Genetics of Sudden Cardiac Arrest: Overview of Genetic Risk Factors and Aetiologies

Sudden cardiac death (SCD) is a leading cause of death worldwide, accounting for 15–20% of all deaths and 50% of deaths from cardiovascular disease.¹ SCD is defined as any unexpected death from a presumed cardiac cause occurring within 1 hour of symptom onset if witnessed, or within 24 hours of being seen alive if unwitnessed.² SCD most often results from ventricular tachyarrhythmias, such as VF.¹ Coronary artery disease (CAD) is the predominant cause of SCD in the older and general population, responsible for 75–80% of SCDs (Figure 1 ).^2–4

In contrast, inherited cardiac disorders such as cardiomyopathies, in which arrhythmogenic substrates consist of structural abnormalities of the myocardium, and channelopathies, in which arrhythmogenic substrates arise from abnormal electrical properties in structurally normal hearts, are more common in young individuals under the age of 35 years.^2,5,6

Identifying underlying risk factors and aetiologies of SCD can be challenging. A large proportion of SCD victims are not known to have cardiac disease, and SCD may be the first clinical manifestation of cardiac disease.⁷ Around 30–40% of SCDs occur in structurally normal hearts, thus remaining unexplained after autopsy.⁸ Determining the genetic basis of SCD has important implications because this may help to elucidate the cause of SCD in victims and guide genetic evaluation of surviving relatives for SCD prevention.² Furthermore, identifying common genetic variants in cardiac disorders with complex inheritance may eventually enable risk prediction in the general population, lead to further studies aimed at improving our understanding of novel disease mechanisms, and promote preventative and therapeutic strategies in SCD.

Over the last three decades, significant progress has been made in understanding the genetic architecture of cardiac disorders associated with SCD.⁹ Inherited cardiomyopathies and channelopathies have traditionally been considered monogenic disorders with Mendelian patterns of inheritance. However, increasing evidence, particularly from genome-wide association studies (GWASs), has suggested that many of these disorders have a complex genetic basis with contributions from multiple genetic variants (polygenic inheritance) and environmental factors (e.g. obesity, diabetes, drug overdose, heat stroke).^9–13

This review provides an overview of the genetic profile of common cardiac disorders related to SCD in old and young patients, specifically CAD and inherited cardiomyopathies and channelopathies, respectively.

Genetic Basis of Cardiac Disorders

Genetic Susceptibility to Sudden Cardiac Death

Family history of SCD has been recognised as a strong predictor of SCD.^14–17 In the Paris Prospective Study I, a long-term cohort study of 7,746 middle-aged French men, the relative risk of SCD was 1.89 if an individual had a parental history of SCD, increasing to 9.44 if both parents had SCD.¹⁴ This was substantiated by a case–control study in Seattle that showed that parental history of early-onset SCD (age <65 years) was independently associated with a 2.7-fold increased risk of sudden cardiac arrest (SCA), after adjusting for parental history of MI and other risk factors.¹⁵ Two subsequent case–control studies of MI patients from Netherlands and Finland reported that a family history of SCD in first-degree relatives was an independent risk factor for VF and SCD, respectively.^16,17 Collectively, these findings suggest that heritable factors contribute to SCD risk.

Monogenic Disorders: Mendelian Inheritance Patterns

Increasing evidence has supported contributions of monogenic and polygenic risk in many genetic cardiac disorders. Genetic variants in monogenic and polygenic disorders have different population frequencies and effect sizes (Figure 2).¹² Monogenic disorders arise from genetic variants in a single gene and follow Mendelian patterns of inheritance.¹² Such variants are rare in the general population (minor allele frequency <1%), but have large effect sizes and thus confer a large risk of disease.^18,19 Populations with rare genetic variants comprise only a small proportion of the overall incidence of SCD.¹³

Clinical heterogeneity is a feature of many monogenic disorders, in which a genetic variant may not necessarily lead to a disease phenotype. This is evident in incomplete penetrance, in which not all individuals carrying a particular genetic variant express the disease phenotype. Additionally, there may be variable expressivity, in which affected individuals have different disease severity. Incomplete penetrance and variable expressivity can be influenced by an interplay of genetic modifiers, environmental and lifestyle factors.¹²

Polygenic Disorders: Complex Inheritance Patterns

In contrast, polygenic disorders display complex inheritance, involving co-inheritance of multiple common genetic variants (minor allele frequency ≥1%). While common variants individually have small effect sizes, they can collectively be responsible for significant cardiac disease risk.^7,18,19

Since the completion of the Human Genome Project in 2003 and launch of the International Haplotype Map Project in 2002, an increasing number of GWASs have been performed.^20,21 GWASs have uncovered common genetic variants contributing to the susceptibility of inherited cardiac disorders, elucidating potential novel mechanisms underlying cardiac diseases. These studies evaluate the entire genome, comparing genetic sequences of affected individuals and controls to detect regions of common variants referred to as single-nucleotide polymorphisms (SNPs).¹² GWASs may be useful for identifying variations potentially explaining variability in penetrance, and the occurrence of genotype-negative cases.

Genetic Risk Factors Associated with Sudden Cardiac Death

CAD is the most common cause of SCD in older populations, accounting for half of all SCD cases in the fourth decade. In individuals aged under 35 years, a significant proportion of SCD cases are related to inherited cardiomyopathies and channelopathies (Figure 1).^2–5 Heritable factors contribute to SCD risk, possibly related to shared genes increasing the susceptibility to SCD. Increased emphasis has been placed on interactions between disease-causing mutations and SNPs influencing the risk of developing diseases.²² This section summarises genetic studies, variants and potential mechanisms of cardiac disorders associated with SCD, namely CAD, inheritable cardiomyopathies and channelopathies (Table 1 and Supplementary Tables 1 and 2).

Coronary Artery Disease

Genetic Basis of Coronary Artery Disease

CAD is a complex disease arising from genetic, environmental and lifestyle factors. Family history of CAD/MI is an important risk factor for CAD, and heritability of CAD is around 40–60% based on family and twin studies.²³ In the Framingham Offspring Study, parental history of premature CAD independently predicted future offspring events of CAD after adjusting for traditional risk factors.²⁴ Pre-existing CAD also conferred a 2.8–3.3-fold increased long-term risk and a 1.9–5.3-fold increased short-term risk of SCD in the Framingham heart study.²⁵ Traditional risk factors (e.g. older age, male sex, hypertension, hypercholesterolaemia, diabetes, obesity and smoking) have been associated with an increased risk of CAD and SCD, but lack specificity for predicting the risk of SCD.⁷ Monogenic and polygenic factors contribute to CAD risk. Pathogenic variants in genes associated with lipid and triglyceride metabolism (e.g. ANGPTL4, APOA5, APOC3, LDLR, LPA, LPL, PCSK9) have been associated with CAD risk (Supplementary Table 1).^23,26

Genetic Variants for Sudden Cardiac Death in Coronary Artery Disease

Several studies have identified genetic associations for SCD in CAD (Supplementary Table 2).^27–29 The AGNES study, a GWAS of first acute MI patients with or without VF, showed that an SNP (rs2824292) on the 21q21 locus near CXADR was an independent risk factor for VF.²⁷ CXADR encodes for Coxsackie virus and adenovirus receptor (CAR), a cell adhesion molecule in intercalated discs implicated in myocarditis, dilated cardiomyopathy and defects in atrioventricular conduction. CAR knockout mice developed progressive atrioventricular block due to loss of expression of the gap junction protein, connexin 45. Loss of CAR also led to reduced expression of connexin 43, the most abundantly expressed gap junction protein in ventricular myocardium, suggesting that CAR may be involved in ventricular conduction.³⁰ CAR-deficient mice had slowing of ventricular conduction and earlier onset of inducible ventricular arrhythmias in acute myocardial ischaemia, with proposed mechanisms including reduced membrane excitability and electrical uncoupling during ischaemia, providing substrates for re-entrant ventricular arrhythmias.³¹ In contrast, an association between 21q21 and VF was not detected in a small German case–control study.³² Potential explanations for these discrepancies include a lack of DNA and phenotype data for VF and SCD, as well as small population size, thus limiting statistical power and the ability to replicate these findings.

A subsequent meta-analysis of five European GWASs identified a genome-wide significant signal on the 2q24.2 locus near BAZ2B (rs4665058), associated with an increased risk of SCD (Supplementary Table 2). Although the 2q24.2 locus contains three genes (BAZ2B, WDSUB1 and TANC1) expressed in the heart, the causal gene at this locus and its mechanisms in arrhythmia susceptibility is unknown.³³ However, chromosome 2q24 is known to encode sodium channel genes, and 2q24 deletion has been associated with congenital heart defects, epilepsy and developmental delays.³⁴ In contrast, no effect for rs4665058 was seen in both the AGNES study (which involved a narrower phenotype of only first MI patients) and in a small Han Chinese GWAS (suggesting that genetic variants may confer different effects in different ethnic populations).^33,35 Instead, the Chinese GWAS identified multiple SNPs associated with SCD with or without CAD ( Supplementary Table 2).³⁵

In another GWAS, an SNP (rs3864180) on GPC5, a cell surface heparan sulphate proteoglycan (HSPG), conferred protection against SCA in CAD.²⁸ Cell surfaces and extracellular matrices in the heart express a large amount of HSPGs, which play a role in the regulation of angiogenesis and vasculogenesis after ischaemic injury. However, the role of HSPG in SCA/CAD is unknown, and defects in GPC5 have not previously been associated with disease.²⁸ A subsequent GWAS from the United States demonstrated that 14 SNPs on seven genes (ACYP2, AP1G2, ESR1, DEGS2, GRIA1, KCTD1, ZNF385B) were associated with increased SCA risk due to ventricular tachycardia (VT) and/or VF in CAD patients.²⁹ Several of these SNPs (ESR1: rs2982694 and rs12429889; KCTD1: rs16942421) were also identified in a Chinese GWAS.³⁵ While there have been limited data regarding the mechanisms of most of these genes in cardiovascular disease, ESR1 is known to regulate the expression of multiple genes after activation by oestrogen in cardiovascular disease, and has been associated with increased CAD risk.²⁹

In a candidate gene association study, six SNPs (in or near CASQ2, GPD1L and NOS1AP) were associated with increased SCD risk in CAD. CASQ2 and GPD1L have previously been reported in arrhythmia syndromes, while NOS1AP is associated with QT prolongation and SCD risk.³⁶ The underlying mechanisms for NOS1AP in SCD are unclear. Mutations in CASQ2, encoding the intra-sarcoplasmic reticulum calcium-binding protein calsequestrin 2, have been associated with catecholaminergic polymorphic VT (CPVT) due to increased release of calcium from sarcoplasmic reticulum. GPD1L may regulate cardiac sodium channel current and has been implicated in Brugada syndrome (BrS).^36,37 Hence, there may be genetic overlap between rare and common forms of SCD.³⁶ While these genetic variants provide insight into potential genetic risk factors for SCA/SCD, further research is needed to elucidate the causal mechanisms underlying these associations and validate these findings.

Polygenic Risk Scores for Coronary Artery Disease

Polygenic risk scores (PRS) for CAD have been validated in multiple population-based cohort studies, mostly involving individuals of European ancestry. Khera et al. developed a PRS for CAD, derived from large GWASs of mainly individuals of European ancestry, involving 6.6 million variants and predicting CAD occurrence.¹⁸ That PRS identified 20-fold more individuals at similar or higher risk of CAD, when compared with individuals with familial hypercholesterolaemia mutations in prior studies. Furthermore, a high PRS predicted sudden arrhythmic death in CAD patients without severe systolic dysfunction.¹⁸ A subsequent study by Sandhu et al., involving participants from the PRE-DETERMINE study of European ancestry, generated a PRS for CAD without severe systolic dysfunction that predicted sudden and/or arrhythmic death.³⁸ More recently, an East Asian study of Chinese CAD patients found that a PRS for CAD and seven risk factors (MI, ischaemic stroke, angina, heart failure (HF), total cholesterol, LDL cholesterol and C-reactive protein) predicted all-cause death.³⁹ PRSs may improve the risk assessment for CAD in combination with clinical risk factors.³⁹ However, future studies should further validate and assess the clinical utility of PRSs for CAD, especially in more diverse ethnic populations.

Inherited Channelopathies

Around 5% of SCDs occur in patients without structural heart disease or CAD, and are often attributed to channelopathies including long QT syndrome (LQTS), BrS and CPVT.⁴⁰ Although these disorders have traditionally been considered monogenic, genetic modifiers (e.g. SNPs) and modulators (e.g. age, sex, heart rate, medication use) can contribute to disease variability.⁴¹

Long QT Syndrome

Congenital LQTS is an inherited channelopathy characterised by QT prolongation, predisposing to torsade de pointes (TdP), VF and SCD.¹² There are two forms of disease: a more common autosomal dominant (AD) form, and a less common autosomal recessive form associated with deafness.⁹ Rare pathologic variants involving KCNQ1, KCNH2 and SCN5A account for most cases (70–75%).^37,42 In LQTS, the risk of SCD can be influenced by the underlying gene involved. The incidence of first cardiac event before the age of 40 years was found to be lower (30%) in individuals with KCNQ1 mutations (LQT1, 30%) than in those with KCNH2 mutations (LQT2, 46%).⁴³ Hence, awareness of genotype may provide information regarding clinical course and enable risk stratification.

Prolonged QT interval is also an established risk factor for SCD in the general population.⁷ Common genetic variants affecting QT interval duration in the general population have been identified in GWASs (summarised in Supplementary Table 2). In 2006, the first GWAS for QT interval identified a common genetic variant in NOS1AP modulating the QT interval in the general population, explaining up to 1.5% of variability in QT interval duration.⁴⁴ Multiple studies from Europe and the US subsequently found that variants in NOS1AP were associated with QT prolongation and increased SCD risk.^44–46 More specifically, similar variants (rs10494366 in German and Australian/New Zealand studies; rs12143842 in European, Australian/New Zealand and international studies) have been identified in multiple studies worldwide.^44,47–50 NOS1AP, encoding the nitric oxide synthase 1 adaptor protein, is a regulator of neuronal nitric oxide synthase that modulates cardiac repolarisation. These associations suggest a possible role for the nitric oxide synthase pathway in myocardial function, although further studies are needed to explain the underlying role of NOS1AP in influencing QT interval and SCD risk.¹¹

Common variants alone may be inadequate to cause SCD, but may increase the risk of SCD by reducing repolarisation reserve. Given that repolarisation relies on multiple redundant mechanisms, a genetic variant causing minor alterations to repolarisation reserve may not be clinically apparent until further losses of repolarising capacity lead to an arrhythmic substrate. Interactions between common variants and risk factors that reduce repolarisation capacity, including hypokalaemia, ischaemia and QT-prolonging drugs, could collectively increase the heterogeneity of ventricular repolarisation and subsequently increase susceptibility to VF/SCD.⁴⁰

Three GWASs (QTSCD, QTGEN and QT-IGC) reported strong associations between SNPs and QT interval in European populations.^46,47,51 Findings from these studies were incorporated into a PRS of 61 common genetic variants for resting QTc interval, which was significantly associated with the risk of drug-induced QT prolongation and TdP.⁵² Recently, a trans-ethnic GWAS of LQTS patients of European and Japanese ancestries found that around 50% of variance in QT interval susceptibility was due to common genetic variants.⁵⁰ A meta-analysis of three GWASs demonstrated that nine SNPs at five candidate genes (NOS1AP, KCNQ1, KCNE1, KCNH2, SCN5A) and five SNPs at loci not previously recognised to modulate myocardial repolarisation explained 2.3–6.5% of QT interval variation.⁴⁷ Furthermore, a candidate gene study identified a common variant in KCNE1 D85N (rs1805128), which increased the risk of TdP in association with QT-prolonging drugs.⁵³ In another study, patients with variants of KCNQ1’s 3′ untranslated region (rs2519184, rs8234 and rs10798), on the same allele as the LQTS-associated KCNQ1 mutation, had a less severe disease phenotype compared with those with variants on the other allele.⁵⁴ Therefore, these findings highlight the role of genetic modifiers in QT interval duration.

Brugada Syndrome

BrS is an inherited arrhythmia involving right precordial ST-segment elevation, right bundle-branch block, and increased risk of ventricular arrhythmia. The underlying pathophysiological mechanisms of BrS have been debated for decades, with two hypotheses involving abnormal repolarisation and depolarisation. The repolarisation hypothesis, based on a study using a canine wedge preparation, views transmural dispersion of repolarisation as a substrate for re-entry-mediated arrhythmias.⁵ The depolarisation hypothesis considers conduction delays, especially across the right ventricle, as arrhythmogenic factors that increase susceptibility to re-entry arrhythmias.⁵

BrS typically has an AD pattern of inheritance and is most often caused by pathogenic variants in SCN5A (responsible for 20–25% cases).³⁷ Pathogenic variants in SCN5A have also been reported in LQTS, dilated cardiomyopathy, sinus node dysfunction and atrial standstill.¹³ In BrS, SCN5A mutations impair trafficking of channel to cardiomyocyte membrane or alter channel biophysical properties, resulting in decreased inward sodium current.⁵ Prior genotype–phenotype studies in large families with pathogenic SCN5A variants have shown incomplete or low disease penetrance, and many cases are sporadic. Phenotype-positive but genotype-negative patients have also been reported.¹⁰ Hence, these suggest that rare genetic variants alone may not be sufficient to cause the disorder.

Increasing evidence has supported a complex polygenic model in BrS.¹⁰ GWASs have identified genetic loci harbouring common variants associated with BrS (Supplementary Table 2). In the absence of rare SCN5A variants, BrS is largely polygenic.¹² An international GWAS identified three SNPs, in SCN5A (rs11708996), SCN10A (rs10428132) and near HEY2 (rs9388451), associated with BrS (mechanisms listed in Supplementary Table 1). Notably, disease risk increased with an increasing number of risk alleles, with an estimated odds ratio of 21.5 for more than four risk alleles (versus fewer than two).⁵⁵ These findings were supported by a recent Japanese study that found that SNPs at SCN5A/SCN10A and HEY2 loci, along with a novel SNP near ZSCAN20 (rs2336244), were associated with BrS.⁵⁶ A non-synonymous variant (S1103Y) in SCN5A has also been associated with an increased risk of ventricular arrhythmias and SCD in African-American individuals.⁵⁷ Furthermore, a PRS for BrS, comprising the aforementioned variants (rs11708996, rs10428132, rs9388451), was independently associated with a type I BrS ECG in response to ajmaline provocation drug testing.⁵⁸

Another Japanese study confirmed the association between SCN10A (rs10428132) and HEY2 (rs9388451) with BrS, but found no relationship with the previously reported SCN5A (rs11708996).⁵⁹ Notably, the HEY2 C allele was more common in BrS patients without VF than in those with VF, suggesting potential protective effects against VF. Given that the exact mechanism for these associations is unclear, follow-up studies on gene function and expression are needed.⁵⁹

Catecholaminergic Polymorphic Ventricular Tachycardia

CPVT is a rare inherited arrhythmia syndrome characterised by polymorphic VT, often triggered by vigorous physical exertion or emotional stress. CPVT is usually inherited in an AD pattern, although AR inheritance is possible.³⁷ There is a high frequency of sporadic de novo variants, especially in RYR2, the most commonly implicated gene. RYR2 encodes for cardiac ryanodine receptor, a calcium release channel mediating the release of calcium from the sarcoplasmic reticulum into cytosol, thus playing an important role in triggering cardiac myocyte contraction.¹² Mutations in RYR2 lead to increased release of calcium from sarcoplasmic reticulum, leading to delayed afterdepolarisations and triggering arrhythmias (Supplementary Table 1).⁴¹ More than 100 pathogenic variants have been identified in multiple genes (RYR2, CASQ2, KCNJ2, TRDN, CALM1 and CALM2), contributing to approximately 60% of all clinically diagnosed cases.⁴²

Currently, no GWASs have specifically assessed genetic variants in CPVT. However, a recent GWAS identified two SNPs on CCR7 (rs78960694 and rs2229095) associated with idiopathic VT (Supplementary Table 2). CCR7, encoding a G protein-coupled receptor, regulates expression of genes encoding for the voltage-gated calcium channel Cav1.2 (RYR2 and NOS1AP). Cav1.2 mediates the plateau phase of the cardiac action potential, and associated mutations can cause BrS and LQTS. RYR2 has been implicated in CPVT, while NOS1AP has been implicated in QT prolongation.⁶⁰ Given that many individuals with CPVT are variant negative, GWASs are needed to identify potential genetic variants that confer susceptibility to CPVT and SCD.

Inherited Cardiomyopathies

Inherited cardiomyopathies responsible for SCD consist of hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM) and arrhythmogenic cardiomyopathy (ACM).¹² Limited studies have assessed common genetic variants in cardiomyopathies in association with the risk of SCD.

Dilated Cardiomyopathy

DCM is the most common form of cardiomyopathy, characterised by left ventricular (LV) dilatation and systolic dysfunction in the absence of abnormal loading conditions.¹² Re-entry is a mechanism for sustained VT/VF in DCM, which is associated with myocardial fibrosis. Focal automaticity, stretch-induced arrhythmias and electrolyte disturbances may contribute to arrhythmogenesis in DCM.⁶¹ More than 40 genes (including BAG3, DES, DSP, FLNC, LMNA, MYH7, PLN, RBM20, SCN5A, TNNC1, TNNT2 and TTN) have been implicated in DCM, of which LMNA and TTN are the most common.¹² Genetic variants in LMNA have been associated with increased risk of life-threatening ventricular arrhythmias and SCD. LMNA encodes for nuclear matrix proteins lamin A and C, and its pathophysiological mechanisms in DCM and SCD remain unclear. However, genetic variants in LMNA may lead to changes in cellular signalling, nuclear fragility and abnormal interactions between lamines and other nuclear proteins.⁶² Additionally, carriers of pathogenic variants in LMNA, RBM20 and DSP have increased risk for HF and heart transplantation.¹²

Penetrance in AD DCM is age-dependent, therefore individuals carrying pathogenic variants are more likely to express the phenotype with increasing age.¹² In such carriers, long-term follow-up for early phenotype detection, and management and prevention of complications is important.¹² Environmental factors such as chemotherapy agents and antiviral therapies can trigger DCM, while other medications (e.g. antipsychotics, antidepressants, lithium, glucocorticoids, retinoids) have been reported to affect disease onset. These gene–environment interactions could contribute to incomplete penetrance in DCM.⁶³

Polygenic burden may contribute to risk of DCM in both genotype-positive and -negative cases.¹² Several European GWASs identified genetic variants associated with DCM (Supplementary Table 2). These genetic studies identified SNPs associated with DCM including 1p36.13 (rs10927875; also spans other genes including ZBZTB17 and HSPB7) and BAG3 (rs2234962), as well as the SNPs 3p25.1 (rs62232870; extends over six genes, of which TMEM43 and SLC6A6 have been expressed in the heart), and 22q11.23 (rs7284877).^64,65 Notably, rare mutations in BAG3 contributed to monogenic forms of DCM, while common genetic variants were associated with sporadic DCM.⁶⁴ Rare pathogenic variants in TMEM43 have previously been identified in arrhythmogenic right ventricular cardiomyopathy.⁶⁵ Recently, a small case–control study from Iran identified an SNP (rs505058) on LMNA associated with familial DCM.⁶⁶ Currently, data regarding common genetic variants in DCM and SCD risk in the general population are not available.⁴⁰

Hypertrophic Cardiomyopathy

HCM is one of the most common causes of SCD in young adults. This condition involves asymmetric or concentric increase in LV wall thickness, resulting in poor blood outflow due to reduced LV filling.⁹ Phenotypes and genotypes of HCM are heterogeneous, given that different genes and genetic variants can lead to different cardiac morphologies, disease severity and prognosis.⁶⁷ Genes associated with HCM include MYBPC3, MYH7, TNNI3, TNNT2, TPM1, MYL2, MYL3, ACTC1 and CSRP3. For individuals with clinical phenotypes of a syndromic disorder, other genes including GLA (Fabry’s disease), LAMP2 (Danon disease) and PRKAG2 (glycogen storage disease) may be involved.¹²

SCD risk for HCM patients may be influenced by underlying mutation. TNNT2 mutations have been associated with increased risk of ventricular arrhythmias and SCD despite causing minimal hypertrophy.⁶⁸ TNNT2 encodes for cardiac troponin T, a thin filament protein of striated muscle modulating muscle contraction in response to changes in intracellular calcium concentration. TNNT2 variant I79N has been shown to increase myofilament Ca²⁺ sensitivity and decrease Ca²⁺ transients arising from increased cytosolic Ca²⁺ buffering.⁶⁸ A ‘two-hit’ model may contribute to variability in HCM. Primary sarcomere mutations may increase susceptibility to secondary insult, promoting focal myocyte death and fibrosis. The severity of HCM may be modified by ‘second hit’ events (e.g. local haemodynamic factors, exercise, diet), resulting in worsening hypertrophy, myocyte disarray and fibrosis.⁶⁹

Common variants can also contribute to HCM risk, with sarcomere-negative HCM having a strong polygenic influence. A registry-based study of HCM patients from Australia and New Zealand found that two SNPs in NOS1AP (rs10494366 and rs12143842) were associated with prolonged QTc, but not with the risk of SCD in HCM.⁴⁹ A GWAS from the UK identified 13 SNPs associated with HCM (Supplementary Table 2).⁷⁰ More recently, Zheng et al. developed a PRS for HCM, predicting disease risk in sarcomere-positive carriers, risk of all-cause mortality, and major adverse cardiac events after HCM diagnosis.⁷¹

Arrhythmogenic Cardiomyopathy

ACM is characterised by progressive fibrofatty replacement of myocardium, which can lead to life-threatening ventricular arrhythmias.² ACM predominantly affects the right ventricle, but can have biventricular or LV involvement. ACM is usually inherited in an AD pattern with incomplete penetrance and variable expression. Associated genes can be broadly classified into desmosomal (e.g. DSP, DSC2, DSG2, JUP, PKP2) and non-desmosomal genes (e.g. DES, PLN, TMEM43), of which PKP2 is the most commonly involved.⁵ PKP2 encodes an important structural protein of desmosomes, and mutations may lead to decreased PKP2 protein at intercalated discs, thus disrupting desmosomes and gap junctions. Loss of PKP2 expression also results in reduced connexin 43, an integral gap junction protein, leading to impaired electrical coupling and conduction.⁷² Given that GWASs for ACM and SCD have been lacking, there has been limited understanding of common genetic variants in the general population. GWASs are needed to determine associations between genotype and phenotype, and to develop PRSs for SCD in ACM.

Genetic Profile of SCD and SCA Cohorts

The genetic profile of SCA and SCD in the general population is summarised in Table 1.^8,73–76 CAD was present in around one-third of patients with SCD,^73,74 while cardiomyopathies were more common than arrhythmias in the general population.⁸ In a post-mortem GWAS of 30 SCD cases by Beccacece et al., SNPs likely to be pathogenic for SCD were located in genes involved in cardiovascular disease (e.g. atherosclerosis, CAD, thrombosis) and metabolism (e.g. lipids, cholesterol, arachidonic acid, drugs), as well as located in genes previously associated with SCD (CACNA1C, KCND2, PRKAG2).⁷³ Common genes implicated in cardiomyopathies were LMNA, MYBPC3, MYH7 and TNNI3, while those implicated in arrhythmias or channelopathies were KCNQ1, SCN5A, PKP2 and KCNH2.^73,74

In contrast, another GWAS by Ashar et al. reported no common variants associated with SCA at a genome-wide significance level (in European-descent individuals), suggesting that common variants may not significantly contribute to SCA risk in the general population.⁷⁷ Instead, phenotypes associated with electrical instability (e.g. atrial fibrillation, QT interval) were associated with the risk of SCA.⁷⁷

Evaluation of Individuals with a Family History of SCD

Given that SCD has a strong genetic basis, evaluation of family members who may be at increased risk of SCD is crucial. The main aims of evaluation are to identify the cause of death for the victim, and to risk-stratify and guide preventative measures in surviving relatives.⁷⁸ Combining clinical and genetic evaluation may increase diagnostic yield.⁷⁹ Potential clinical implications of genetic testing in SCD include the identification of genetic risk factors for risk stratification; and the elucidation of novel mechanisms and aetiologies, leading to preventative and therapeutic strategies. A multidisciplinary approach, involving collaboration between cardiology sub-specialties, pathologists, geneticists, genetic counsellors and/or psychologists, is central to the assessment and management of victims and families with SCD.⁷⁹

Sudden Cardiac Death without an Identifiable Cause

The workup approach of relatives is guided by whether or not the cause of SCD was identified in the decedent. This leads to two possible situations: SCD without an identified cause; and SCD with clinical findings or genetic testing pointing towards a specific diagnosis.⁷⁹ If the cause of SCD is not identified, evaluation of first-degree family members may identify an underlying cause. Family screening of first-degree relatives is recommended in sudden unexplained death with negative autopsy (or no autopsy) if the victim was aged <45 years, and in all patients with clear phenotypes. In individuals with autopsy-negative SCD, potential causes include inherited channelopathies and latent cardiomyopathies. In particular, genetic testing should be performed if a pathogenic or probable pathogenic variant is identified in the victim.⁷⁹

Initial clinical evaluation should include a thorough medical and family history (with a family pedigree of at least three generations), physical examination, standard and high precordial ECG, echocardiogram and exercise testing. A family history of heart disease, syncope or sudden death should raise suspicion for a genetic cardiac disorder, while history of chest pain, prior CAD/MI or traditional risk factors may point towards underlying CAD.^79,80 Depending on the initial investigation findings, cardiac rhythm monitoring, cardiac MRI and pharmacological provocation tests may be considered to inform diagnosis.⁷⁸

Sudden Cardiac Death with an Identifiable Cause

Conventional autopsy is recommended in sudden unexplained death, and may identify structural causes of SCD, including CAD, inherited cardiomyopathies and congenital heart diseases.⁸⁰ Post-mortem genetic testing on blood or tissue samples, also referred to as molecular autopsy, is recommended to support clinical observations and facilitate cascade genetic testing. If a pathogenic or probable pathogenic variant consistent with phenotype is identified in the victim, then first-degree relatives should be offered genetic testing. Cascade testing is efficient and cost-effective for AD diseases, given that the first-degree relatives have a 50% likelihood of carrying the same pathogenic variant.^12,79 Genetic testing should be tailored to clinical diagnosis and phenotype, with guidelines recommending the use of targeted gene panels (e.g. for channelopathies, cardiomyopathies, familial thoracic aortic aneurysm, familial hypercholesterolaemia).⁷⁹

Differentiating between a family history of non-CAD- versus CAD-related SCD is important. Non-CAD-related SCD is more probable in young victims, often arising from inherited cardiomyopathies (e.g. HCM, DCM, ACM, non-compaction cardiomyopathy) and channelopathies (e.g. LQTS, BrS, CPVT). Given that many of these inherited cardiac disorders are monogenic, there is a role for genetic testing in the victims (post-mortem) and surviving relatives.⁸⁰ Guidelines for genetic testing of monogenic cardiac disorders and SCD have recently been published in the European Heart Rhythm Association/Heart Rhythm Society/Asia Pacific Heart Rhythm Society/Latin American Heart Rhythm Society (EHRA/HRS/APHRS/LAHRS) expert consensus statements.^12,79 In contrast, SCD related to CAD (e.g. coronary atherosclerosis, ischaemic cardiomyopathy) is more common in older individuals. Currently, genetic testing for CAD-related SCD has limited utility because atherosclerosis is a complex genetic disorder that is largely polygenic and strongly influenced by environmental factors.⁸⁰ Although PRSs for CAD have been developed, they have primarily been used for research purposes and their predictive utility in clinical practice remains unclear. Management has therefore been largely limited to identifying and aggressively managing traditional risk factors.¹²

Current Limitations and Future Directions

Limitations of Current Studies

A current limitation in genetic studies of SCD is small sample size, which leads to limited statistical power. Meta-analyses of GWASs have been performed to improve power by combining datasets. Most studies have also included VF and/or SCD of various aetiologies, which leads to increased heterogeneity and a reduction in statistical power for detecting the associated common genetic variants. Population stratification is an important confounder in GWASs, especially if studies involve different ancestry in cases and controls. This may lead to inaccurate results, arising from differences in the major allele frequencies of genetic variants in individuals of different ancestry.

Few SNPs have been replicated in current genetic studies. Potential explanations for the different polymorphisms in SCA and SCD, alongside the varying proportions of known genetic variants for CAD and inherited cardiac disorders, include genetic modifiers, autosomal random monoallelic expression (aRMAE), environmental factors and complex gene–environment interactions. Inheritance of genetic modifiers, which are genetic variants not directly causing the disease, can influence the expression or severity of the primary genetic defect. Genetic modifiers may increase or decrease the severity of the genetic defect or protect the carrier of the genetic defect from developing the disease.¹⁰ aRMAE, a form of random allele-specific expression in which gene transcription occurs from one of the two homologous alleles, may also cause phenotypic variability in disease.⁸¹ Additionally, environmental factors, such as age, pollution, radiation and certain foods, can modify the expression of SNPs.²²

Many studies have involved populations from the United States and Europe, with limited data from Asian and low- and middle-income countries. While geographical differences could contribute to different types and proportions of polymorphisms, few studies from the same countries identified the same SNPs. These findings suggest that such differences could also be on a population level, thus highlighting the need to characterise the genetics of SCD/SCA of local populations in order to be potentially useful to the respective populations. While the application of SNPs in clinical practice for risk stratification and precision medicine appears promising, it has not yet been established. Worldwide collaborations, involving different populations, are needed to increase population size, identify genetic variants and determine the clinical utility of and recalibrate PRSs for cardiac disorders associated with SCD in diverse populations. Before implementation in clinical practice, PRSs for SCD will need to demonstrate high discriminative capacity and clinical efficiency (e.g. improved accuracy when incorporated into clinical risk tools, identification of individuals with at least similar risk compared with monogenic risk variants).

Limitations of Genetic Testing and Understanding of Mechanisms

Currently, genetic screening for the primary prevention of SCD (phenotype-directed gene panel testing evaluating for cardiomyopathy and/or channelopathy) is performed in the presence of a family history of SCD (in first-degree relatives), inherited cardiac disorders or incidental findings and symptoms. A limitation of this approach is that it identifies only a small group of individuals with rare genetic variants,^2,13 which have not emerged as targets for predicting SCD risk. This underscores the need for identification of genetic variants with sufficiently large effects to be clinically useful for individual risk prediction in the general population.

In GWASs, SNPs may be present in coding or non-coding regions, and can be a functional allele associated with phenotype or in linkage disequilibrium (LD) with the functional allele. Given that GWAS is an indirect approach, these SNPs may not be disease-causing variants, but instead may be in strong LD with the respective diseases. Hence, GWASs do not provide information regarding specific genetic variants or the genes mediating the effect.¹³ The exact biological effects of associated SNPs are often unclear, but most are thought to be involved in gene modulation or transcript expression level.⁸² Importantly, genetic variants identified as associated with SCD may not necessarily be disease-causing variants. Currently, underlying mechanisms of genetic variants associated with ventricular arrhythmias and SCA/SCD remain largely unknown. Further studies should identify causal variants and potential novel mechanisms by which candidate genes and causal variants lead to disease phenotype and increase SCD risk, because this may elucidate possible targets and promote the development of preventative and therapeutic strategies.

Potential Role of Artificial Intelligence for Risk Stratification

Given the low survival rates associated with SCA, accurate prediction and prevention of such events are important. The role of artificial intelligence (AI) tools in predicting ventricular arrhythmias and SCA has been assessed in recent studies.^83–88 These studies have used machine learning models incorporating various input parameters, such as clinical history, vital signs, ECG (real-time remote monitoring in HF patients with implantable defibrillator/cardiac synchronisation devices; ambulatory ECG) and imaging (late gadolinium enhancement cardiac MRI in cardiomyopathies) to predict ventricular arrhythmias and/or cardiac arrest.^83,85–88 Overall, AI has shown promise for predicting ventricular arrhythmias and SCA in specific population groups and using certain types of input parameter. However, challenges that remain include low event rate, small sample size, lack of external validation and phenotypic accuracy of the population being studied.⁸⁴

With an increasing wealth of clinical, imaging, electrophysiological, biochemical and genetic data being electronically collated across health systems worldwide, AI will continue to have an increasing role in the identification of novel risk markers. To date, there remains an absence of published data on AI identifying genetic markers of SCA. Given that SCA is a clinically heterogeneous and complex condition with multiple substrates, triggers and varying genetic predisposition, further studies of AI algorithms, especially using genetic markers, may help to identify novel patterns and improve risk stratification in the future.⁸⁴

Conclusion

Inherited cardiomyopathies, channelopathies and CAD are important causes of SCD. While many inherited cardiac disorders have been considered monogenic, they may also have polygenic contributions. Significant advances in genetic analysis techniques, especially the use of GWASs, have enabled more efficient and comprehensive evaluation of genetic variants associated with inherited cardiac disorders and SCD. Identifying the genetic contribution to cardiac disorders associated with SCD has important implications, given that this may enable risk stratification, elucidate novel disease mechanisms and promote targeted therapy.

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