14
University/Institute: Landspitali - The National University Hospital of Iceland
Department: Clinical Laboratory Services, Diagnostics and Blood Bank
Series: European Journal of Human Genetics; 30(3)
ISSN: 1018-4813
DOI: https://doi.org/10.1038/s41431-021-00987-7
Subject: Náttúrufræðingar; Genetics (clinical); Genetics
URI: https://hdl.handle.net/20.500.11815/3006

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Citation:

GEMO Study Collaborators & Barkardóttir , R B 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

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.

Description:

Funding Information: ADF has received a research grant from AstraZeneca, not directly related to the content of this manuscript. MWB conducts research funded by Amgen, Novartis and Pfizer. PAF conducts research funded by Amgen, Novartis and Pfizer. He received Honoraria from Roche, Novartis and Pfizer. AWK reports research funding to her institution from Myriad Genetics for an unrelated project. UM owns stocks in Abcodia Ltd. Rachel A. Murphy is a consultant for Pharmavite. The other authors declare no conflicts of interest. Publisher Copyright: © 2021, The Author(s).

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