Investigating genes associated with cardiovascular disease among heart failure patients for translational research and precision medicine

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.

Deciphering expression and variants in cardiovascular disease genes among heart failure population for precision medicine

“… 4 DNA‐based gene variant detection when combined with RNA‐seq‐driven gene expression analysis has the potential to reveal novel and sensitive biomarkers and stratify CVD patient populations based on their disease risk. 5 , 6 The genetic variants predisposing to CVD span from rare and deleterious mutations that may be responsible for familial aggregation. 6 Investigating differentially expressed genes (DEGs) and disease‐causing variants can support finding the root cause of uncertainties in patient care.…”

“… 5 , 6 The genetic variants predisposing to CVD span from rare and deleterious mutations that may be responsible for familial aggregation. 6 Investigating differentially expressed genes (DEGs) and disease‐causing variants can support finding the root cause of uncertainties in patient care. 7 Understanding of the genetic basis of complex CVD can hamper genetic risk scoring which can now outperform traditional risk factors in risk prediction.…”

“…To improve the deciphering of CVD mechanisms, here, we report our investigation of expression and variants among known genes that are responsible for the development of CVD. 5 , 6 Supporting this study, we developed an open‐source, cross‐platform, interactive, and user‐friendly bioinformatics pipeline (GVViZ) for RNA‐seq data pre‐processing, and gene expression data analysis, annotation with relevant diseases, and heatmap visualization without requiring a strong computational background from the user. 8 In addition, we have developed a new bioinformatics pipeline (JWES), for variant discovery and interpretation, and big data modelling and visualization.…”

“…In this study, we conducted analysis of gene expression, disease‐causing gene‐variants, and associated phenotypes among CVD populations, with a focus on high‐risk Heart Failure (HF). 5 , 6 We built a cohort of CVD patients, 40 male and 21 female individuals ( n = 61), aged between 45 to 92, with self‐described race (42 Whites, 7 Blacks or African Americans, 1 Asian, and 11 of unknown race). We collected blood samples, performed RNA‐seq and gene expression analysis to generate transcriptomic profiles ( Figure 1 A ).…”

“…The most common mutation types in HF and CVD genes were intronic, flanking (5′ and 3′ flank) mutations. 6 Mutations in these genes have been linked to aberrant expression in CVD. WGS allowed us to do in‐depth analysis of CVD genes as RNA‐seq cannot detect any of the variants located in noncoding DNA regions.…”