Genome-wide polygenic risk scores predict risk of glioma and molecular subtypes

• DOI: 10.1093/neuonc/noae112
• Publication Date: 2024-06-25T10:02:10Z
• Authors: Taishi Nakase; Geno A Guerra; Quinn T Ostrom; Tian Ge; Beatrice S Melin; Margaret Wrensch; John K Wiencke; Robert B Jenkins; Jeanette E Eckel-...; ; Melissa L Bondy; Stephen S Francis; Linda Kachuri
• Keywords: Genome-wide Association Study; Polygenic risk score; Genetic Association

Abstract Background Polygenic risk scores (PRS) aggregate the contribution of many variants to provide a personalized genetic susceptibility profile. Since sample sizes glioma genome-wide association studies (GWAS) remain modest, there is need efficiently capture using available data. Methods We applied method based on continuous shrinkage priors (PRS-CS) model joint effects over 1 million common disease and compared this an approach (PRS-CT) that only selects limited set independent reach significance (P < 5 × 10–8). PRS models were trained GWAS stratified by histological (10 346 cases 14 687 controls) molecular subtype (2632 2445 controls), validated in 2 cohorts. Results PRS-CS was generally more predictive than PRS-CT with median increase explained variance (R2) 24% (interquartile range = 11–30%) across subtypes. Improvements pronounced for glioblastoma (GBM), yielding larger odds ratios (OR) per standard deviation (SD) (OR 1.93, P 2.0 10–54 vs. OR 1.83, 9.4 10–50) higher (R2 2.82% R2 2.56%). Individuals 80th percentile distribution had significantly GBM (0.107%) at age 60 those average (0.046%, 2.4 10–12). Lifetime absolute reached 1.18% 0.76% IDH wildtype tumors individuals 95th percentile. augmented classification mutation status when added demographic factors (AUC 0.839 AUC 0.895, PΔAUC 6.8 10–9). Conclusions Genome-wide has potential enhance detection high-risk help distinguish between prognostic