Issues and Challenges in Diagnostic Sequencing for Inherited Cardiac Conditions

Abstract

BACKGROUND

Inherited cardiac conditions are a relatively common group of Mendelian diseases associated with ill health and death, often in the young. Research into the genetic causes of these conditions has enabled confirmatory and predictive diagnostic sequencing to become an integral part of the clinical management of inherited cardiomyopathies, arrhythmias, aortopathies, and dyslipidemias.

CONTENT

Currently, the principle benefit of clinical genetic testing is the cascade screening of family members of patients with a pathogenic variant, enabling targeted follow up of presymptomatic genotype-positive individuals and discharge of genotype-negative individuals to health. For the affected proband, diagnostic sequencing can also be useful in discriminating inherited disease from alternative diagnoses, directing treatment, and for molecular autopsy in cases of sudden unexplained death. Advances in sequencing technology have expanded testing panels for inherited cardiac conditions and driven down costs, further improving the cost-effectiveness of genetic testing. However, this expanded testing requires great rigor in the identification of pathogenic variants, with domain-specific knowledge required for variant interpretation.

SUMMARY

Diagnostic sequencing has the potential to become an integral part of the clinical management of patients with inherited cardiac conditions. However, to move beyond just confirmatory and predictive testing, a much greater understanding is needed of the genetic basis of these conditions, the role of the environment, and the underlying disease mechanisms. With this additional information it is likely that genetic testing will increasingly be used for stratified and preventative strategies in the era of genomic medicine.

Inherited cardiac conditions (ICCs)⁶ comprise a broad range of syndromes affecting the heart and major blood vessels, including cardiomyopathies, arrhythmias, aortopathies, familial hypercholesterolemia (FH), and congenital structural heart defects. Collectively, these disorders are relatively common and are associated with adverse clinical outcomes such as sudden cardiac death (SCD), heart failure, and premature coronary heart disease (CHD). ICCs are typically monogenic (each case caused by a single variant in 1 gene) autosomal dominant conditions, but genetically and allelically heterogeneous (there are many genes in which variation can cause disease). ICC genes feature prominently in the American College of Medical Genetics and Genomics list of proposed genes to be routinely analyzed and interpreted in all exome or genome sequencing (1). Research into the genetic factors underlying ICCs has made substantial progress in recent years and has ensured that genetic testing for several conditions has made the transition from research to clinical diagnostic laboratories. However, considerable challenges in fully understanding the genetics of these diseases, and explaining how observed variation relates to clinical phenotype, will need to be addressed to maximize the benefit of diagnostic sequencing in ICCs.

Major ICCS

While ICCs comprise a wide range of conditions, from relatively common to extremely rare, this review focuses on some of the conditions for which the genetic etiology has been most studied, and therefore diagnostic sequencing most implemented, including cardiomyopathies, arrhythmias, and FH. The contribution of the main genes associated with these conditions are summarized in Table 1.

HYPERTROPHIC CARDIOMYOPATHY

Hypertrophic cardiomyopathy (HCM) is defined by the presence of primary left ventricular (LV) hypertrophy above a defined threshold in the absence of other cardiac or systemic conditions, and is characterized by myocyte disarray and myocardial fibrosis (2). It affects approximately 1 in 500 people and is the most frequent cause of sudden death in young people and athletes, although the majority of patients have a normal life expectancy. The genetic basis of HCM was first elucidated in the 1990s, with variants in sarcomeric genes identified through linkage studies in large family pedigrees (3). Although variants in over 50 genes have since been claimed to be associated with HCM, 8 sarcomeric genes still account for the vast majority of pathogenic variants, with expanded panels offering little increased clinical sensitivity (4).

DILATED CARDIOMYOPATHY

Dilated cardiomyopathy (DCM) is currently defined by the presence of left ventricular (LV) or biventricular dilation and systolic dysfunction in the absence of abnormal loading conditions (hypertension, valve disease) or coronary artery disease sufficient to cause global systolic impairment. The causes of DCM can be classified as genetic or nongenetic, but there are circumstances in which genetic predisposition interacts with extrinsic or environmental factors (5). DCM is a frequent cause of heart failure and the most common indication for heart transplantation and is estimated to affect at least 1 in 500 people. DCM is a genetically heterogeneous disease, although only titin (TTN)⁷ has a substantial (>5%) yield in genetic testing, with truncating variants (frameshift, nonsense, and essential splice site variants that disrupt the open reading frame) in this gene accounting for 15%–25% of patients with DCM (6, 7). Although DCM gene panels have expanded to incorporate novel gene associations, the yield of clinical testing still does not exceed 40% (8).

ARRHYTHMOGENIC RIGHT VENTRICULAR CARDIOMYOPATHY

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a progressive cardiomyopathy and is estimated to affect 1 in 1000–5000 people. It most frequently affects the right ventricular myocardium, although LV involvement may also be observed. Most variants associated with ARVC occur in genes encoding desmosomal proteins [plakophilin 2 (PKP2), desmocollin 2 (DSC2), desmoglein 2 (DSG2), desmoplakin (DSP), junction plakoglobin (JUP)] (9). Diagnostic testing in ARVC is hampered by the relatively high rate of rare missense variants in these genes in the general population, which limits interpretability and questions many published associations that were not adequately controlled (10, 11). However truncating variants in these genes are likely to be actionable, especially in PKP2 where they occur in approximately 25% of ARVC patients (12).

LONG QT SYNDROME

Long QT syndrome (LQTS) is an inherited arrhythmia that causes cardiac syncope and SCD. It is diagnosed by a characteristic QT prolongation on electrocardiogram and is estimated to affect 1 in 2500 people. It occurs more commonly in the autosomal dominant form (Romano-Ward syndrome) and rarely as the autosomal recessive Jervell and Lange-Nielsen syndrome (associated with sensorineural hearing loss). Variants in 3 ion channel genes account for over 70% of patients with a definitive diagnosis of LQTS (13, 14) [although the yield is substantially less for all referral cases (15)]: loss of function variants in the potassium ion channel genes potassium voltage-gated channel subfamily Q member 1 (KCNQ1) and potassium voltage-gated channel subfamily H member 2 (KCNH2; LQT1 and LQT2 respectively) and gain of function variants in the sodium ion channel gene sodium voltage-gated channel alpha α subunit 5 (SCN5A; LQT3). More than 10 other ion channels and associated proteins are more rarely associated with LQTS.

BRUGADA SYNDROME

Brugada syndrome is an inherited cardiac arrhythmia characterized by distinct type ST segment elevation on electrocardiogram examination and is associated with an increased risk of SCD in young adults. The prevalence of the disease varies significantly based on location and ethnicity and is highest in Southeast Asia, where it affects approximately 1 in 2000 people and is known in the local vernacular in Thailand as “Lai Tai” (death in sleep). Loss of function variants in SCN5A, both truncating and missense, occur in approximately 20% of Brugada patients (16). Variants in over 20 other genes have also been associated with the disease, but their prevalence is uncertain, with no large, broadly sequenced cohorts yet published.

CATECHOLAMINERGIC POLYMORPHIC VENTRICULAR TACHYCARDIA

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is an inherited cardiac arrhythmia characterized by polymorphic ventricular tachycardia induced by adrenergic stress and has an estimated prevalence of 1 in 10000 people. Missense variants in the cardiac ryanodine receptor encoding the ryanodine receptor 2 (RYR2) gene are detected in approximately 50% of CPVT probands (17–19). Although variant interpretation in this gene is complicated by the relatively high background frequency of rare variants, a tiered strategy has been proposed that relies on the clustering of pathogenic variants into 3 distinct clusters in RYR2 (19). A small number of CPVT cases have been associated with variants in the calsequestrin 2 (CASQ2; autosomal recessive), triadin (TRDN), calmodulin 1 (CALM1), and potassium voltage-gated channel subfamily J member 2 (KCNJ2) genes.

FAMILIAL HYPERCHOLESTEROLAEMIA

Familial hypercholesterolemia (FH) leads to premature CHD due to raised plasma levels of LDL cholesterol (LDL-C). It is the most common monogenic disorder, affecting up to 1 in 200 people, though most remain undiagnosed (20). Genetic testing for FH has a high yield, with variants in the low density lipoprotein receptor (LDLR) gene detected in approximately 80% of cases. The availability of highly effective statin therapy for patients with FH ensures that diagnosis of this disease through genetic testing in a proband, and follow on cascade screening of the family, can have substantial health benefits, which has led to national efforts in some countries.

Benefits of Genetic Testing

The practical benefits of incorporating genetic testing into the diagnostic screening process for ICCs encompass several factors.

CASCADE SCREENING

One of the main drivers for genetic testing in ICCs is to enable cascade screening of family members of the index patient. If a definitive pathogenic variant is identified in the proband, relatives can be genotyped to determine who is at risk of developing the disease, which may be beneficial for presymptomatic and younger family members. Genetic testing has twin advantages: (a) those without the causative variant can be discharged and exempted from regular follow-up clinical screening, offering both economic savings to healthcare systems and psychological benefits to individuals (lifelong surveillance is standard practice for many ICCs, especially cardiomyopathies, due to age-related penetrance and many conditions can never be confidently ruled out without a genetic test), and (b) those carrying the variant can be offered detailed clinical investigations, follow-up, and hopefully increasingly, prophylactic treatments. The latter is particularly critical for assessing the risk of SCD in patients with arrhythmias and cardiomyopathies and for employing statin therapy to treat CHD in young patients with pathogenic FH variants.

DIFFERENTIAL DIAGNOSIS AND THERAPY GUIDANCE

Currently, genetic testing of a clinically affected proband provides limited impact into clinical management of the proband. In most cases, the phenotype of the patient will be defined through clinical diagnostic approaches (such as imaging) that provide the main basis for therapeutic decisions. An exception to this is the diagnosis of metabolic cardiomyopathies, which can present with LV hypertrophy and therefore be initially diagnosed as HCM. Genetic testing in these cases can reveal pathogenic variants in the causative genes of the following conditions—Danon disease, Fabry disease, Wolff–Parkinson–White syndrome, and glycogen storage disease II/Pompe disease that can directly impact treatment options, e.g., enzyme replacement therapy for Fabry disease.

Detecting or confirming the subtype of LQTS through genetic testing can also play a role in guiding therapy through lifestyle modifications and drug treatment. Based on known triggering stimuli, patients with LQT1 are advised to avoid strenuous exercise (particularly swimming and competitive sports), patients with LQT2 should try to avoid unexpected auditory stimuli, whereas patients with LQT3 may be advised to be monitored or accompanied while sleeping (21). Treatment with β blockers such as atenolol or nadolol is recommended for all patients with LQTS but may be most effective for LQT1 (22), in particular in patients with specific classes of KCNQ1 variants (23). In contrast, LQT3 (caused by gain of function variants in SCN5A) may be treated with sodium channel blockers such as mexilitine, flecainide, or ranolazine (24).

RISK ASSESSMENT AND GENOTYPE–PHENOTYPE CORRELATION

Initial hopes that genetic testing for ICCs could be useful for risk assessment, i.e., prediction of the disease severity/outcomes and the specific therapy that warranted, have been confounded by the limited correlation between genotype and phenotype in many of these conditions. ICCs are generally characterized by variable expressivity and penetrance, even between family members carrying the same pathogenic variant, and there is currently sparse evidence that different classes of variants have different outcomes.

In several ICCs, patients with multiple pathogenic variants (either compound in the same gene or digenic) have more severe phenotypes than patients with single variants. In LQTS, patients with 2 pathogenic variants had longer corrected QT (QTc) intervals and were more likely to experience cardiac events and cardiac arrest than patients with single variants (25). This “gene dosage” effect has also been observed in HCM, in which multiple pathogenic variants have been associated with more adverse outcomes in a number of studies (26–28). In addition, patients with HCM who have a sarcomeric gene variant have been shown to have an increased risk of cardiovascular events and heart failure compared to patients with no identified pathogenic variant (29, 30).

Differentiating phenotype based on the presence of variants in specific genes has thus far had limited success. In HCM, there have been conflicting reports as to whether variants in thin filament genes [troponin T type 2, cardiac type (TNNT2), troponin I3, cardiac type (TNNI3), tropomyosin 1 (alpha) (TPM1), actin, alpha, cardiac muscle 1 (ACTC1)] confer distinct clinical phenotypes to variants in the major thick filament genes [myosin binding protein C, cardiac (MYBPC3), myosin heavy chain 7 (MYH7)], likely due to limited study sizes given the small proportion of patients with HCM who have thin filament variants. However, a recent multicenter study concluded that thin filament variants were associated with an increased risk of advanced LV dysfunction and heart failure, though with milder LV hypertrophy and outflow tract obstruction and similar rates of arrhythmia and SCD (31). No difference in clinical outcomes have been observed between patients with HCM harboring the 2 major thick filament variants (30). Limited data exist for genotype-phenotype correlation in DCM although it is believed that patients with lamin A/C (LMNA) variants have higher risks of cardiac events, particularly malignant ventricular arrhythmias (32).

COST-EFFECTIVENESS OF GENETIC TESTING

A number of studies have been published assessing the cost-effectiveness of genetic testing in patients with HCM and their families in the UK (33), Australia (34), and the US (4). These studies examined economic decision models comparing genetic cascade screening with the standard clinical approach for identifying and treating individuals at risk, as well as the potential savings associated with detecting and discharging genotype-negative relatives. The UK and Australian studies concluded that genetic cascade screening is more cost-effective for HCM than clinical diagnosis alone, with the incremental cost per life year saved/gained of €14397 in the UK (33) and €9509 in Australia (34), well within the recommended thresholds of €35000 (UK National Institute for Health and Care Excellence guidelines) and €31400 (Australian Pharmaceutical Benefits Advisory Committee). The savings associated with discharging genotype-negative relatives were estimated as €8615 for an individual over his/her lifetime in the UK study (33) and $0.7 million for a cohort of 2912 patients with HCM in the US study, covering 691 discharged family members (4). Given the conservative estimates used in these models, the benefits associated with expanding cascade screening beyond first degree relatives together with the recent dramatic reductions in the costs of genetic sequencing, the cost-effectiveness of comprehensive genetic testing in HCM is likely to be substantially greater.

Because the diagnostic yield in DCM is approximately 20%, the savings will be less than for HCM for discharges, but may be greater for prophylactic approaches in genotype positive individuals because DCM has substantial costs associated with heart failure treatments, devices, and transplantation. New cost-effectiveness studies for DCM (in the era of TTN sequencing) and other ICCs are required to accurately gauge the economic benefits of genetic testing in these conditions, particularly to incorporate the ongoing reductions in sequencing costs.

Sequencing Technologies

Advances in DNA sequencing technologies have had a dramatic effect on the use of genetic testing for clinical use. Diagnostic testing initially depended on techniques such as denaturing HPLC (dHPLC), Sanger sequencing, or array-based technologies (35). These methods were variously expensive (Sanger), time consuming (dHPLC, Sanger), lacking in analytical sensitivity (dHPLC), and difficult to scale up (all, especially for large genes such as TTN). Although genetic testing using these techniques became established for a number of ICCs, these limitations prevented their wide scale adoption as a routine part of clinical management and it will be some time before clinical workflows take advantages of the new opportunities now apparent.

The development of next generation sequencing (NGS) technologies since 2005 has addressed many of these shortcomings. NGS approaches have dramatically reduced the costs and timescales compared to traditional techniques, while offering an analytically sensitive and accurate sequencing for variant detection, although many clinical laboratories still, likely unnecessarily, confirm findings with an alternative technique (usually Sanger sequencing). NGS has also enabled expanded testing to account for the increasing number of genes associated with many ICCs, though this can present substantial challenges in variant interpretation and domain-specific knowledge is increasingly important.

Currently, NGS strategies for diagnostic testing include predefined gene panels, whole exome sequencing (WES) and whole genome sequencing (WGS), each of which offers distinct advantages and disadvantages. Gene panels can offer comprehensive sequencing of most or all genes that have been associated with the disease in question, allowing both the sequencing and data analysis to focus on these validated genes. Pan-ICC panels such as Illumina's TruSight Cardio panel (36) offer a single laboratory assay for a wide range of cardiac diseases, with the possibility to restrict data analysis to just those genes or conditions in question. Gene panels have been demonstrated to obtain excellent coverage of target regions and analytical sensitivity in variant detection, outperforming both WES and WGS (36) (Fig. 1). However, panels do not allow for variant detection in novel genes, require periodic review to account for newly discovered gene associations and targeted sequencing is poor at detecting certain variants classes such as copy number variants.

Sequencing coverage metrics for ICC genes on the TruSight Cardio panel using MiSeq and NextSeq and comparison with coverage for these genes using WES, Deep WES (i.e. 6 samples combined to obtain sequencing depth equivalent to TruSight Cardio panel), and WGS, as well as approximate targeted enrichment and sequencing cost per sample.

WES involves the targeted sequencing of all coding regions of the genome, comprising approximately 30 million bases across over 20000 genes. WES enables comprehensive analysis of the protein-altering variation in the genome (which accounts for the vast majority of known pathogenic variants in ICCs), both for genes with a known role in the disease of interest and for the discovery of novel genetic associations. However, identifying causative variants in novel genes is extremely difficult if not impossible in individual cases, given the large number of very rare or private variants found in every exome. WES is more successful at identifying putative pathogenic variants in trios and small nuclear families in which a known ICC gene is not involved, particularly in cases of recessive or de novo inheritance (e.g., an affected child with 2 unaffected parents, unusual for ICCs) (37, 38). WES can also be used as a comprehensive gene panel test, with analysis restricted to those genes associated with the disease (an exome “slice”), with the possibility of reanalysis when novel genetic links are proposed. However, limitations in sequencing coverage associated with WES, particularly for key disease genes and especially for difficult-to-capture but clinically important exons (e.g., exon 1 of KCNQ1), should be taken into account before pursuing this strategy.

WGS involves the complete sequencing of every base pair in the genome. It is the most comprehensive sequencing strategy available, provides more uniform coverage than WES, is more suited to detecting large structural variants, and is increasingly an affordable option with reduced sequencing costs. However, the practical advantages of WGS for diagnostic testing of individual patients are currently questionable because prioritizing the thousands of rare variants observed in each genome, and identifying actionable putative pathogenic variants, is an extremely difficult proposition, and even more challenging than the analysis of novel coding variants identified by WES. There are also major issues relating to data processing, storage, and indeed ownership with WGS. It is true that the data may be reinterrogated for other variants in the case of a second genetic condition (though it is questionable how often this is really needed), but if the data are in one hospital in one format and kept in deep storage, there are large logistic, manpower, and cost implications with accessing these data and sharing them with a second healthcare provider.

Variant Analysis and Interpretation

The accurate interpretation of genetic variants, in particular the identification of pathogenic variants that can impact the clinical management of patients and their families, is the single greatest challenge in diagnostic genetic testing for ICCs (and other Mendelian disorders). Unlike research findings, the consequences of a false-positive or false-negative result in a clinical diagnostic setting can be devastating. However, as a result of this, clinical laboratories may be too conservative in assigning pathogenicity to variants and reporting back variants of unknown significance (VUS) that could be classified as at least likely pathogenic with more discriminating analysis (12).

Multiple lines of evidence can be assessed to help classify variants (Table 2). These include the population frequency of a variant [derived from databases such as 1000 Genomes, the Exome Sequencing Project, and the Exome Aggregation Consortium (ExAC)](39), prior knowledge of the clinical and functional effect of a variant, segregation of a variant in affected family members (for the patient in question or previously studied or published pedigrees), and computational algorithms that predict the effect of a variant on protein function. Recently, guidelines such as those produced by the American College of Medical Genetics and Genomics (40) have attempted to standardize how this evidence is utilized to classify variants, by determining the weight that should be applied to each line of evidence and the requirements for categorizing variants into 5 recommended classes—pathogenic, likely pathogenic (>90% chance), VUS, likely benign (>90% chance), and benign.

Although the American College of Medical Genetics and Genomics guidelines provide a framework for the analysis of variants associated with Mendelian disease, their implementation in clinical diagnostic laboratories requires addressing the ambiguities associated with some lines of evidence. In particular, defining the evidence required for a variant to be classified as “known pathogenic” is challenging. The advent of population databases revealed that many variants that had been published as disease-causing (and listed in resources such as the Human Gene Mutation Database) were in fact present at far too high a frequency to be disease-causing (10, 41), indicating that these studies were poorly controlled or relied on insufficient evidence for defining pathogenicity. The ClinVar database was recently established to enable clinical laboratories to share information on the relationships between human variations and phenotypes (42). However, the standard of evidence associated with ClinVar entries is highly variable, obliging users to manually assess the data supporting each claim. To assist these efforts, well established cardiac diagnostic laboratories including the Laboratory of Molecular Medicine (4, 8) and the Oxford Molecular Genetics Laboratory (12) have recently published compendiums of their experience of over 10 years of cardiomyopathy genetic testing. We have created an online resource, the Atlas of Cardiac Genetic Variation (http://cardiodb.org/ACGV), to enable users to easily access these data.

Currently the evidence required to classify a variant as pathogenic relies heavily on observed segregation of the variant with the phenotype in affected families, a significant excess of the variant in disease cohorts over control groups (it should be absent or at very low frequencies, e.g., < 1:10000 alleles for HCM, in population databases), and characterization with well-established functional assays, preferably in vivo animal models. The exception is truncating variants in genes with a known loss of function mechanism for the disease, such as MYBPC3 in HCM, where the probability of pathogenicity is high enough to negate the need for additional evidence. For other variants, either novel or for which limited data already exists, it can be difficult to definitively assess their effect on phenotype, given that segregation analysis and functional characterization are not routinely available in diagnostic laboratories. To address this, recent studies have focused on improving the interpretability of novel and rare variants found in key cardiac genes, by using large disease and control cohorts to identify regions of ion channels (associated with LQTS) (14, 43), MYH7 (missense variants in HCM) (12) and TTN (truncating variants in DCM) (7) that are enriched with variants in patients. A prior probability of pathogenicity for rare variants found in these regions can be calculated from these analyses [described as the etiological fraction (12) or estimated predictive value (14)], which if sufficiently high (e.g., 97% in the HCM cluster of MYH7 (12), can provide strong evidence that a variant is disease-causing.

An increasing issue facing clinical genetics laboratories is the reinterpretation of previously identified variants in the light of new evidence, particularly if this leads to the reclassification of variants that have been reported to patients. New entries to resources like ClinVar or analysis techniques as described above may enable some VUS to be reclassified as pathogenic and initiate cascade screening in the affected patients' families. More seriously, variants that have previously been reported as disease causing have been reassessed with improved, population-specific controls such as provided by ExAC. Inadequate control cohorts were recently shown to cause misdiagnosis in HCM patients of African ancestry (44).

Challenges and Future Directions in Genetic Testing

MISSING HERITABILITY AND INACCURATE GENETIC ASSOCIATIONS

Despite decades of research into the genetics of Mendelian cardiac disease, the genetic test yield for several ICCs has remained stubbornly low, particularly for important conditions like HCM (approximately 40%), DCM (approximately 30% at best) and Brugada syndrome (approximately 20% at best). Although not all cases of these conditions will have a genetic basis, the substantial proportion of familial cases without identified causative variants suggests that additional genetic factors remain to be discovered. These factors may include large structural deletions (currently largely invisible using existing techniques) and copy number variations, variants affecting regulatory regions of genes or exon splicing, mosaicism (whole body or tissue level), or polygenic models of disease for which several variants and the environment may contribute to the phenotype.

To date, most studies have focused on trying to identify protein-altering variation in candidate disease genes with known cardiac function and a hypothetical role in the ICC in question, or using broad panels of genes associated with a range of ICCs (45, 46). These studies have substantially increased the number of genes putatively associated with a number of ICCs (Fig. 2). Rare variants (typically defined as a population allele frequency <0.001) in these genes that are identified through sequencing of large disease cohorts are classified as putative pathogenic variants, supported variously by computational algorithms, functional assays, or segregation of the variant in small family groups. However, this approach fails to recognize that although these variants may be individually rare, they can be collectively quite common when assessed across the entire gene. Without sequencing a similarly sized control cohort, or assessing the level of rare variation in genes in the available population databases such as ExAC, it cannot be demonstrated that there is an excess burden of rare variants in cases for these putatively associated genes. Indeed, many genes that have been linked to cardiomyopathies and are regularly sequenced in clinical diagnostics have no case excess compared to the ExAC reference population (12) (Fig. 3). Because most of these genes lack any variants with detailed linkage or segregation data for the diseases in question, it is likely they have been erroneously associated with these conditions.

Cumulative increase in the number of genes reported to be associated with major ICCs.

Proportion of individuals with rare variants (ExAC allele frequency <1 × 10⁻⁴) in HCM and DCM clinical cohorts (red = pathogenic, orange = likely pathogenic, yellow = VUS) compared to ExAC (grey columns).

Far from addressing the missing heritability, these studies have clouded our understanding of the genetic architecture of many ICCs, leading to an increase in the number of VUSs, or potentially even false positive results. Because diagnostic sequencing can now easily accommodate large gene panels or even WES/WGS approaches, it is critical that the analysis of detected variants focuses on genes with strong evidence for association with the query condition. To assist diagnostic laboratories in this task, the Clinical Genome Resource is developing frameworks to assess the evidence for associations between genes and particular diseases (47). A clear understanding of the roles of genes in Mendelian diseases is an essential step for the analysis and interpretation of genetic variants identified in patients.

MODIFIER GENES AND VARIANTS

Most ICCs are characterized by variable expressivity, i.e., the severity of disease can diverge strongly among genotype positive variant carriers. Although this is partly explained by the varying effects of different classes of variants and the impact of multiple variants as described above, phenotypic diversity is also observed within families amongst individuals who share the same primary pathogenic variant. Environmental factors are likely to be responsible for some of this effect but it is believed that additional genetic factors may also play a role. These modifying variants can add to the genetic burden and exacerbate the phenotype or may act as protective factors to limit the expression of disease. Some initial studies have begun to explore the role of modifying variants in ICCs, particularly focusing on LQTS.

Common single nucleotide polymorphisms (SNPs) in the nitric oxide synthase 1 adaptor protein (NOS1AP) gene have been shown through genome wide association studies to be associated with QTc interval (48–51). Genotype-phenotype studies on patients with LQTS have shown that SNPs in this gene are strongly associated with QTc interval (>12 ms/allele) (52) and an increased risk of cardiac events (52, 53). Because these SNPs can have a cumulative effect, genetic risk scores have been developed to analyze the overall effect of the modifier variants on phenotype and clinical outcomes (52). Another study assessed how SNPs in the 3′ untranslated region of the KCNQ1 gene can repress the expression of their associated KCNQ1 allele (54); patients with SNPs on the mutated KCNQ1 allele had shorter QTc intervals and fewer symptoms, whereas those with SNPs on the normal allele had longer QTc intervals and more symptoms.

The cumulative burden of SNPs associated with small phenotypic effects has also been studied in FH. Using 12 SNPs identified by metaanalysis of several genome wide association studies that are associated with raised LDL-C, a weighted gene score was calculated and compared in FH variant-positive, FH variant-negative, and control subjects (55). The score was significantly higher in both FH cohorts compared to controls, suggesting that the raised LDL-C concentrations in variant-negative patients may have a polygenic etiology, and that the LDL-C-associated SNPs may also contribute to the genetic burden of variant-positive patients, explaining some of the variable penetrance observed in this condition.

POPULATION SCREENING

Genetic sequencing in ICCs has thus far been limited to testing probands and, where a causative variant has been identified, cascade screening of family members. With the ever decreasing costs associated with DNA sequencing, the possibility of universal genetic testing (as applied in new-born screening for cystic fibrosis) or targeted testing of particular at risk groups (e.g., athletes at risk of SCD) has begun to be explored. However, because the frequency of rare variation in many key cardiac genes has recently been revealed to be substantially higher than previously recognized (10, 12, 56), the prior probability that a variant detected in a phenotype-negative individual will be disease-causing is low, given the rarity of these genetic conditions. Additionally, the limited therapeutic options available for cardiomyopathies and arrhythmias limits the benefits that could be derived from identifying at risk individuals.

One condition for which a universal or targeted screening approach could prove attractive is FH, which has the advantages of a relatively high genetic yield and effective clinical management with statin therapy. One of the strategies that has been proposed is reverse cascade screening, whereby infants with increased total cholesterol concentrations are genetically screened and, if a causative variant is identified, cascade screening applied to their parents (57). Another approach targets patients with a clinical history that suggests an increased risk of FH. A report of patients under age 60 years with a history of coronary artery disease found that 14.3% were estimated to have FH (58). However, a recent study that sequenced the 3 genes LDLR, apolipoprotein B (APOB), and proprotein convertase subtilisin/kexin type 9 (PCSK9) in 26025 individuals from several coronary artery disease, control and prospective cohorts identified FH mutations in <2% of participants with LDL-C ≥190 mg/dL, though this proportion could be increased by assessing structural variants, additional missense variants not classified as pathogenic, and polygenic SNPs. The presence of FH mutations did however confer a substantial increased risk of coronary artery disease across all LDL-C categories (59). If more widely adopted, approaches such as these have the potential to significantly increase the diagnosis of FH in the population, with prophylactic treatment offering substantial clinical and economic benefits (60).

Conclusion

Genetic testing in ICCs has made a rapid transition over the last 30 years from discovery research studies to evidence-based genetic sequencing of patients and their families. Recent advances in both sequencing technologies, which have driven down associated costs, and in the databases and analysis techniques used for variant interpretation, which improve the accuracy and analytical and clinical sensitivity of testing, should ensure that genetic testing becomes a standardized element of clinical management for many of these conditions. This promises to considerably increase the diagnosis of these diseases, particularly through cascade screening of patients' families, and reduce their clinical burden through appropriate clinical management and treatment. However, to fully unleash the potential of genetic testing in cardiac disease, further progress will be required in our understanding of the genetic etiology of these conditions and the impact of variants on clinical outcomes in presymptomatic individuals and in those with existing disease. Efficient data sharing between clinical laboratories (using resources like ClinVar) and the application of novel research findings will be essential to improve the interpretation of rare variation and hence the accuracy and yield of diagnostic tests. Addressing the issue of missing heritability, particularly by exploring genetic variation in nonprotein coding parts of the genome, will be important to maximize the benefits of genetic sequencing for ICCs. Finally, investigating the basis of the phenotypic variability observed in many ICCs, and the role that modifier variants may have in explaining this, may enable genetic testing to play a greater role in directing clinical care and treatment.

6 Nonstandard abbreviations

 
• ICC

  inherited cardiac condition

 
• FH

  familial hypercholesterolemia

 
• SCD

  sudden cardiac death

 
• CHD

  coronary heart disease

 
• HCM

  hypertrophic cardiomyopathy

 
• LV

  left ventricular

 
• DCM

  dilated cardiomyopathy

 
• ARVC

  arrhythmogenic right ventricular cardiomyopathy

 
• RV

  right ventricular

 
• LQTS

  long QT syndrome

 
• CPVT

  catecholaminergic polymorphic ventricular tachycardia

 
• LDL-C

  LDL cholesterol

 
• QTc

  corrected QT

 
• dHPLC

  denaturing HPLC

 
• NGS

  next generation sequencing

 
• WES

  whole exome sequencing

 
• WGS

  whole genome sequencing

 
• VUS

  variant of unknown significance

 
• ExAC

  exome aggregation consortium

 
• SNP

  single nucleotide polymorphism.

7 Human genes

 
• TTN

  titin

 
• PKP2

  plakophilin 2

 
• DSC2

  desmocollin 2

 
• DSG2

  desmoglein 2

 
• DSP

  desmoplakin

 
• JUP

  junction plakoglobin

 
• KCNQ1

  potassium voltage-gated channel subfamily Q member 1

 
• KCNH2

  potassium voltage-gated channel subfamily H member 2

 
• SCN5A

  sodium voltage-gated channel alpha subunit 5

 
• RYR2

  ryanodine receptor 2 (cardiac)

 
• CASQ2

  calsequestrin 2 (cardiac muscle)

 
• TRDN

  triadin

 
• CALM1

  calmodulin 1 (phosphorylase kinase, delta)

 
• KCNJ2

  potassium voltage-gated channel subfamily J member 2

 
• LDLR

  low density lipoprotein receptor

 
• TNNT2

  troponin T2, cardiac type

 
• TNNI3

  troponin I3, cardiac type

 
• TPM1

  tropomyosin 1 (alpha)

 
• ACTC1

  actin, alpha, cardiac muscle 1

 
• MYBPC3

  myosin binding protein C, cardiac

 
• MYH7

  myosin, heavy chain 7

 
• LMNA

  lamin A/C

 
• NOS1AP

  nitric oxide synthase 1 adaptor protein

 
• APOB

  apolipoprotein B

 
• PCSK9

  proprotein convertase subtilisin/kexin type 9

 
• MYL2

  myosin, light chain 2

 
• MYL3

  myosin, light chain 3

 
• BAG3

  BCL2 associated athanogene 3

 
• RBM20

  RNA binding motif protein 20.

References