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 Cance...
Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk s...
Funder: Funding details are provided in the Supplementary MaterialAbstractPolygenic risk scores (PRS...
Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have ...
Q1Q1Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve ri...
Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk s...
Funder: Funding details are provided in the Supplementary MaterialAbstractPolygenic risk scores (PRS...
Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have ...
Q1Q1Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve ri...
Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk s...
Funder: Funding details are provided in the Supplementary MaterialAbstractPolygenic risk scores (PRS...
Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have ...