Integrative genomics and transcriptomics analysis identified key genes with high variance associated with heart failure, highlighting the potential of multi-omics to understand CVD pathophysiology.
Abstract Background Cardiovascular disease (CVD) is a leading cause of premature mortality in the United States and the world. CVD comprises several complex and mostly heritable conditions, which range from myocardial infarction to congenital heart disease. The risk factors contributing to the development of CVD and response to therapy in an individual patient are highly variable. Here, we report our findings from an integrative analysis of gene expression, disease‐causing gene variants and associated phenotypes among CVD populations, with a focus on high‐risk heart failure (HF) patients. Methods We built a cohort using electronic health records of consented patients with available samples and then performed high‐throughput whole genome and RNA sequencing of key genes responsible for HF and other CVD pathologies. Our in‐depth gene expression analysis revealed differentially expressed genes associated with HF and other CVDs. We performed a variant analysis of whole genome sequence data of CVD patients and identified genes with altered gene expression with functional and non‐functional mutations in these genes. Results Our results highlight the importance of investigating the mechanisms of CVD progression through multi‐omics datasets. Next, we performed splice mutation and variant distribution analysis of genes associated with HF and other CVD. We implemented Jensen–Shannon divergence (JSD)‐based method and identified HBA1 , FADD , ADRB2 , NPPB , ADRB1 , ADB and NPPC genes with the greatest variance based on their JSD scores. Our study provided evidence that applying integrative data analysis approach involving genomics and transcriptomics data will not only help understand the pathophysiology of CVD diseases but also reduce heterogeneity in disease subtypes.
Ahmed et al. (Tue,) studied this question.