Next-Generation Sequencing for Personalized Cardiovascular Disease Care

Next-Generation Sequencing for Personalized Cardiovascular Disease Care Cardiovascular disease (CVD) is the leading cause of death worldwide. Prediction and prevention of CVD, such as coronary artery disease and atherosclerosis, traditionally depend on identification of risk factors. These factors are effective in the general assessment of CVD risk but are not consistent indicators for all individuals. Therefore, CVD research has been recently expanded to include the identification of omic biomarkers (eg, genomic, transcriptomic, and epigenomic) that may (1) improve our understanding of the molecular mechanisms of CVD, (2) facilitate the development of personalized CVD care, and (3) reduce CVD mortality rates by accurately identifying high-risk individuals. Next-generation sequencing (NGS) is a promising technology to identify omic biomarkers. Because of its high-throughput capability in discovering novel genomic features with base-pair resolution, NGS is projected to play an increasingly important role in clinical diagnostics and personalized medicine for CVD. NGS and associated bioinformatics methods have been applied to cardiovascular genomics, transcriptomics, and epigenomics. Figure 1 illustrates 4 NGS applications such as (1) identification of differentially expressed genes (DEGs) using RNA sequencing (RNA-seq), (2) identification of proteinbinding regions in the genome using chromatin immunoprecipitation sequencing (ChIP-seq), (3) identification of genetic variants in exon regions using exome sequencing, and (4) identification of genomic methylation patterns using methyl-CpGbinding domain sequencing. These applications identify and quantify omic biomarkers that may be clinically viable for early disease diagnosis and effective disease treatment and management. In this article, we focus on 2 major applications of NGS technology: (1) RNA-seq, which has enabled researchers to characterize CVD by studying transcriptome-wide expression profiles, alternative splicing patterns, and miRNA regulatory networks and (2) ChIP-seq, which has enabled researchers to examine the epigenetic mechanisms of CVD by profiling the genome-wide pattern of protein-binding regions (eg, transcription factors and enhancers) or histone modifications. Although NGS provides a great opportunity for discovering potential CVD biomarkers, a few NGS bioinformatics challenges remain: first, the overwhelming NGS data volume requires huge data storage and computational resources. Second, at each data analysis step, multiple bioinformatics tools are publicly available, which makes it challenging to assemble sensible NGS bioinformatics pipelines. Third, 1 single NGS experiment processed by various NGS bioinformatics pipelines can result in a wide variety of information with different biological and clinical interpretation and validation. The rest of this article is organized as follows: In the NGS Bioinformatics for CVD section, we will review the challenges and progress of bioinformatics in NGS data analysis with a focus on RNA-seq and ChIP-seq. In the Case Studies: RNA-seq and ChIP-seq Bioinformatics for CVD section, we will present 2 CVD case studies to illustrate the applications of NGS bioinformatics. Finally, in the Opportunities of NGS for CVD section, we will summarize future opportunities for CVD research using NGS.