Genetics of Congenital Heart Disease

Introduction

Congenital heart disease (CHD) is the most common congenital anomaly, affecting nearly 1% of all live births and is a significant contributor to neonatal morbidity and mortality worldwide.¹ Over the past several decades, there have been significant advancements in prenatal care, diagnosis, medical management and surgical care of infants and children with CHD which has resulted in dramatic improvements in the survival of this patient population, even for more complex types of CHD.² Accordingly, there is a growing population of CHD survivors reaching adulthood that often have long-term disease and associated co-morbidities. While the anatomical and physiological complexities of CHD have been studied extensively and are used to guide clinical management, the understanding of underlying etiology for CHD is still ongoing. There have been significant strides in our understanding of the molecular pathways governing the formation of the four chambered human heart and great vessels and this has informed our understanding of the genetic basis of CHD etiology.³ Historically, CHD was primarily attributed to environmental factors or chromosomal anomalies detected through chromosome analysis or karyotyping. However, the past few decades have witnessed transformative advances in genomic technologies, reshaping our understanding of CHD pathogenesis and revealing its intricate genetic foundations.

The evolution of genomic tools, from the discovery of microdeletions through fluorescence in situ hybridization to high-resolution chromosomal microarrays, exome sequencing (ES) and genome sequencing (GS), has profoundly enhanced our ability to identify genetic contributors to CHD.^4,5 These advancements have not only uncovered pathogenic variation in a single gene and rare syndromic associations, but also highlighted the interplay between de novo mutations, oligogenic/polygenic inheritance, and gene-environment interactions.^6,7 Notably, the advent of next-generation sequencing has facilitated the identification of novel CHD-associated genes, offering new insights into developmental pathways and the molecular mechanisms underlying cardiac morphogenesis.⁸

This review aims to provide an updated synthesis of the genetic contributors to CHD, with a particular emphasis on recent advances in gene discovery and their clinical implications. By summarizing key findings in the field, we hope to bridge the gap between genomic research and clinical practice, highlighting how genetic discoveries inform risk stratification, guide reproductive counseling, and shape precision medicine approaches for patient and their families. Through this lens, we highlight the continued importance of integrating genomic advancements into perinatal and pediatric care to improve outcomes for individuals with CHD.

Established Genetic Contributors to CHD Etiology

The etiology of CHD is multifactorial where genetic and environmental contributors have both been implicated. Genetic abnormalities associated with CHD are very heterogeneous, and include chromosomal anomalies (~13%, range: 9–18%),⁹ copy number variants (CNVs) (~10–15%, ranging from 3–25% in syndromic CHD and 3–10% in non-syndromic CHD),^10,11 and single gene disorders (~12%) (Figure 1).^12,13

Figure 1.

Etiologies of congenital heart disease (CHD). Genetic contributors to CHD, of which the majority (~55%) remain unknown. All estimates are approximate based on publications (9–13).

Aneuploidies and CNVs Associated with CHD

Chromosomal aneuploidies and CNVs found with CHD are shown in Table 1. These aneuploidies include trisomy 21, 18, and 13 and sex chromosome aneuploidies such as Turner syndrome, which are detectable by karyotype.^14–16 CNVs vary in size from single genes to large deletions or duplications of millions of base pairs and pathogenic CNVs that are detected by chromosomal microarray or fluorescent in situ hybridization, in the past. CNVs include 22q11.2 deletion syndrome (DiGeorge syndrome),¹⁷ Williams-Beuren syndrome (7q11.23 deletion),¹⁸ Jacobsen syndrome (11q terminal deletion disorder),¹⁹ and 1p36 deletion syndrome.²⁰

Table 1.

Chromosomal Aneuploidies and Copy Number Variants Associated With CHD

Chromosome Change	Gene	Cardiac Defect	Percent with CHD	Clinical features	References
Chromosomal Aneuploidies
Trisomy 21 (Down syndrome)	Unknown	AVSD, ASD, VSD, PDA, TOF	40–50%	Short stature, cognitive deficits, atlantoaxial instability, immune system dysfunction, hypotonia, hypothyroidism, premature aging	21788214 20624800
Trisomy 18 (Edwards syndrome)	Unknown	ASD, VSD, PDA, TOF, DORV, TGA, CoA, BAV	80–90%	IUGR, polyhydramnios, micrognathia, short sternum, hypertonia, rocker-bottom feet, overlapping fingers and toes, tracheoesophageal fistula, congenital diaphragmatic hernia, omphalocele, renal anomalies, biliary atresia, severe intellectual disability	26347425
Trisomy 13 (Patau syndrome)	Unknown	ASD, VSD, PDA, HLHS, atrial isomerism	57–80%	Cleft lip and palate, scalp defects, hypotelorism, microphthalmia or anophthalmia, colobomata of irides, holoprosencephaly, microcephaly, deafness, severe intellectual disability, rib abnormalities, polydactyly, omphalocele, renal abnormalities, hypospadias, cryptorchidism, uterine abnormalities	26347425 21990216
Monosomy X (Turner syndrome)	Unknown	CoA, BAV, AS, HLHS, Dilated Ao	23–35%	Short stature (partially growth hormone responsive), cognitive deficits, ADHD, lymphedema, widely spaced hypoplastic nipples, webbed neck, primary amenorrhea	28705803 25517026
Copy Number Variants
22q11.2 deletion syndrome (DiGeorge syndrome)	TBX1	Conotruncal defects, IAA-B, TOF, conoventricular VSD	75%	Cleft palate, bifid uvula, velopharyngeal insufficiency, microcephaly, hypocalcemia/hypoparathyroidism, thymic hypoplasia/immune deficit, psychiatric disorder, learning disability	23604262 26742502
1p36 deletion syndrome	Unknown	PDA, VSD, ASD, BAV, Ebstein’s anomaly, LVNC	70%	Growth deficiency, intellectual disability, microcephaly, deep-set eyes, low-set ears, hearing loss, hypotonia, seizures, central nervous system defects, genital anomalies	18245432
Deletion 7q11.23 (Williams-Beuren syndrome)	ELN	SVAS, PS, PPS	80%	Unusual facies, thick lips, strabismus, stellate iris pattern, intellectual disability	20089974 12161592
Deletion 11q (Jacobsen syndrome)	ETS1 FLI1	HLHS, AS, VSD, CoA, Shone’s complex	56%	Growth retardation, developmental delay, thrombocytopenia, platelet dysfunction, widely spaced eyes, strabismus, broad nasal bridge, thin upper lip, prominent forehead, intellectual disability	15266616 26285164

ADHD, attention deficit/hyperactivity disorder; AS, aortic stenosis; ASD, atrial septal defect; AVSD, atrioventricular septal defect; BAV, bicuspid aortic valve; CHD, congenital heart disease; CNVs, copy number variants; CoA, coarctation of the aorta; Dilated Ao, dilated ascending aorta; DORV, double-outlet right ventricle; HLHS, hypoplastic left heart syndrome; IAA-B, interruption of aortic arch type B; IUGR, intrauterine growth retardation; LVNC, left ventricular noncompaction cardiomyopathy; PDA, patent ductus arteriosus; PPS, peripheral pulmonary stenosis; PS, pulmonary stenosis; SVAS, supravalvular aortic stenosis; TGA, transposition of great arteries; TOF, tetralogy of Fallot and VSD, ventricular septal defect.

Well-Characterized Syndromes Caused by Single-Gene Variation

Well-characterized syndromes caused by single gene variants are summarized in Table 2, including Alagille,²¹ Holt-Oram,²² Char,²³ Ellis-van Creveld,²⁴ Kabuki,²⁵ CHARGE,²⁶ and Noonan syndromes.²⁷

Table 2.

Well-Characterized Syndromes Caused by Single-Gene Variation Associated with CHD

Syndrome	Gene	Loci	Cardiac Defect	Percent with CHD	Clinical findings	References
Alagille syndrome	JAG1 NOTCH2	20p12.2 1p12-p11	PPS, TOF, PA	>90	Bile duct paucity, posterior embryotoxon, butterfly vertebrae, renal defects	21934706 16773578
Char syndrome	TFAP2B	6p12.3	PDA, VSD	58%	Wide-set eyes, down-slanting palpebral fissures, thick lips, hand anomalies	10802654
CHARGE syndrome	CHD7	8q12	TOF, PDA, DORV, AVSD, VSD	75–85%	Coloboma, choanal atresia, genital hypoplasia, ear anomalies, hearing loss, developmental delay, growth retardation, intellectual disability	28160409
Ellis-van Creveld syndrome	EVC EVC2	4p16.2 4p16.2	Common atrium	60%	Skeletal dysplasia, short limbs, polydactyly, short ribs, dysplastic nails, respiratory insufficiency	10700184 12571802
Holt-Oram syndrome	TBX5	12q24.1	VSD, ASD, AVSD, conduction defects	50%	Absent, hypoplastic, or triphalangeal thumbs; phocomelia; defects of radius; limb defects more prominent on left	16183809
Kabuki syndrome	KMT2D KDM6A	12q13 Xp11.3	CoA, BAV, VSD, TOF, TGA, HLHS	50%	Growth deficiency, wide palpebral fissures, large protuberant ears, fetal finger pads, intellectual disability, clinodactyly	12002156 21671394
Noonan syndrome	PTPN11 SOS1 RAF1 KRAS NRAS RIT1 SHOC2 SOS2 BRAF	12q24.13 2p22.1 3p25.2 12p12.1 1p13.2 1q22 10q25.2 14q21.3 7q34	Dysplastic PVS, ASD, TOF, AVSD, HCM, VSD, PDA	75%	Short stature, hypertelorism, down-slanting palpebral fissures, ptosis, low posterior hairline, pectus deformity, bleeding disorder, chylothorax, cryptorchidism	20876176

ASD, atrial septal defect; AVSD, atrioventricular septal defect; BAV, bicuspid aortic valve; CHARGE, coloboma, heart defects, choanal atresia, retarded growth and development, genital anomalies, and ear anomalies; CHD, congenital heart disease; CoA, coarctation of the aorta; DORV, double-outlet right ventricle; HCM, hypertrophic cardiomyopathy; HLHS, hypoplastic left heart syndrome; PA, pulmonary atresia; PDA, patent ductus arteriosus; PPS, peripheral pulmonary stenosis; PVS, pulmonary valve stenosis; TGA, transposition of great arteries; and VSD, ventricular septal defect.

Non-syndromic CHD attributable to single-gene variation

Single gene etiologies of CHD were first discovered by positional cloning and linkage approaches and targeted sequencing of candidate genes based on studies of large, multigenerational kindreds in which multiple family members were affected with CHD. Despite identification of an increasing number of candidate genes in children with CHD involved in the molecular regulation of cardiac development, establishing disease causality remains challenging. Genes harboring pathologic variants associated with non-syndromic CHD can be divided into transcription factors, cell signaling and adhesion molecules, and cardiac cardiac proteins.⁵ Non-syndromic CHD genes with sufficient evidence are shown in Table 3 and briefly discussed here.

Table 3.

Genes associated with non-syndromic congenital heart disease (selected)

Gene	Cardiovascular malformation	OMIM	References
Transcription factors
CITED2	ASD, VSD	602937	16287139
GATA4	ASD, VSD, AVSD, PS, TOF	600576	12845333 15235040 15810002 15689439 18055909 18076106
GATA5	ASD, VSD, DORV, TOF, BAV	611496	23031282 24638895
GATA6	PTA, TOF, ASD, BAV	601656	19666519 20581743 20631719 37700164
HAND1	AVSD, DORV, HLHS, ASD, VSD	602406	18276607 19586923
HAND2	TOF, LVNC, VSD	602407	20819618 26865696
MED13L	TGA	608771	14638541
NR2F2	AVSD, AS, CoA, VSD, HLHS, TOF	107773	24702954
NKX2–5	ASD, atrioventricular conduction delay, TOF, HLHS, VSD	600584	9651244 10587520 11714651 14607454
NKX2–6	PTA	611770	15649947 24421281
TBX1	DORV, TOF, IAA, PTA, VSD	602054	14585638
TBX5	AVSD, TOF, BAV, CoA, ASD, VSD	601620	22543974
TBX20	ASD, VSD, MS, DCM	606061	17668378 27510170
ZFPM2/FOG2	TOF, DORV	603693	14517948
Cell signaling and adhesion proteins
ACVR1/ALK2	AVSD	102576	19506109
CRELD1	ASD, AVSD	607170	12632326
FOXH1	TOF, TGA, VSD	603621	18538293
GJA1	HLHS, VSD, PA	121014	11470490 19615768
HEY2	AVSD	604674	16329098
JAG1	TOF, PS	601920	9207788 9207787 11152664
NOTCH1	BAV, AS, HLHS, TOF, PS, ASD, VSD, CoA, DORV	190198	16025100 26820064
PDGFRA2	TAPVR	173490	20071345
SMAD6	BAV, CoA, AS	602931	22275001
TAB2	BAV, AS, TOF	605101	20493459
VEGFA	TOF, PDA, AS, BAV, CoA, IAA, VSD	192240	30232381 20420808
Structural proteins
ACTC1	ASD, HCM, DCM, LVNC	102540	17947298
DCHS1	MVP	603057	26258302
ELN	SVAS	130160	7693128 11175284
MYH6	ASD, HCM, DCM	160710	15735645 20656787
MYH7	Ebstein’s anomaly, LVNC, HCM, DCM	160760	18159245 21127202
MYH11	PDA, TAA	160745	16444274 27418595

AS, aortic stenosis; ASD, atrial septal defect; AVSD, atrioventricular septal defect; BAV, bicuspid aortic valve; CoA, coarctation of the aorta; DCM, dilated cardiomyopathy; DORV, double-outlet right ventricle; HCM, hypertrophic cardiomyopathy; HLHS, hypoplastic left heart syndrome; IAA, interruption of aortic arch; LVNC, left ventricular noncompaction cardiomyopathy; MS, mitral valve stenosis; MVP, mitral valve prolapse; PA, pulmonary atresia; PDA, patent ductus arteriosus; PS, pulmonary stenosis; PTA, persistent truncus arteriosus; SVAS, supravalvular aortic stenosis; TAA thoracic aortic aneurysm; TAPVR total anomalous pulmonary venous return; TGA, transposition of great arteries; TOF, tetralogy of Fallot; and VSD, ventricular septal defect.

Transcription factors

Disease-causing variants in transcription factors important for cardiovascular development in multiple animal model systems have been identified in human genetic studies as the pathogenesis of non-syndromic CHD.²⁸ Variants in the homeobox transcription factor NKX2–5 were first reported in four kindreds with autosomal dominant CHD.²⁹ Familial atrial septal defect (ASD) along with atrioventricular conduction abnormalities is the major phenotype associated with pathogenic NKX2–5 variations,³⁰ which also lead to a range of CHD including ventricular septal defect (VSD), tetralogy of Fallot (TOF), subvalvar aortic stenosis, pulmonary atresia and hypoplastic left heart syndrome (HLHS).³¹ These findings have been supported by studies in mice harboring Nkx2–5 variants that recapitulate human cardiac phenotypes.³²

Pathogenic variants in the GATA family of transcription factors have been discovered in various types of CHD. Heterozygous pathologic variants in GATA4 were first identified in familial CHD, primarily with septation defects.³³ GATA4 pathogenic variation has subsequently been linked to not only ASD, VSD, and atrioventricular septal defect (AVSD) but also pulmonary stenosis (PS), and TOF.^34,35 Animal models of mice haploinsufficient for Gata4 or harboring disease-causing Gata4 variants have replicated human disease phenotypes.^36–38 GATA5 genetic variants have been found in a variety of CHD subtypes, including bicuspid aortic valve (BAV), VSD, TOF and double outlet right ventricle (DORV).^39–41 Deletion of Gata5 in mice also results in BAV.⁴² Pathogenic variation in GATA6 was first described in patients with persistent truncus arteriosus (PTA)⁴³ and has been since found in multiple CHD subtypes, including TOF, DORV, transposition of the great arteries (TGA), ASD, VSD, PS and BAV.^44,45 Mice haploinsufficient for Gata6 exhibited BAV.⁴⁶ GATA6 pathologic variation has been associated with a spectrum of extracardiac phenotypes, including pancreatic agenesis, diabetes mellitus, congenital diaphragmatic hernia, and intestinal perforation.^45,47

The T-box family is another important transcription factor family in cardiac development. In addition to the link between TBX5 and Holt-Oram syndrome as well as TBX1 and 22q11.2 deletion syndrome,^48,49 TBX5 and TBX1 variants have been identified in non-syndromic CHD, e.g., TOF and cardiac septal defects.^50,51 Further, TBX20 pathologic variation has been associated with non-syndromic CHD, including septation defects, TOF, mitral valve stenosis, and dilated cardiomyopathy.⁵²

Cell signaling and adhesion molecules

The Notch signaling pathway is critical for multiple cellular processes during cardiac morphogenesis and is implicated in CHD. Pathologic variants in NOTCH1 were found to cause aortic valve disease, primarily BAV.⁵³ Subsequently, NOTCH1 variants have been identified in both non-syndromic and syndromic forms of CHD, i.e., Adams-Oliver Syndrome.^54,55 The subtypes of CHD have expanded to other forms of left-sided CHD including aortic stenosis (AS), coarctation of the aorta (CoA), and HLHS as well as TOF and other right-sided CHD.⁵⁶

Structural proteins

Cardiac sarcomeric and extracellular matrix proteins are essential for the normal architecture and function of cardiac muscle. Pathogenic variations in sarcomeric genes are known to cause cardiomyopathy; however, they are also implicated as an etiology of non-syndromic CHD. MYH6 (α-myosin heavy chain 6) variants have been described in familial ASD.⁵⁷ Variants in MYH7 (β-myosin heavy chain) have been reported in Ebstein’s anomaly and left ventricular noncompaction (LVNC).⁵⁸ MYH11 (myosin heavy chain 11) variants have been associated with familial thoracic aortic aneurysm and patent ductus arteriosus (PDA).^59,60

Recent advances in deciphering the genetic architecture of CHD

Next-generation sequencing (NGS) in large CHD cohorts

Remarkable advancements in NGS technologies over the past decade have led to an improved understanding of the complex genetic architecture of CHD. The multi-institutional effort led by the NIH/NHLBI-funded Pediatric Cardiac Genomics Consortium (PCGC) involved the ES of 2871 CHD probands (among which were 2645 trios). These studies found damaging rare transmitted variants and de novo variants (DNVs) in 8% of sporadic cases of CHD, specifically 28% of syndromic and 3% of non-syndromic CHD.⁶¹ A gene-burden analysis of 2391 CHD trios uncovered that cilia-related genes are enriched for rare, damaging recessive variants but relatively less enriched for damaging DNVs.⁶² Furthermore, a large ES study of 1891 probands found enrichment of loss-of-function DNVs in syndromic CHD while incompletely penetrant inherited protein-truncating variants were enriched in non-syndromic CHD.¹² It is postulated that the incomplete penetrance may contribute to heterogeneity of phenotypes and potentially an oligogenic etiology for CHD.⁶³

Using NGS approaches, large patient cohort studies have identified new genes involved in the etiology of CHD. Variants in FLT4, which encodes vascular endothelial growth factor receptor 3 (VEGFR3), have been identified in patients with TOF by ES of large cohorts.⁶¹ In addition to FLT4, variants in KDR, encoding the vascular endothelial growth factor receptor 2 (VEGFR2), were identified in TOF cohorts and a familial case of TOF.^64,65 A large ES study by PCGC identified MYH6 variants in patients with Shone complex and left ventricular dysfunction.⁶¹ RBFOX2, which encodes an RNA-binding protein that regulates alternative splicing, has been implicated as a new etiologic gene for CHD.⁶⁶ Patients with HLHS have been found to harbor damaging DNVs in RBFOX2 in these large cohort studies.^61,67 Significant enrichment of damaging DNVs in KMT2D, encoding the histone-lysine N-methyltransferase 2D enzyme, known to cause Kabuki syndrome have been found in CHD cases.^61,67,68 Recently, a pathogenic in TMEM260, which encodes a transmembrane protein, has been identified in a Japanese family with PTA by ES.⁶⁹

Genome sequencing

Whole genome sequencing (WGS) sequences the entire genome and can identify single-nucleotide variants, insertions, deletions, CNVs as well as microRNAs, non-coding RNAs, and promoter and regulatory genes. A recent study of WGS in 231 individuals with CHD, most with TOF, demonstrated a significant truncating variant burden for FLT4.⁷⁰ Several WGS studies identified MYH6 variants in patients with HLHS.^71,72 WGS of 100 isolated TGA cohorts revealed significantly more sequence variation in CHD genes, but no clinically relevant variant was identified suggesting an oligogenic or polygenic inheritance of TGA.⁷³

Challenges and advances with variant interpretation in CHD

Whereas NGS technologies have allowed for identifying variants associated with CHD, there are some challenges that limit its clinical implementation. First, variant interpretation and establishing the causality of identified variants remain challenging. Next, determining the biological significance and impact of noncoding variants is still a significant challenge.

Variant prioritization and interpretation

Despite the advances in genomic sequencing technology, the discovery of pathologic genetic variation in non-syndromic CHD has been difficult. Initially, ES combined with candidate CHD gene prioritization along with bioinformatics pipelines and filtering strategies identified pathogenic variants in familial non-syndromic CHD.^59,74 In these studies, variants were filtered using an in silico bioinformatics approach and prioritized according to computational prediction programs, mode of inheritance, minor allele frequency, and presence in public databases including dbSNP (Single Nucleotide Polymorphism Database), NHLBI Exome Sequencing Projects (ESP) (https://evs.gs.washington.edu/EVS/), 1000 Genomes (https://www.internationalgenome.org/), the Exome Aggregation Consortium (ExAC) and the Genome Aggregation Database (gnomAD) (https://gnomad.broadinstitute.org/). Classification was then performed based on the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) guidelines.⁷⁵ Since the release of these guidelines, several online tools and repositories have been developed for classification and interpretation of genetic variants, including Clinvar (https://www.ncbi.nlm.nih.gov/clinvar/), National Institutes of Health-funded Clinical Genome Resource (ClinGen) (https://clinicalgenome.org/working-groups/sequence-variant-interpretation/), Franklin (https://franklin.genoox.com/clinical-db/home), and Varsome (https://varsome.com/). Subsequently, many variant prioritization methods have been described, and widely accepted variant prioritization to rank potential candidate variants are based on the combined evidence of the predicted deleterious effect of the variant on protein function, accumulated mutational damage in the harboring genes, and the biological association with known CHD-causing genes.^76–78 We recently reported a multi-omics approach using transcriptomic datasets generated from the tissues from mouse models of CHD for the prioritization of disease-contributing genes in patients with CHD.⁷⁹ More recently, an integrated proteomics and human genetics approach was leveraged to develop a variant prioritization score to identify and prioritize potential disease-causing genes harboring variants associated with CHD.⁸⁰

Single-cell transcriptomics data have demonstrated the expression of genes associated with CHD in specific cell types with the advent of single-cell RNA sequencing (scRNA-seq) technology. Cardiac cell atlases at various stages of cardiac morphogenesis were generated by scRNA-seq and profiled the anatomical locations in the developing heart.⁸¹ In addition, scRNA-seq has been utilized to define the molecular pathways regulating the emergence and segregation of the earliest cardiac lineages during cardiac development.⁸² A network-based computational method for scRNA-seq analysis has defined the mechanisms by which a relatively small population of cells are impacted during early heart development and how regulatory defects in these discrete cell subsets can lead to morphologic abnormalities in the heart.⁸³ These approaches with single-cell genomics can be utilized to refine pipelines for variant prioritization by filtering novel candidate genes along with confirmation of expression from scRNA-seq datasets at the single-cell level to improve pathogenic variant identification and interpretation.

Noncoding genetic variation

WGS captures both the coding and noncoding regions of the genome, allowing for the discoveries of both the number of CNVs and single nucleotide variations (SNVs) in noncoding regions and potentially regulatory regions of the genome. However, determining a link between noncoding genetic variation and CHD remains difficult. WGS combined with RNA-seq identified candidate noncoding genetic variants in complex genetic diseases including CHD.⁶⁶ A study using WGS demonstrated an enrichment in damaging DNVs in noncoding regions in CHD trios compared to controls.⁸⁴ A recent study developed a scalable system to measure the effect of noncoding DNVs on cis-regulatory element and to systematically assess the contribution of noncoding DNVs to CHD.⁸⁵ Rare noncoding variants have been shown to be associated with genes controlling cardiac development with substantial risk for CHD suggesting that the oligogenic basis and significant heritability of CHD linked to rare variants outside the protein coding regions.⁸⁶ Future studies are warranted to establish the transcriptional and post-transcriptional regulatory effects of noncoding variants on the genetic etiology of CHD.

Clinical implications of recent genetic advances

As genetic testing becomes more widely available, its utility extends beyond diagnosis to include prognostication, decision-making, transplant evaluation and listing, and surveillance of other organs. It also enables familial cascade testing, allowing for early identification and management of at-risk relatives. With improved knowledge and deeper understanding of molecular mechanisms, we have entered into an era where therapies are being designed to target specific pathogenic variation. The integration of genomic technologies has enhanced our understanding of the underlying genetic mechanisms of certain conditions, leading to more precise and personalized therapeutic approaches.

Multiple studies in pediatric cardiology have identified syndromes as high-risk for worse outcomes, though these finding often lack specificity regarding the exact syndrome or genetic variation involved.^87,88 Recent advancements in genomic sequencing have revealed genetic causes for critically ill infants and rapid genome sequencing (rGS) has been recognized to be associated with improvements in clinical management.⁸⁹ rGS provided timely actionable information that increased the rate of diagnosis and decreased the cost of care.⁹⁰ Genetic disorders have been identified in 62% of cases with complex CHD by using rGS, leading to changes in critical management.⁹¹

Progress in genomic technologies have also influenced the clinical management of CHD, through precision medicine and implementation science. By leveraging genomic data, clinicians and researchers are beginning to explore how specific genetic variants may predispose patients to adverse outcomes or, conversely, confer resilience in the post-operative period. These insights are critical for tailoring perioperative care strategies and refining risk assessments. For example, the PCGC has played a key role in identifying broad genetic variants associated with worse post-operative outcomes and advocating for evidence-based genomic testing protocols in routine pediatric cardiac practice.⁹² Another study from this group highlighted a higher incidence of cancer in CHD with certain genotypes, underscoring the need for long-term surveillance in the CHD population and the need for development of intervention strategies.⁹³

Advances in genomic understanding that have paved the way for targeted therapies in pediatric cardiology. For instance, Noonan syndrome, often associated with hypertrophic cardiomyopathy (HCM), has been linked to mutations in RAS/MAPK pathway. MEK inhibitors, such as trametinib, has shown promise in treating HCM in patients with Noonan syndrome, indicating a potential for reversing cardiac hypertrophy, even in the later stages of disease.^94,95 Similarly, Costello syndrome, another RASopathy linked to severe HCM, when utilizing MEK inhibition with trametinib has been reported to improve cardiomyopathy symptoms in affected children, further highlighting the therapeutic potential of targeting specific genetic pathways.⁹⁶

Genetic testing also plays a pivotal role in guiding reproductive counseling and family planning. Identifying pathogenic variants not only provides clarity for families, but also informs decisions regarding future pregnancies such as the use of preimplantation genetic testing and prenatal diagnostics. Additionally, the discovery of novel gene-disease associations has deepened our understanding of CHD pathogenesis, paving the way for innovative therapeutic approaches targeting specific molecular pathways.

The next frontier in this field involves identifying additional genetic associations with critical outcomes, such as impaired wound healing, heightened inflammatory responses, or altered pharmacologic profiles to guide personalized management plans. The integration of genetic data into multidisciplinary care teams may enable more precise predictions of postoperative complications including arrhythmias, thromboembolic events, or organ dysfunction, ultimately improving surgical outcomes and quality of life for patients.

As genomic datasets grow in complexity and scale, artificial intelligence (AI) or machine learning (ML) is poised to play a potentially transformative role in advancing out understanding of genetic architecture underlying CHD and assist in defining the role of genetics in medical management. The AI-based approach to predict pathogenicity of missense variants, AlphaMissense, represents an important first step in harnessing genomic information from WGS.⁹⁷ AI-driven tools can also enhance the efficiency of genomic analysis by automating variant interpretation and prioritizing clinically actionable findings.^98,99 Machine learning algorithms and predictive modeling can help identify patterns and relationships within genomic data that may not be apparent through traditional analytical methods, as seen in other areas of cardiovascular medicine.¹⁰⁰ By integrating genomic, clinical and post-operative data, AI has the potential to uncover novel biomarkers, stratify patients risk more effectively, and predict outcomes with greater accuracy.

Future directions

Looking ahead, in addition to continued medical, interventional and surgical advancements, the future of CHD care will be shaped by the connection of genomic discoveries, technological innovations, and collaborative research efforts. As the field progresses, there is a growing need to move beyond genetic discovery to the functional characterization of identified variants, bridging the gap between genotype and phenotype. Understanding how genetic variation influences cellular and molecular pathways will unlock new therapeutic targets and enable the development of precision interventions tailored to individual patients. Importantly, the ethical and equitable implementation of these technologies will be critical to ensuring that the benefits of genomic advances reach all patients, irrespective of socioeconomic or geographic barriers.

Ultimately, the vision for the future is one of personalized, data-driven genomic care that empowers clinicians and patients alike. By fostering interdisciplinary collaboration, investing in infrastructure and prioritizing education and advocacy for genomic research and genetic testing implementation, the field of CHD is poised to make significant strides toward improving outcomes and quality of life for affected individuals and their families. This journey will require a commitment to innovation, equity and the seamless integration of genomic science into clinical care, ensuring a brighter future for all those impacted by CHD.

Synopsis:

Congenital heart disease (CHD) is a common type of birth defect and a leading cause of infant and childhood mortality. Although recent advancements in genetic technologies have allowed for the discovery of new genomic variation associated with CHD, the prioritization and interpretation of variants for pathogenicity remain a challenge. Further, the underlying molecular genetic mechanisms for CHD remain incompletely defined. In this review, we summarize established genetic etiologies of CHD and highlight advances in our knowledge of the underlying genetic architecture of CHD along with challenges in genetic variation interpretation. We also discuss the clinical implications of these genomic advances and future directions.

Key Points:

• This review describes established etiologic contributors to congenital heart disease (CHD) along with new genetic etiologies discovered using next-generation sequencing technologies.

• We highlight advances and challenges with the prioritization and interpretation of genetic variation, including the understanding of noncoding genetic variation.

• Furthermore, we discuss the clinical implications of recent genetic findings to translate genomic discoveries to pharmacotherapeutic development with goals to improve clinical outcomes in CHD patients.