Identification of novel and known variants in GAA, MYH6, NEXN, ATA3A, RHBDF1, and ACTA1 genes in coronary artery disease patients by Whole-exome sequencing

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 (<1%) or absent frequency in population databases. Among these, eight variants were novel and classified as likely pathogenic, reported in genes GAA, MYH6, NEXN, ATA3A, RHBDF1, and ACTA1. Most gene variants have functional significance in cardiovascular pathologies, as demonstrated by gene ontology and human phenotype ontology enrichment analyses reported in this study.

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.

Introduction

Coronary artery disease (CAD) represents a significant challenge to global health, with its prevalence rising alarmingly across diverse populations. Cardiovascular diseases, which include CAD, stand as a foremost contributor to morbidity and mortality across the globe, ranking as one of the leading causes of death in both developed and developing nations. According to the World Health Organization (WHO), in 2019 cardiovascular diseases, which include CAD, were responsible for approximately 32% of global deaths, translating to around 17.9 million individuals annually. The data also reported that out of the 17 million premature deaths (under the age of 70) due to non-communicable diseases in 2019, 38% were caused by CVDs (WHO factsheet) (1). In 2021, CVDs accounted for 20.5 million deaths, comprising approximately one-third of all global deaths (2). CAD has potential complications and poses a significant risk of events ranging from mild discomfort to fatal outcomes, including chest pain, myocardial infarction, arrhythmias, heart failure, and sudden cardiac death (3). Coronary artery disease is often accompanied by a range of comorbid conditions that can significantly exacerbate the symptoms and severity of CAD, leading to poor prognosis. The etiology of CAD is multifactorial, involving a complex interplay of genetic, environmental, and lifestyle factors. Traditional risk factors, including hypertension, hyperlipidemia, diabetes mellitus, tobacco use, obesity, and physical inactivity, have been associated with an increased likelihood of CAD, markedly impairing the quality of life. These factors contribute to the pathophysiological processes that lead to atherosclerosis, the underlying condition of CAD, by damaging the endothelial lining of blood vessels and promoting inflammation. Individuals suffering from Type 2 diabetes mellitus (T2DM) face an increased likelihood (2–4-fold) of developing coronary artery disease compared to those who do not have this condition (4). This heightened risk is primarily due to the metabolic and vascular complications associated with diabetes, which can lead to the early onset of atherosclerosis and other cardiovascular issues. It has been recorded that approximately 80% of patients with T2DM ultimately succumb to complications related to cardiovascular diseases, with CAD being a significant contributor to these fatalities (5). Studies indicate that patients with T2DM tend to have coronary plaques characterized by larger necrotic cores and heightened inflammation, featuring increased numbers of T lymphocytes and macrophages. Additionally, these patients exhibit a higher rate of positive remodeling and plaque ruptures compared to non-diabetic individuals, indicating a more aggressive atherosclerotic process (6). Studies using advanced genome-wide techniques have demonstrated that a substantial portion of the genetic component of CAD is attributable to common alleles at many loci, estimating CAD heritability at 35%–50% (7). Additionally, the Framingham Heart Study found that having a family history of cardiovascular disease in a parent or sibling was a significant predictor of developing the disease (8, 9). It has been observed in genome-wide association studies (GWAS) that the majority of common inter-individual genetic variations can be analyzed individually by comparing their frequency in disease cases and healthy controls (10). However, certain rare variants that are not usually identified in GWASs have also been reported in certain genes such as SCARB, LDLR, APOA, DYRK1B, MEF2A, and PDE5A, which cause a monogenic form of CAD (11, 12). It is understood that genetic information offers the only tool to guide primordial prevention (i.e., targeted interventions) before onset of traditional risk factors or visible disease manifestations. This can effectively identify subjects at risk with greater precision than conventional risk scores by using a polygenic risk score that analyzes and predicts the outcome at birth (13). Traditional genetic investigations of CAD primarily focus on canonical pathways involved in lipid metabolism, plaque stability, and systemic inflammation. Nevertheless, a substantial subset of CAD patients experiences acute ischemic events, severe myocardial remodeling, or sudden cardiac death, in which secondary structural or electrophysiological vulnerabilities significantly increase baseline risk. To investigate this genetic overlap, the present study employs whole-exome sequencing in conjunction with a comprehensive multi-disease super-panel approach. By examining genetic variations across integrated structural and electrical cardiac sub-panels, the study seeks to identify rare, high-impact modifiers that influence myocardial resilience during severe ischemic stress, thereby extending beyond conventional atherosclerotic risk metrics. Through this combined approach, we identified novel genetic variants that had not been previously documented.

Methodology

Subjects

The present study was conducted in accordance with the guidelines outlined in the Helsinki Declaration and was ethically approved by the University of Tabuk Research Ethics Committee (# UT-91-23-2020). A total of 28 clinically confirmed CAD patients were included in this study. All the participants signed the consent form. We included patients from the King Fahad Specialist Hospital, Tabuk, KSA, who underwent elective angiography for diagnosis of stable angina. The following tests were conducted: x-rays, exercise stress testing, myocardial perfusion imaging, ambulatory electrocardiography, Holter monitoring, chest echocardiogram, computed tomography coronary angiography, and multigated acquisition scans (MUGA). CAD was defined based on clinical symptoms (stable or unstable angina) and confirmed through invasive coronary angiography. Inclusion criteria specified macrovascular disease with at least 50% luminal diameter stenosis in one or more of the three major coronary arteries: left anterior descending (LAD), left circumflex (LCx), or right coronary artery (RCA). A total of 28 patients were consecutively selected from a larger registry to provide a representative sample of symptomatic obstructive CAD. Although electrocardiograms (ECG) and echocardiograms were performed as part of the initial diagnostic process for stable angina, the only inclusion criterion was structural CAD, defined as stenosis of 50% or greater. Patients were not pre-screened to exclude those with primary rhythm or myocardial disorders.

Genomic DNA preparation and sequencing

High-quality human genomic DNA was used as the starting material for this study. Library preparation was performed following the protocol outlined in the Twist Human Core Exome 2.0 Kit instruction manual. Sequencing was conducted on the Illumina NovaSeq 6000 platform according to the manufacturer's guidelines.

Quality control and preprocessing of sequencing data

The quality of the raw sequencing reads was assessed using FastQC (v0.11.9) (14) to ensure reliable data. Reads were then processed to remove sequencing adapters and low-quality bases using TrimGalore (v0.6.6), providing high-quality (HQ) reads for downstream analysis. Using a standard mapping protocol, the resulting HQ reads were aligned to the human reference genome (hg38).

Variant calling and annotation

The variant discovery process was conducted using the Genome Analysis Toolkit (GATK) v4.3 (15), following the best practice pipeline. HaplotypeCaller was employed to identify small variants, including single nucleotide variants (SNVs) and short insertions/deletions (InDels). Quality Score Recalibration (VQSR), with tranche sensitivity set to 99.5% for SNPs and 99.0% for indels. Hard filtering was applied to variants failing VQSR using stringent thresholds (QD < 2.0, FS > 60.0, and MQ < 40.0 for SNPs). Our final callset exhibited a Transition-to-Transversion (Ti/Tv) ratio of 1.99, an average variant call rate of 99%, and a mean on-target depth of ∼91X, with >95% of target bases covered at >20×.

Identified variants were annotated comprehensively using various databases and tools to gain biological and clinical insights. For gene identification and variant characterization, the RefSeq database was utilized. The Ensembl Variant Effect Predictor (VEP) was used to annotate variants, providing detailed information on their functional and biological consequences (16). Default VEP annotations were supplemented with several plugins, including dbNSFP (17), CADD (18), and Phenotypes (19), to enhance the annotation quality. Potential disease associations of variants were explored through publicly available databases such as OMIM (20), ClinVar (21), and UniProtVar (22). Population allele frequency data were sourced from the 1,000 Genomes Project (23) and gnomAD (24) (including both exome and genome datasets) to distinguish rare variants from common polymorphisms effectively. For functional prediction of mutations, tools integrated within dbNSFP such as SIFT (25), PolyPhen (26), FATHMM (27), MutationTaster (28), MutationAssessor (29), and PROVEAN (30) were employed. Missense Variants were further annotated using CADD scores. The SIFT-indel tool (31) was applied to evaluate the functional impact of InDels. Additionally, only canonical transcript-dependent consequences were retained in the final VEP-annotated file to ensure consistency and relevance in downstream analyses.

Filtering and classification of variants

Variants within these genes were prioritized for further analysis. To ensure the clinical relevance of the identified variants, we applied a multi-step filtering process. Variants with a population frequency of 1% or less in databases like gnomAD and 1,000 Genomes Project were retained. The analysis was restricted to protein-coding regions and canonical splice sites, prioritizing variants with “HIGH” or “MODERATE” VEP impact. To assess the potential clinical implications of the filtered variants, we employed a comprehensive annotation approach. Variants were annotated using clinical databases like ClinVar and UniprotVar to leverage existing clinical knowledge. Additionally, a combination of standalone and ensemble in silico prediction tools, including SIFT, PolyPhen-2, FATHMM, MutationTaster, Mutation Assessor, CADD, PROVEAN, and SIFT-Indels, were used to predict the impact of nonsynonymous variants, indels, and frameshift variants. Based on the combined information from clinical databases and in silico predictions, variants were classified into five categories: Benign, Likely Benign, Variant of Unknown Significance, Likely Pathogenic, and Pathogenic. The classification process prioritized clinical data over in silico predictions. In cases of conflicting clinical annotations, in silico predictions were used to make a final classification. For variants absent in clinical databases, stringent criteria based on CADD scores (≥20 for deleterious) and in silico tool predictions (at least 4 tools having consensus) were applied to classify them as Likely Pathogenic or Likely Benign. Variants that did not meet these criteria were classified as VUS.

Novelty of each variant was assessed by cross-referencing identified alterations with major public genomic repositories, such as dbSNP, ClinVar, gnomAD, COSMIC, and the 1,000 Genomes Project. For robust classification, an exhaustive PubMed literature search was conducted for each of the eight novel, likely pathogenic variants. None of these specific genomic coordinates have been previously reported in peer-reviewed case reports or cohort studies.

Functional annotation of candidate variant genes

To elucidate the functional impact of the identified pathogenic and likely pathogenic variants, we employed the g:profiler webserver (33) for enrichment analysis. This tool enables the systematic exploration of gene sets, identifying overrepresented Gene Ontology (GO) terms. By focusing on Gene Ontology—Molecular Function (GO:MF) and Biological Process (GO:BP) terms, as well as Human Phenotype ontology, we aimed to uncover the specific molecular and cellular processes influenced by these variants and their potential contribution to cardiovascular disease. Multiple testing corrections were controlled using the default g:SCS (Set Counts and Sizes) threshold algorithm, and only terms with an adjusted p-value (p_adj < 0.05) were considered statistically significant. In contrast to standard independent correction methods such as Bonferroni or Benjamini-Hochberg, g:SCS calculates a more stringent, analytically derived significance threshold (α = 0.05). This approach accounts for the complex, hierarchically dependent network structure characteristic of Gene Ontology (GO) and Human Phenotype Ontology (HPO) terms, thereby minimizing type I error while preserving biological signal.

Results

Study population characteristics

A total of 28 patients diagnosed with coronary artery disease were enrolled in this study. The median age of the participants was 53 years [interquartile range (IQR): 40–63], with a male predominance (86%). A significant proportion of patients had hypertension (54%) and hyperlipidemia (46%). Additionally, 54% were overweight or obese, and 54% were diabetic. In terms of angina presentation, 71% had stable angina, while 29% had unstable angina. A history of myocardial infarction (MI) was reported in 36% of the participants, with 14% experiencing ST-elevation myocardial infarction (STEMI), 14% experiencing non-ST-elevation myocardial infarction (NSTEMI), and 3.6% undergoing coronary artery bypass grafting (CABG). A familial history of cardiovascular disease was present in 36% of the participants. Laboratory parameters revealed a median cholesterol level of 4 mmol/L (IQR: 3–127), a median triglyceride level of 3 mmol/L (IQR: 1–160), a median LDL-C level of 90 mmol/L (IQR: 80–121), and a median HDL-C level of 1 mmol/L (IQR: 1–24). The median creatinine level was 71 μmol/L (IQR: 2–118), and the median hemoglobin level was 14.75 g/dL (IQR: 12.57–16.33). Table 1 enlist the patient characteristic of the study cohort.

Identified variants in the cohort

A total of 108 genes, overlapping with the GenePanel, were identified with variants in our whole-exome sequencing analysis. This analysis yielded 13,289 variants across the cohort, with a median of 545 variants per patient. The majority of these variants were either intronic (38%) or synonymous (21%). Additionally, 1,978 missense variants, 1,003 3′ UTR variants, 262 5′ UTR variants, and 84 frameshift variants were identified. Notably, 421 variants (3%) were novel and not previously reported in public databases such as dbSNP or COSMIC. Based on VEP impact categorization, 92 variants were classified as high impact, 3,688 as low impact, 2,106 as moderate impact, and 7,403 as modifier impact. The most frequently mutated genes were TTN, followed by GAA, SVIL, NRAP, and DMD. Figure 1 illustrates the methodology used in the study to identify the candidate variants. Figure 2 provides the key summary of the identified variants in the study cohort.

Figure 1

Figure 2

Candidate variant detection and classification

After filtering variants based on population frequency and VEP impact, 341 variants in 58 genes were identified. Of these, 122 variants (36%) were novel, not previously reported in public databases. The majority of the variants were missense (66%), followed by frameshifts (25%) and in-frame indels (5%). The remaining 4% were nonsense, splice site, and other protein-altering variants. To assess the clinical significance of these candidate variants, we annotated them using ClinVar, UniprotVar, and various in-silico prediction tools. The classification of these variants revealed a predominance of variants of unknown significance (VUS, 237), followed by likely benign (LB, 63), benign (B, 20), likely pathogenic (LP, 18), and pathogenic (P, 3) variants. TTN emerged as the most frequently mutated gene, being present in 96% of patient samples. Other frequently mutated genes included FLNC (29%), AGL (25%), ALPK3 (21%), and DSP (21%). Figure 3 provides a detailed overview of the candidate variants detected in each individual sample.

Figure 3

Rare and potentially pathogenic variant identification

This study identified 21 candidate variants classified as either pathogenic (n = 3) or likely pathogenic (n = 18) within a cohort of 12 patients with cardiovascular disease. Each patient harbored at least one such variant. A majority of the identified variants (n = 18) were unique to individual patients. Two variants, however, were recurrent: MYO6 (rs551348450; ENST00000369977.8:c.2751dup) was observed in three patients, and MYPN (rs71534278; ENST00000358913.10:c.3335C>T) was found in two patients. All identified variants were heterozygous.

Among the identified likely pathogenic variants, eight novel alterations were detected in the GAA, MYH6, NEXN, ATAD3A, RHBDF1, and ACTA1 genes. Notably, these specific genomic coordinates are absent from major population and clinical databases, such as dbSNP, ClinVar, gnomAD, and the 1,000 Genomes Project. Comprehensive literature searches further confirmed that these variants have not been previously reported in any peer-reviewed case reports or cohort studies.

All 21 variants met the American College of Medical Genetics and Genomics (ACMG) criteria for PM2 classification, indicating a very low (<1%) or absent frequency in population databases. Further ACMG classification revealed additional supporting evidence for pathogenicity for specific variants. The MYO6 (ENST00000369977.8:c.2751dup), NEXN (ENST00000334785.12:c.175G>T), and ATAD3A (ENST00000378756.8:c.274A>T) variants were assigned PVS1 classification due to their nature as null variants (nonsense, frameshift, canonical splice sites, initiation codon, single or multi-exon deletion) in genes where loss-of-function (LOF) is a known mechanism of disease.

Missense variants identified in TPM1 (ENST00000403994.9:c.574G>A), GAA (ENST00000302262.8:c.1559A>T), and ACTA1 (ENST00000366684.7:c.984G>C; ENST00000366684.7:c.989A>T; ENST00000366684.7:c.983A>T) were classified as PM1 due to their presence in known hotspot regions. Variants in NRAP (ENST00000359988.4:c.919G>A), RHBDF1 (ENST00000262316.10:c.1880C>T), MYH6 (ENST00000405093.9:c.3710T>G;ENST00000405093.9:c.4865G>A),FLII (ENST00000327031.9:c.1841A>G), and RPL3L (ENST00000268661.8:c.34G>A) received PM2 and PP3 classifications, as multiple lines of computational evidence suggested a deleterious effect on the respective gene or gene product. Although TTN was the most frequently mutated gene across the cohort, no TTN variants met the ACMG and in silico consensus criteria required for a pathogenic or likely pathogenic designation. All final prioritized pathogenic variants were restricted to non-TTN loci.

A detailed summary of the identified unique pathogenic and likely pathogenic variants within this cohort is provided in Table 2. The detailed clinical phenotype of each individual patient cross-referenced with their respective pathogenic or likely pathogenic variants is provided in Supplementary Table S1.

Functional characterization of pathogenic variants

To delve deeper into the functional implications of the identified pathogenic and likely pathogenic variants, we conducted a comprehensive Gene Ontology (GO) and Human Phenotype Ontology (HPO) enrichment analysis of the genes harboring these variants. The GO analysis revealed significant enrichment in specific molecular functions and biological processes. In terms of molecular function (GO:MF), the genes were enriched for actin filament binding (p_adj = 2.0 × 10⁻⁷) and muscle alpha-actinin binding (p_adj = 7.1 × 10⁻³). Regarding biological processes (GO:BP), the genes were enriched for myofibril assembly (p_adj = 1.4 × 10⁻⁹) and striated muscle cell development (p_adj = 1.8 × 10⁻⁹). These findings indicate that the identified variants may disrupt fundamental processes involved in cardiac muscle development and function. The HPO enrichment analysis provided insights into the potential clinical manifestations associated with these variants. Notably, the genes were enriched for several cardiovascular phenotypes, including abnormal left ventricular end-diastolic volume (p_adj = 7.9 × 10⁻⁵), orthopnea (p_adj = 1.6 × 10⁻⁴), thromboembolic stroke (p_adj = 1.9 × 10⁻⁴), lipoatrophy (p_adj = 2.3 × 10⁻⁴), and dilated cardiomyopathy (p_adj = 3.2 × 10⁻⁴). These findings suggest that the identified variants may contribute to the development of diverse cardiovascular disorders. Figure 4 provides a visual representation of the enriched GO and HPO terms, highlighting the functional impact of the identified pathogenic variants.

Figure 4

Discussion

Genome-wide association studies (GWAS) have been instrumental in identifying common genetic variants associated with CAD risk, with heritability estimates of 40%–50% attributed to common alleles at many loci (7). However, GWAS approaches have inherent limitations in detecting rare, high-impact variants that may significantly influence disease severity, myocardial resilience, or clinical outcomes in individual patients (34, 35). These rare variants, typically with allele frequencies <1%, are often missed by GWAS due to insufficient statistical power and the limitations of imputation-based genotyping arrays. Whole-exome sequencing overcomes these constraints by providing comprehensive, unbiased interrogation of protein-coding regions, enabling the discovery of rare and novel variants with potentially large effect sizes (36). While traditional genetic investigations of CAD have focused on canonical pathways in lipid metabolism, plaque stability, and systemic inflammation, WES allows exploration of variants in genes that may modulate myocardial structural integrity and electrical stability, factors that could lower the threshold for severe ischemic complications in patients with obstructive CAD. In an attempt to capture these potential modifiers, we have employed the Genomics England PanelApp's “Sudden Unexplained Death/Cardiac Event” super-panel, an integrated framework encompassing eight clinically curated cardiovascular sub-panels, including hypertrophic cardiomyopathy, dilated and arrhythmogenic cardiomyopathy, and various cardiac channelopathies. As mentioned such an approach allowed us to evaluate the presence of subclinical sarcomeric, desmosomal, and ion-channel variants that may influence phenotypic severity in our CAD cohort, extending beyond conventional atherosclerotic risk assessment.

An interesting finding of our study is the identification of variants in genes traditionally associated with primary cardiomyopathies such as TTN, MYH6, NEXN, ACTA1, FLNC, ALPK3 and DSP within a cohort of patients with obstructive coronary artery disease. This observation raises an important conceptual question about the genetic overlap between coronary atherosclerosis and myocardial structural disorders. Particularly since the CAD and cardiomyopathies have been usually studied as distinct clinical entities, with CAD attributed primarily to atherosclerotic vascular disease and cardiomyopathies to intrinsic myocardial dysfunction (37). However, our findings suggest that these conditions may share genetic determinants that influence myocardial resilience within the ischemic stress. It is hypothesised that in patients with significant coronary stenosis, subtle genetic alterations such as those in sarcomeric proteins, desmosomal components, or cytoskeletal elements could predispose to adverse myocardial remodeling, arrhythmogenesis, or reduced tolerance to ischemia; mechanisms that may explain why some patients with similar coronary anatomy experience more severe clinical outcomes than others.

The high frequency of TTN variants (96% of patients) in our cohort needs a specific clarification. TTN encodes titin, the largest protein in the human body and a critical component of the sarcomere, functioning as a biological spring and structural scaffold (38). While pathogenic TTN variants are well-established causes of dilated cardiomyopathy (39), the prevalence in our CAD cohort likely reflects the gene's enormous size and its inherent propensity for accumulating rare background variation. Our stringent filtering pipeline successfully distinguished incidental TTN variants from clinically relevant alterations as no TTN variants met our combined ACMG and in silico consensus criteria for pathogenic classification. This approach is critical to avoid over-interpretation of TTN variants in non-cardiomyopathy populations, as up to 1%–3% of healthy individuals may harbor rare TTN variants. Beyond TTN, we observed substantial mutation frequencies in FLNC (29%), AGL (25%), ALPK3 (21%), and DSP (21%). Filamin C (FLNC) maintains sarcomere structural integrity by crosslinking actin filaments (40), and FLNC variants are enriched in dilated cardiomyopathy (1%–4.5%) and hypertrophic cardiomyopathy (1.3%–8.7%) cohorts (41). ALPK3, encoding α-kinase 3, is a muscle-specific pseudokinase whose loss-of-function variants are associated with unique cardiomyopathy phenotypes in both children and adults (42, 43). Desmoplakin (DSP), a crucial desmosomal protein for cardiomyocyte cell-to-cell adhesion (44), has been linked to arrhythmogenic cardiomyopathy and left ventricular dysfunction, with DSP variant carriers facing significantly elevated rates of sustained ventricular arrhythmias and heart failure hospitalizations (45).

After applying strict population frequency filters (≤1% in gnomAD and 1,000 Genomes), VEP impact criteria (HIGH or MODERATE), and comprehensive in silico prediction consensus, we identified 21 variants classified as either pathogenic (n = 3) or likely pathogenic (n = 18) across 12 patients in our cohort. Each patient harbored at least one such variant, with all variants occurring in a heterozygous state. Most identified variants (n = 18) were unique to individual patients, suggesting a highly heterogeneous genetic landscape within our cohort. However, two recurrent variants were observed: MYO6 (rs551348450; c.2751dup) in three patients, and MYPN (rs71534278; c.3335C>T) in two patients. This recurrence pattern, though limited by sample size, hints at potential founder effects or mutational hotspots that may warrant investigation in larger populations. All 21 variants met ACMG PM2 criteria, indicating very low or absent population frequency (<1%). Additional ACMG classifications provided further evidence of pathogenicity. Variants in MYO6 (c.2751dup), NEXN (c.175G>T), and ATAD3A (c.274A>T) received PVS1 classification due to their nature as null variants (frameshift or stop-gained) in genes where loss-of-function is a known disease mechanism. Missense variants in TPM1 (c.574G>A), GAA (c.1559A>T), and ACTA1 (c.984G>C, c.989A>T, c.983A>T) were assigned PM1 classification due to localization in known functional hotspot regions. Additional PM2 and PP3 classifications supported the likely pathogenic designation for variants in NRAP, RHBDF1, MYH6, FLII, and RPL3L, based on multiple lines of computational evidence.

Notably, the genes harboring novel variants converge on common biological pathways that relate to caridiac function: sarcomeric structure and function (MYH6, NEXN, ACTA1), mitochondrial energetics (ATAD3A), lysosomal function (GAA), and membrane-associated signalling (RHBDF1). This convergence suggests that multiple pathways may contribute to myocardial vulnerability in CAD patients, and that individual patients may harbor distinct genetic susceptibilities. Moreover, the consistency across GO and HPO analyses from molecular function to biological process to clinical phenotype provides some convincing evidence that the identified variants are not merely statistical noise but likely contribute to genuine biological dysfunction with potential clinical consequences that may lower the threshold for ischemic injury in CAD.

Limitations

Our study has several limitations, with the most significant being the small sample size of 28 patients. Although this cohort allowed for a detailed exploratory evaluation of rare exomic variants through rigorous bioinformatics filtering, the limited sample size reduces the generalizability of our findings and diminishes the statistical power to identify broader monogenic associations. Future prospective, large-scale, multi-center studies are necessary to validate these novel variants in more diverse CAD populations. Furthermore, while our pipeline utilized strict global allele frequency cutoffs (≤1%) via gnomAD and the 1,000 Genomes Project, the current cohort was not filtered against localized regional databases such as the Greater Middle East (GME) Variome or Qatar Genome Programme. Consequently, we cannot entirely rule out the presence of population-specific rare polymorphisms. Accessing and cross-referencing local reference databases represents a critical target for our ongoing validation studies. Additionally, orthogonal wet-lab confirmation (e.g., Sanger sequencing) and familial segregation analyses were not performed, which represents a limitation of this exploratory analysis. Future work will focus on performing targeted Sanger verification and familial screening for the 8 identified novel likely pathogenic variants to definitively trace their inheritance patterns.

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.

References

• 1.

  WHO Factsheet-World Health Organization. Cardiovascular diseases (CVDs). Available online at:https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)(Accessed November 21, 2024).

  • View reference in article

• 2.

  LindstromMDeCleeneNDorseyHFusterVJohnsonCOLeGrandKEet al. Global burden of cardiovascular diseases and risks collaboration, 1990–2021. J Am Coll Cardiol. (2022) 80:2372–425. 10.1016/j.jacc.2022.11.001

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 3.

  KesslerTVilneBSchunkertH. The impact of genome-wide association studies on the pathophysiology and therapy of cardiovascular disease. EMBO Mol Med. (2016) 8(7):688–701. 10.15252/emmm.201506174

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 4.

  KatsikiNBanachMMikhailidisDP. Is type 2 diabetes mellitus a coronary heart disease equivalent or not? Do not just enjoy the debate and forget the patient!. Arch Med Sci. (2019) 15(6):1357–64. 10.5114/aoms.2019.89449

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 5.

  Abdul-GhaniMDeFronzoRADel PratoSChiltonRSinghRRyderREJ. Cardiovascular disease and type 2 diabetes: has the Dawn of a new era arrived?Diabetes Care. (2017) 40(7):813–20. 10.2337/dc16-2736

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 6.

  YahagiKKolodgieFDLutterCMoriHRomeroMEFinnAVet al. Pathology of human coronary and carotid artery atherosclerosis and vascular calcification in diabetes mellitus. Arterioscler Thromb Vasc Biol. (2017) 37:191–204. 10.1161/ATVBAHA.116.306256

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 7.

  RocheleauGClarkeSLAugusteGHasbaniNRMorrisonACHeathASet al. Rare variant contribution to the heritability of coronary artery disease. Nat Commun. (2024) 15(1):8741. 10.1038/s41467-024-52939-6

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 8.

  Lloyd-JonesDMNamBHD'AgostinoRBSrLevyDMurabitoJMWangTJet al. Parental cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults: a prospective study of parents and offspring. JAMA. (2004) 291(18):2204–11. 10.1001/jama.291.18.2204

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 9.

  MurabitoJMPencinaMJNamBHD'AgostinoRBSrWangTJLloyd-JonesDet al. Sibling cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults. JAMA. (2005) 294(24):3117–23. 10.1001/jama.294.24.3117

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 10.

  AltshulerDDalyMJLanderES. Genetic mapping in human disease. Science. (2008) 322:881–8. 10.1126/science.1156409

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 11.

  DangTAKesslerTWobstJWiererMBraenneIStromTMet al. Identification of a functional PDE5A variant at the chromosome 4q27 coronary artery disease locus in an extended myocardial infarction family. Circulation. (2021) 144(8):662–5. 10.1161/CIRCULATIONAHA.120.052975

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 12.

  ErdmannJKesslerTMunoz VenegasLSchunkertH. A decade of genome-wide association studies for coronary artery disease: the challenges ahead. Cardiovasc Res. (2018) 114(9):1241–57. 10.1093/cvr/cvy084

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 13.

  KesslerTSchunkertH. Coronary artery disease genetics enlightened by genome-wide association studies. JACC Basic Transl Sci. (2021) 6(7):610–23. 10.1016/j.jacbts.2021.04.001

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 14.

  AndrewsS. FastQC: A Quality Control Tool for High Throughput Sequence Data [Online]. (2010) 15(6). Available online at:http://www.bioinformatics.babraham.ac.uk/projects/fastqc

  • Google Scholar
  • View reference in article

• 15.

  PoplinRRuano-RubioVDePristoMAFennellTJCarneiroMOVan der AuweraGAet al. Scaling accurate genetic variant discovery to tens of thousands of samples bioRxiv, (2017) 201178.

  • Google Scholar
  • View reference in article

• 16.

  McLarenWGilLHuntSERiatHSRitchieGRThormannAet al. The ensembl variant effect predictor. Genome Biol. (2016) 17(1):122. 10.1186/s13059-016-0974-4

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 17.

  LiuXLiCMouCDongYTuY. dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs. Genome Med. (2020) 12:103. 10.1186/s13073-020-00803-9

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 18.

  RentzschPWittenDCooperGMShendureJKircherM. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. (2019) 47(D1):D886–94. 10.1093/nar/gky1016

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 19.

  KöhlerSGarganoMMatentzogluNCarmodyLCLewis-SmithDVasilevskyNAet al. The human phenotype ontology in 2021. Nucleic Acids Res. (2021) 49(D1):D1207–17. 10.1093/nar/gkaa1043

  • CrossRef
  • Google Scholar
  • View reference in article

• 20.

  HamoshAScottAFAmbergerJValleDMcKusickVA. Online Mendelian Inheritance in Man (OMIM). Hum Mutat. (2000) 15(1):57–61. 10.1002/(SICI)1098-1004(200001)15:1<57::AID-HUMU12>3.0.CO;2-G

  • Pubmed Abstract
  • Google Scholar
  • View reference in article

• 21.

  LandrumMJLeeJMRileyGRJangWRubinsteinWSChurchDMet al. Clinvar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. (2014) 42(Database issue):D980–5. 10.1093/nar/gkt1113

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 22.

  UniProt Consortium. The universal protein resource (UniProt). Nucleic Acids Res. (2008) 36(Database issue):D190–5. 10.1093/nar/gkm895

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 23.

  FairleySLowy-GallegoEPerryEFlicekP. The international genome sample resource (IGSR) collection of open human genomic variation resources. Nucleic Acids Res. (2020) 48(D1):D941–7. 10.1093/nar/gkz836

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 24.

  KarczewskiKJFrancioliLCTiaoGCummingsBBAlföldiJWangQet al. The mutational constraint spectrum quantified from variation in 141,456 humans [published correction appears in Nature. 2021 Feb;590(7846):E53] [published correction appears in Nature. 2021 Sep;597(7874):E3–E4]. Nature. (2020) 581(7809):434–43. 10.1038/s41586-020-2308-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 25.

  NgPCHenikoffS. Predicting deleterious amino acid substitutions. Genome Res. (2001) 11(5):863–74. 10.1101/gr.176601

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 26.

  AdzhubeiIASchmidtSPeshkinLRamenskyVEGerasimovaABorkPet al. A method and server for predicting damaging missense mutations. Nat Methods. (2010) 7(4):248–9. 10.1038/nmeth0410-248

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 27.

  RogersMFShihabHAMortMCooperDNGauntTRCampbellC. FATHMM-XF: accurate prediction of pathogenic point mutations via extended features. Bioinformatics (Oxford, England). (2018) 34(3):511–3. 10.1093/bioinformatics/btx536

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 28.

  SchwarzJMCooperDNSchuelkeMSeelowD. Mutationtaster2: mutation prediction for the deep-sequencing age. Nat Methods. (2014) 11:361–2. 10.1038/nmeth.2890

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 29.

  RevaBAntipinYSanderC. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. (2011) 39(17):e118. 10.1093/nar/gkr407

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 30.

  ChoiYChanAP. PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics. (2015) 31(16):2745–7. 10.1093/bioinformatics/btv195

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 31.

  HuJNgPC. SIFT Indel: predictions for the functional effects of amino acid insertions/deletions in proteins. PLoS One. (2013) 8(10):e77940. 10.1371/journal.pone.0077940

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 32.

  MartinARWilliamsEFoulgerRELeighSDaughertyLCNiblockOet al. Panelapp crowdsources expert knowledge to establish consensus diagnostic gene panels. Nat Genet. (2019) 51(11):1560–5. 10.1038/s41588-019-0528-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 33.

  KolbergLRaudvereUKuzminIAdlerPViloJPetersonH. G:Profiler-interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update). Nucleic Acids Res. (2023) 51(W1):W207–12. 10.1093/nar/gkad347

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 34.

  SunBBKurkiMIFoleyCNMechakraAChenC-YMarshallEet al. Genetic associations of protein-coding variants in human disease. Nature. (2022) 603(7899):95–102. 10.1038/s41586-022-04394-w

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 35.

  CasaburiGMcCulloughRD’ArgenioV. Establishing best practices for clinical GWAS: tackling imputation and data quality challenges. Int J Mol Sci. (2025) 26(13):6397. 10.3390/ijms26136397

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 36.

  LiBCicekMLiuN. Recent advances and challenges of rare variant association analysis in the biobank sequencing era. Front Genet. (2022) 13:1014947. 10.3389/fgene.2022.1014947

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 37.

  ElliottP. Towards a new classification of cardiomyopathies. Curr Cardiol Rep. (2023) 25(5):395–403. 10.1007/s11886-023-01849-y

  • CrossRef
  • Google Scholar
  • View reference in article

• 38.

  TharpCAHaywoodMESbaizeroOTaylorMRGMestroniL. The giant protein titin’s role in cardiomyopathy: genetic, transcriptional, and post-translational modifications of TTN and their contribution to cardiac disease. Front Physiol. (2019) 10:1436. 10.3389/fphys.2019.01436

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 39.

  JolfayiAGKohansalEGhasemiSNaderiNHesamiMMozafaryBazarganyMet al. Exploring TTN variants as genetic insights into cardiomyopathy pathogenesis and potential emerging clues to molecular mechanisms in cardiomyopathies. Sci Rep. (2024) 14(1):5313. 10.1038/s41598-024-56154-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 40.

  DengYYanJ. Force-dependent structural changes of Filamin C rod domains regulated by filamin C dimer. J Am Chem Soc. (2023) 145(27):14670–8. 10.1021/jacs.3c02303

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 41.

  VerdonschotJAJVanhoutteEKClaesGRFHelderman-van den EndenATJMHoeijmakersJGJHellebrekersDMEIet al. A mutation update for the FLNC gene in myopathies and cardiomyopathies. Hum Mutat. (2020) 41(6):1091–111. 10.1002/humu.24004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 42.

  AgarwalRWakimotoHPauloJAZhangQReichartDToepferCet al. Pathogenesis of cardiomyopathy caused by variants in ALPK3, an essential pseudokinase in the cardiomyocyte nucleus and sarcomere. Circulation. (2022) 146(22):1674–93. 10.1161/CIRCULATIONAHA.122.059688

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 43.

  LiTJinYLiuRHuaYZhouKLuoSet al. A novel compound heterozygous variant in ALPK3 induced hypertrophic cardiomyopathy: a case report. Front Cardiovasc Med. (2023) 10:1212417. 10.3389/fcvm.2023.1212417

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 44.

  RezaNDe FeriaAChownsJLHoffman-AndrewsLVannLKimJet al. Cardiovascular characteristics of patients with genetic variation in desmoplakin (DSP). Cardiogenetics. (2022) 12(1):24–36. 10.3390/cardiogenetics12010003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 45.

  GasperettiACarrickRTProtonotariosAMurrayBLaredoMVan Der SchaafIet al. Clinical features and outcomes in carriers of pathogenic desmoplakin variants. Eur Heart J. (2025) 46(4):362–76. 10.1093/eurheartj/ehae571

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 46.

  ZhaoHHMaZYingZXNiuFNLuoMTWangZet al. Clinical manifestations and acid alpha-glucosidase mutation characterization of a cohort of patients with late-onset Pompe disease in eastern China. Ann Transl Med. (2021) 9(24):1803. 10.21037/atm-21-3710

  • Pubmed Abstract
  • CrossRef
  • Google Scholar
  • View reference in article

• 47.

  ZhangJMaLZhangJHuangJWeiGLiuLet al. Altered expression of lysosomal hydrolase, acid α-glucosidase, gene in coronary artery disease. Coron Artery Dis. (2016) 27(2):104–8. 10.1097/MCA.0000000000000322

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 48.

  AnfinsonMFittsRHLoughJWJamesJMSimpsonPMHandlerSSet al. Significance of α-Myosin Heavy Chain (MYH6) variants in hypoplastic left heart syndrome and related cardiovascular diseases. J Cardiovasc Dev Dis. (2022) 9(5):144. 10.3390/jcdd9050144

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 49.

  WuCYanHSunJYangFSongCJiangFet al. NEXN Is a novel susceptibility gene for coronary artery disease in Han Chinese. PLoS One. (2013) 8(12):e82135. 10.1371/journal.pone.0082135

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 50.

  PengYYeJXuYHuangJWuYLiuWet al. Two genetic variants in NEXN and ABCC6 genes found in a patient with right coronary artery to right ventricle fistula combined with giant coronary aneurysm and patent ductus arteriosus. Front Cardiovasc Med. (2022) 9:1048795. 10.3389/fcvm.2022.1048795

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 51.

  FrazierAEComptonAGKishitaYHockDHWelchAMEAmarasekeraSSCet al. Fatal perinatal mitochondrial cardiac failure caused by recurrent de novo duplications in the ATAD3 locus. Med. (2021) 2(1):49–73. 10.1016/j.medj.2020.06.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 52.

  RezaNGargAMerrillSLChownsJLRaoSOwensAT. ACTA1 Novel likely pathogenic variant in a family with dilated cardiomyopathy. Circ Genom Precis Med. (2018) 11(10):e002243. 10.1161/CIRCGEN.118.002243

  • Pubmed Abstract
  • CrossRef
  • Google Scholar