Cai Xuehong, Li Haochang, Cao Xiaoxiao, Ma Xinyan, Zhu Wenhao, Xu Lei, Yang Sheng, Yu Rongbin, Huang Peng
Department of Epidemiology, Center for Global Health, School of Public Health, Key Laboratory of Public Health Safety and Emergency Prevention and Control Technology of Higher Education Institutions in Jiangsu Province, National Vaccine Innovation Platform, Nanjing Medical University, Nanjing, 211166, China.
Department of Biostatistics, Center for Global Health, School of Public Health, National Vaccine Innovation Platform, Nanjing Medical University, Nanjing, China.
Hum Genet. 2025 Jan;144(1):43-54. doi: 10.1007/s00439-024-02717-7. Epub 2024 Nov 18.
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.
缺血性中风（IS）病因复杂多样，是一项重大的全球健康挑战。全基因组关联研究（GWAS）和转录组分析的最新进展为加强风险预测和理解疾病机制提供了有前景的途径。使用了GIGASTROKE联盟的GWAS汇总统计数据以及英国生物银行（UKB）的遗传和表型数据。使用FUSION进行转录组全关联研究（TWAS），以确定与IS及其在八个组织中的亚型相关的基因。共定位分析确定了影响基因表达和疾病风险的共享遗传变异。通过使用逻辑回归将多基因风险评分（PRS）和多基因转录组风险评分（PTRS）相结合，构建了总和转录组-多基因风险评分（STPRS）模型。使用曲线下面积（AUC）评估STPRS的预测性能。一项全表型关联研究（PheWAS）探索了STPRS与各种表型之间的关联。TWAS确定了34个与IS及其亚型相关的易感基因。共定位分析揭示了18个基因，其联合表达数量性状位点（eQTL）和GWAS关联的后验概率（PP）H4>75%，突出了它们的遗传相关性。与传统的PRS相比，STPRS模型显示出卓越的预测准确性，与包括心房颤动和血压在内的众多UKB表型存在显著关联。通过STPRS将转录组数据与多基因风险评分相结合，可提高对IS及其亚型的预测准确性。这种方法深化了我们对中风遗传和分子格局的理解，并为量身定制的预防和治疗策略铺平了道路。
