ArticleHuman genetics2025
Integrating transcriptomic and polygenic risk scores to enhance predictive accuracy for ischemic stroke subtypes.
Article in Human genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Unraveling the genetic interplay and therapeutic potentials between major depressive disorder and metabolic syndrome: multi-ancestry and multi-trait genome-wide association analyses.BMC psychiatry · 2026Article
- Advances in genetics and multi-omics for ischemic stroke: from pathogenesis to clinical translation.Frontiers in genetics · 2026Review
- Genetic Architecture of Ischemic Stroke: Insights from Genome-Wide Association Studies and Beyond.Journal of cardiovascular development and disease · 2025Review
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Authors and funding
9 authors.
Funding
Abstract
Ischemic stroke (IS), characterized by complex etiological diversity, is a significant global health challenge. Recent advancements in genome-wide association studies (GWAS) and transcriptomic profiling offer promising avenues for enhanced risk prediction and understanding of disease mechanisms. GWAS summary statistics from the GIGASTROKE Consortium and genetic and phenotypic data from the UK Biobank (UKB) were used. Transcriptome-Wide Association Studies (TWAS) were conducted using FUSION to identify genes associated with IS and its subtypes across eight tissues. Colocalization analysis identified shared genetic variants influencing both gene expression and disease risk. Sum Transcriptome-Polygenic Risk Scores (STPRS) models were constructed by combining polygenic risk scores (PRS) and polygenic transcriptome risk scores (PTRS) using logistic regression. The predictive performance of STPRS was evaluated using the area under the curve (AUC). A Phenome-wide association study (PheWAS) explored associations between STPRS and various phenotypes. TWAS identified 34 susceptibility genes associated with IS and its subtypes. Colocalization analysis revealed 18 genes with a posterior probability (PP) H4 > 75% for joint expression quantitative trait loci (eQTL) and GWAS associations, highlighting their genetic relevance. The STPRS models demonstrated superior predictive accuracy compared to conventional PRS, showing significant associations with numerous UKB phenotypes, including atrial fibrillation and blood pressure. Integrating transcriptomic data with polygenic risk scores through STPRS enhances predictive accuracy for IS and its subtypes. This approach refines our understanding of the genetic and molecular landscape of stroke and paves the way for tailored preventive and therapeutic strategies.
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