Abstract
Current genome-wide association studies provide valuable insights into the genetic basis of ischaemic stroke (IS) risk. However, polygenic risk scores, the most widely used method for genetic risk prediction, have notable limitations due to their linear nature and inability to capture complex, nonlinear interactions among genetic variants. While deep neural networks offer advantages in modeling these complex relationships, the multifactorial nature of IS and the influence of modifiable risk factors present additional challenges for genetic risk prediction. To address these challenges, we propose a Chromosome-wise Multi-task Genomic (MetaGeno) framework that utilizes genetic data from IS and five related diseases. The framework includes a chromosome-based embedding layer to model local and global interactions among adjacent variants, enabling a biologically informed approach. Incorporating multi-disease learning further enhances predictive accuracy by leveraging shared genetic information. Among various sequential models tested, the Transformer demonstrated superior performance, and outperformed other machine learning models and PRS baselines, achieving an AUROC of 0.809 on the UK Biobank dataset. Risk stratification identified a two-fold increased stroke risk (HR, 2.14; 95% CI: 1.81-2.46) in the top 1% risk group, with a nearly five-fold increase in those with modifiable risk factors such as atrial fibrillation and hypertension. Finally, the model was validated on the diverse All of Us dataset (AUROC = 0.764), highlighting ancestry and population differences while demonstrating effective generalization. This study introduces a predictive framework that identifies high-risk individuals and informs targeted prevention strategies, offering potential as a clinical decision-support tool.