| Title: | Integrating transcriptomic and polygenic risk scores to enhance predictive accuracy for ischemic stroke subtypes |
| Journal: | Human Genetics |
| Published: | 18 Nov 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39551887/ |
| DOI: | https://doi.org/10.1007/s00439-024-02717-7 |
Publication 14102
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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.</p>
9 Authors
- Xuehong Cai
- Haochang Li
- Xiaoxiao Cao
- Xinyan Ma
- Wenhao Zhu
- Lei Xu
- Sheng Yang
- Rongbin Yu
- Peng Huang
1 Application
| Application ID | Title |
|---|---|
| 144904 | Integrative Deep Learning Analysis of UKBB Data: Bridging Genotypes, Medical Imaging, and Polygenic Scores for Precision Health Insights |
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