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

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

Results

PRS-CS was consistently more predictive than PRS-CT across glioma subtypes with an average increase in explained variance (R

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.

IMPORTANCE OF THE STUDY

Inherited 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