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

The integration of transcriptomic and genomic data is critical for understanding the molecular underpinnings of complex diseases. While numerous tools exist for differential gene expression analysis, few platforms offer pathway-specific expression profiling alongside comprehensive integration with Genome-Wide Association Study (GWAS) data. We present OWAS (Omics-Wide Association Analysis Platform), a novel and user-friendly Shiny-based tool that performs pathway-centric differential expression analysis and integrates transcriptomic signals with GWAS-identified loci to elucidate key molecular mechanisms driving cardiovascular diseases (CVD), including heart failure (HF) and coronary artery disease (CAD). OWAS systematically aggregates and harmonizes RNA-seq datasets from 18 NCBI GEO studies, encompassing 208 controls and 419 HF samples, applies robust batch effect correction, and utilizes DESeq2 for gene-level statistics. Pathway-specific effect sizes are calculated using curated gene sets from KEGG and Reactome. Furthermore, OWAS leverages machine learning models to identify transcriptome-variant-pathway linkages by mapping GWAS variants to expression profiles, highlighting the impact of both germline and somatic variants on critical disease pathways. Our platform identified 12 HF- and 19 CAD-associated pathways, uncovering novel insights into inflammatory, metabolic, and signaling cascades altered in disease. OWAS enables translational exploration by allowing researchers to upload patient-level transcriptome data, visualize perturbed pathways, assess variant burden, and prioritize druggable targets. This integrative, pathway-focused approach provides a significant advancement toward precision medicine in CVD and offers a powerful resource for omics-driven hypothesis generation and clinical research.