CardioWAS Pathway-Based RNA-Seq and Genome Integration Platform for CVD Omics-Wide Associations

The integration of transcriptomic and genomic data is essential for dissecting the molecular basis of complex cardiovascular diseases (CVD). Existing resources primarily focus on differential gene expression at the individual gene level, with limited support for pathway-level interpretation or genomic integration. We introduce CARDIOWAS (Cardiovascular Omics-Wide Association Analysis Platform), the first Shiny-based, cardiovascular-specific platform that performs pathway-centric expression profiling in conjunction with Genome-Wide Association Study (GWAS) integration. CARDIOWAS systematically aggregates and harmonizes RNA-seq data from 18 NCBI GEO studies, comprising 419 heart failure (HF) and 208 control samples, and applies robust batch correction followed by DESeq2-based differential expression analysis. Gene-level statistics are translated into pathway effect sizes using curated KEGG and Reactome annotations, enabling systematic evaluation of disease-relevant biological modules.

CARDIOWAS extends beyond transcriptomic profiling by incorporating GWAS-identified loci, mapping variants to proximal genes and pathways, and leveraging machine learning models to highlight transcriptome–variant–pathway associations. Both germline and somatic variant contributions can be interrogated, providing a framework to link non-coding variation with altered pathway activity. Application of the platform identified 12 HF-specific and 19 CAD-enriched pathways, implicating inflammatory, metabolic, and signaling processes central to disease biology. Implemented as a user-friendly Shiny interface, CARDIOWAS allows researchers to upload transcriptomic data, visualize perturbed pathways, evaluate variant burden, and prioritize druggable targets. By uniting large-scale RNA-seq data with GWAS evidence in a pathway-centric framework, CARDIOWAS represents a novel, reproducible, and accessible tool for precision medicine in CVD and offers a resource for omics-driven hypothesis generation and translational research.