ArticleNeuro-oncology2024
Genome-wide polygenic risk scores predict risk of glioma and molecular subtypes.
Article in Neuro-oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Single-cell multi-omic integration analysis prioritizes druggable genes and reveals cell-type-specific causal effects in glioblastomagenesis.Journal of translational medicine · 2026Article
- Fully automated Res3DNet model to predict IDH mutation of gliomas from whole-brain MRI free of tumor segmentation.iScience · 2026Article
- Bioinformatics and artificial intelligence in genomic data analysis: current advances and future directions.Molecular genetics and genomics : MGG · 2025Review
- The Prognostic Value of a Nomogram Model Based on Tumor Immune Markers and Clinical Factors for Adult Primary Glioma.Cancers · 2025Article
- Glioblastoma: A Multidisciplinary Approach to Its Pathophysiology, Treatment, and Innovative Therapeutic Strategies.Biomedicines · 2025Review
- OMT and tensor SVD-based deep learning model for segmentation and predicting genetic markers of glioma: A multicenter study.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Integration of MRI radiomics and germline genetics to predict the IDH mutation status of gliomas.NPJ precision oncology · 2025Article
- Screening of glioma susceptibility SNPs and construction of risk models based on machine learning algorithms.BMC neurology · 2025Article
- Improving neuroblastoma risk prediction through a polygenic risk score derived from genome-wide association study-identified loci.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Dissecting the biology of gliomagenesis: Evaluating the interaction betweenNeuro-oncology advancesArticle
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Abstract
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 efficiently capture genetic risk using available data.
methodsWe applied a method based on continuous shrinkage priors (PRS-CS) to model the joint effects of over 1 million common variants on disease risk and compared this to an approach (PRS-CT) that only selects a limited set of independent variants that reach genome-wide significance (P < 5 × 10-8). PRS models were trained using GWAS stratified by histological (10 346 cases and 14 687 controls) and molecular subtype (2632 cases and 2445 controls), and validated in 2 independent cohorts.
resultsPRS-CS was generally more predictive than PRS-CT with a median increase in explained variance (R2) of 24% (interquartile range = 11-30%) across glioma subtypes. Improvements were pronounced for glioblastoma (GBM), with PRS-CS yielding larger odds ratios (OR) per standard deviation (SD) (OR = 1.93, P = 2.0 × 10-54 vs. OR = 1.83, P = 9.4 × 10-50) and higher explained variance (R2 = 2.82% vs. R2 = 2.56%). Individuals in the 80th percentile of the PRS-CS distribution had a significantly higher risk of GBM (0.107%) at age 60 compared to those with average PRS (0.046%, P = 2.4 × 10-12). Lifetime absolute risk reached 1.18% for glioma and 0.76% for IDH wildtype tumors for individuals in the 95th PRS percentile. PRS-CS augmented the classification of IDH mutation status in cases when added to demographic factors (AUC = 0.839 vs. AUC = 0.895, PΔAUC = 6.8 × 10-9).
conclusionsGenome-wide PRS has the potential to enhance the detection of high-risk individuals and help distinguish between prognostic glioma subtypes.
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