scholarly journals Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Author(s):  
Eileen O. Dareng ◽  
Jonathan P. Tyrer ◽  
Daniel R. Barnes ◽  
Michelle R. Jones ◽  
Xin Yang ◽  
...  

AbstractPolygenic 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.

2020 ◽  
Author(s):  
Eileen O. Dareng ◽  
Jonathan P. Tyrer ◽  
Daniel R. Barnes ◽  
Michelle R. Jones ◽  
Xin Yang ◽  
...  

AbstractPolygenic 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 ancestry; 7,669 women of East Asian ancestry; 1,072 women of African ancestry, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestry. 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 ancestry; 1.14(95%CI:1.08–1.19,AUC:0.538) in women of East Asian ancestry; 1.38(95%CI:1.21-1.58,AUC:0.593) in women of African ancestry; hazard ratios of 1.37(95%CI:1.30–1.44,AUC:0.592) in BRCA1 pathogenic variant carriers and 1.51(95%CI:1.36-1.67,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.


2020 ◽  
Author(s):  
Faiza Gaba ◽  
Oleg Blyuss ◽  
Saskia Sanderson ◽  
Antonis Antoniou ◽  
Jatinderpal Kalsi ◽  
...  

PLoS Medicine ◽  
2019 ◽  
Vol 16 (8) ◽  
pp. e1002893 ◽  
Author(s):  
James Yarmolinsky ◽  
Caroline L. Relton ◽  
Artitaya Lophatananon ◽  
Kenneth Muir ◽  
Usha Menon ◽  
...  

2016 ◽  
Vol 140 (2) ◽  
pp. 277-284 ◽  
Author(s):  
Linda S. Cook ◽  
Andy C.Y. Leung ◽  
Kenneth Swenerton ◽  
Richard P. Gallagher ◽  
Anthony Magliocco ◽  
...  

2014 ◽  
Vol 4 ◽  
Author(s):  
Jennifer Prescott ◽  
Kimberly A. Bertrand ◽  
Brett M. Reid ◽  
Jennifer Permuth-Wey ◽  
Immaculata De Vivo ◽  
...  

2010 ◽  
Author(s):  
Yani Lu ◽  
Jane Sullivan-Halley ◽  
Ellen T. Chang ◽  
Katherine D. Henderson ◽  
James Lacey ◽  
...  

2019 ◽  
Vol 28 (5) ◽  
pp. 987-995 ◽  
Author(s):  
Lisa Leung ◽  
Anne Grundy ◽  
Jack Siemiatycki ◽  
Jocelyne Arseneau ◽  
Lucy Gilbert ◽  
...  

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