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BACKGROUND: Polygenic risk scores (PRS) aggregate the contribution of many risk variants to provide a personalized genetic susceptibility profile. Since sample sizes of glioma genome-wide association studies (GWAS) remain modest, there is a need to find efficient ways of capturing genetic risk factors using available germline data.METHODS: We developed a novel PRS (PRS-CS) that uses continuous shrinkage priors to model the joint effects of over 1 million polymorphisms on disease risk and compared it to an approach (PRS-CT) that selects a limited set of independent variants that reach genome-wide significance (P<5×10). PRS models were trained using GWAS results stratified by histological (10,346 cases, 14,687 controls) and molecular subtype (2,632 cases, 2,445 controls), and validated in two independent cohorts.RESULTS: PRS-CS was consistently more predictive than PRS-CT across glioma subtypes with an average increase in explained variance (R) of 21%. Improvements were particularly pronounced for glioblastoma tumors, with PRS-CS yielding larger effect sizes (odds ratio (OR)=1.93, P=2.0×10 vs. OR=1.83, P=9.4×10) and higher explained variance (R=2.82% vs. R=2.56%). Individuals in the 95 percentile of the PRS-CS distribution had a 3-fold higher lifetime absolute risk of mutant (0.63%) and wildtype (0.76%) glioma relative to individuals with average PRS. PRS-CS also showed high classification accuracy for mutation status among cases (AUC=0.895).CONCLUSIONS: Our novel genome-wide PRS may improve the identification of high-risk individuals and help distinguish between prognostic glioma subtypes, increasing the potential clinical utility of germline genetics in glioma patient management.