Polygenic risk modeling for prediction of epithelial ovarian cancer risk
Dareng, Eileen O. ; Augustinsson, Annelie LU ; Olsson, Håkan LU
and Pharoah, Paul D.
(2022)
In European Journal of Human Genetics
30(3).
p.349-362
- 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... (More)
- 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. © 2021, The Author(s). (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1651edc4-fecd-41a9-99e9-80ecc50189f3
- author
- Dareng, Eileen O.
; Augustinsson, Annelie
LU
; Olsson, Håkan
LU
and Pharoah, Paul D.
- author collaboration
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Bayes Theorem, Breast Neoplasms, Carcinoma, Ovarian Epithelial, Female, Genetic Predisposition to Disease, Humans, Male, Ovarian Neoplasms, Polymorphism, Single Nucleotide, Prospective Studies, Risk Factors, Bayes theorem, breast tumor, female, genetic predisposition, genetics, human, male, ovary tumor, prospective study, risk factor, single nucleotide polymorphism
- in
- European Journal of Human Genetics
- volume
- 30
- issue
- 3
- pages
- 14 pages
- publisher
- Nature Publishing Group
- external identifiers
-
- scopus:85126235376
- pmid:35027648
- ISSN
- 1018-4813
- DOI
- 10.1038/s41431-021-00987-7
- language
- English
- LU publication?
- yes
- id
- 1651edc4-fecd-41a9-99e9-80ecc50189f3
- date added to LUP
- 2022-09-12 12:10:58
- date last changed
- 2022-09-13 03:00:08
@article{1651edc4-fecd-41a9-99e9-80ecc50189f3,
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. © 2021, The Author(s).}},
author = {{Dareng, Eileen O. and Augustinsson, Annelie and Olsson, Håkan and Pharoah, Paul D.}},
issn = {{1018-4813}},
keywords = {{Bayes Theorem; Breast Neoplasms; Carcinoma, Ovarian Epithelial; Female; Genetic Predisposition to Disease; Humans; Male; Ovarian Neoplasms; Polymorphism, Single Nucleotide; Prospective Studies; Risk Factors; Bayes theorem; breast tumor; female; genetic predisposition; genetics; human; male; ovary tumor; prospective study; risk factor; single nucleotide polymorphism}},
language = {{eng}},
number = {{3}},
pages = {{349--362}},
publisher = {{Nature Publishing Group}},
series = {{European Journal of Human Genetics}},
title = {{Polygenic risk modeling for prediction of epithelial ovarian cancer risk}},
url = {{http://dx.doi.org/10.1038/s41431-021-00987-7}},
doi = {{10.1038/s41431-021-00987-7}},
volume = {{30}},
year = {{2022}},
}