Unravelling the genetic architecture of cardiovascular disease through structural variant detection with whole-genome sequencing

TL;DR

This paper reviews how structural variants in the genome contribute to cardiovascular disease and how whole-genome sequencing can help detect them for better diagnosis and treatment.

Contribution

The paper provides a comprehensive overview of structural variant detection methods and their potential clinical applications in cardiovascular disease.

Findings

Structural variants play a significant role in cardiovascular disease development.

Whole-genome sequencing enables comprehensive detection of structural variants.

Integrating structural variant data with epidemiology can improve personalized treatment strategies.

Abstract

Cardiovascular disease (CVD) remains the leading cause of worldwide morbidity and mortality. Studies have found that there is a significant genetic component contributing to CVD development. Advances in genome sequencing technologies have revolutionized the identification of disease-causing variants in the human genome. With the development of whole genome sequencing (WGS), the understanding of these variants has been deepened as it enables comprehensive detection of many variants in the genome including structural variants (SVs). SVs are large genomic variants that are present in the genome of an organism and play a significant role in disease. Numerous techniques are being used to detect SVs with varying accuracy levels. Due to the limited number of focused research studies on SVs and CVD, there is a rich opportunity for further investigation with the aim of utilizing SV data in disease diagnosis and treatment plans. Emerging evidence highlights the role of SVs in CVD and the importance of adopting WGS approaches to unravel the genetic architecture of CVD. Moreover, integrating SV data with population scale epidemiology and advanced risk prediction models would enhance CVD prevention by enabling more personalized treatment strategies. This review aims to describe the different types of SVs and their involvement in CVD development and then to discuss WGS-based SV detection methods and future clinical implementations. We also report an overview of the SVs identified across various CVD types and different bioinformatics tools that can be used to detect SVs in WGS data.

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