Abstract
Background
The etiology of CAD is multifactorial, involving a complex interplay of genetic, environmental, and lifestyle factors, and CAD markedly increases the likelihood of impaired quality of life.
Aim
To identify potentially impactful genetic variants in patients with CAD that may influence the risk, severity, and clinical outcomes of the condition, we performed whole exome sequencing (WES) in 28 patients. Diverse risk factors such as gender and clinical conditions, including hypertension, hyperlipidemia, obesity, and T2DM, which are known contributors to cardiovascular adversities, have been found to be present in a significant proportion of the patients.
Results
Our study identified 13,289 variants in 108 genes across the cohort, with a median of 545 per patient. The most frequently mutated genes were TTN, followed by GAA, SVIL, NRAP, and DMD, all of which are related to sarcomere structural and functional integrity and cardiomyopathies. Analysis of VEP impact identified 341 variants in 58 genes with 122 novel variants (36%) not previously reported in public databases. The variants were classified as missense (66%), followed by frameshifts (25%) and in-frame indels (5%), with a minor proportion (4%) of nonsense, splice-site, and other protein-altering variants. Among these 58 genes, TTN emerged as the most frequently mutated gene, present in 96% of patient samples, while the other substantially mutated genes in patients included FLNC (29%), AGL (25%), ALPK3 (21%), and DSP (21%). A comprehensive analysis of candidate variants identified 21 variants classified as either pathogenic (3) or likely pathogenic (18), which may play a significant role in the development of cardiovascular disease. These variants met the American College of Medical Genetics and Genomics (ACMG) criteria for PM2 classification, indicating a very low (
Conclusion
The study has utilized whole exome sequencing to identify both known and novel gene variants that may contribute to disease risk, facilitating a deeper understanding of the genetic landscape of CAD. This high-resolution approach allows the detection of pathogenic and likely pathogenic variants in critical genes associated with cardiovascular health and provides evidence of functional significance, thereby elucidating complex interactions between genetic factors and enhancing understanding of disease mechanisms.