Genome-wide Polygenic Risk Scores Predict Risk of Glioma and Molecular Subtypes

BackgroundPolygenic 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. MethodsWe 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<5x10-8). 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. ResultsPRS-CS was consistently more predictive than PRS-CT across glioma subtypes with an average increase in explained variance (R2) of 21%. Improvements were particularly pronounced for glioblastoma tumors, with PRS-CS yielding larger effect sizes (odds ratio (OR)=1.93, P=2.0x10-54 vs. OR=1.83, P=9.4x10-50) and higher explained variance (R2=2.82% vs. R2=2.56%). Individuals in the 95th percentile of the PRS-CS distribution had a 3-fold higher lifetime absolute risk of IDH mutant (0.63%) and IDH wildtype (0.76%) glioma relative to individuals with average PRS. PRS-CS also showed high classification accuracy for IDH mutation status among cases (AUC=0.895). ConclusionsOur 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. IMPORTANCE OF THE STUDYInherited genetic variation is one of only a few risk factors known to contribute to gliomagenesis. We leverage the largest available collection of genome-wide association studies for glioma to show that a genome-wide PRS approach that models the joint effect of correlated variants across the genome yields improved prediction of glioma risk. Our novel PRS also improves the classification of cases according to IDH mutation status. Additionally, we provide refined estimates of individual genetic susceptibility and show that risk scores in the highest percentiles of the PRS distribution confer significant increases in relative and lifetime absolute risk. Taken together, our findings provide further evidence of the potential for germline genotyping to be used as a clinical biomarker in the assessment of personalized glioma risk and the non-invasive management of patients with newly diagnosed brain tumors.