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An overview of the genetic basis of cardiovascular disease.

Cardiovascular disease (CVD) claims ~20 million lives worldwide annually, accounting for a third of global deaths. More than 75% of deaths from CVD occur in low- and middle-income countries (LMICs). Eighty-five percent of all CVD deaths worldwide are due to myocardial infarction (MI) and stroke. [1] In sub-Saharan Africa (SSA), heart failure is the dominant form of CVD; the causes are largely non-ischaemic, i.e. hypertension, cardiomyopathy, rheumatic heart disease, pericardial disease and cor pulmonale. [2] The high prevalence and changing profile of CVD in SSA are directly linked to population dynamics and epidemiological transition in some of the most vulnerable societies. The epidemiological transition, characterised by an increase in obesity, hypertension and diabetes mellitus, has seen an increase in non-communicable diseases (NCDs) in urban, semi-urban and rural areas. The burden of NCDs is likely to increase further, as antiretroviral therapy reduces mortality and affects the cardiovascular system. Further, infections and NCDs are fuelled by common precursors such as poverty and poor education. [3] CVD related to genetic predisposition is gaining increased awareness in SSA owing to advances in genetic technologies, resulting in substantial expansion of knowledge of the genetic basis and mechanisms of CVD. [4]

Table 1 provides a glossary of the genetic terminology used in this article. [5]

What are genetic variations?

Each human being is unique, but genetically, we are 99.9% the same. The remaining 0.1% is responsible for all the differences that make each one of us individual and is caused by genetic variation (GV). GV is the difference in DNA sequences between individuals within a population and can be caused by repair errors in the replication machinery. Variation occurs in germ cells and somatic cells. Only variation that arises in germ cells can be inherited from one individual by another and affect population dynamics, and ultimately evolution. Large changes to our genome may have no effect at all, whereas the change of a single nucleotide can have a huge impact. Mutations and recombination are major sources of variation.

There are three types of GV: (i) single-nucleotide polymorphisms (SNPs) are the most common, occurring about once every 1 000 bases; (ii) insertion or deletion of a single stretch of DNA sequence that can range from two to hundreds of base-pairs in length; and (iii) genetic rearrangements that can affect several genes or large areas of a chromosome at once. GV includes both copy number variation and chromosomal rearrangement events.

Most variation is meaningless and does not affect our ability to survive or adapt. For example: (i) 'silent mutations' in DNA, which change the DNA, but not the amino acid sequence; (ii) other mutations may change the amino acid sequence of a protein, but not the overall function of that protein; and (iii) other polymorphisms do not seem to affect our survival at all. Other variations are positive and improve our ability to survive or adapt, e.g. mutations that cause sickle cell anaemia have a protective effect against malaria. Some variation leads to disease such as monogenic single-gene disorders, e.g. the inherited cardiomyopathies.

The effect of GVs on phenotype (effect size) follows a gradient from none (indiscernible) to large (clinically consequential). The vast majority of GVs do not have significant biological effects. GVs that cause large phenotypic effects are usually responsible for single-gene disorders that exhibit Mendelian patterns of inheritance. Complex and polygenic disorders are primarily due to many common GVs, each exerting small, indiscernible effects, but collectively and through interactions with non-genetic factors, influencing the risk of the complex phenotype, e.g. coronary artery disease (CAD). GVs are classified functionally as pathogenic, likely pathogenic, of uncertain significance, likely benign and benign (Table 2).

Genetic cardiovascular conditions

CVD is caused by both genetic and environmental factors. In every condition, the interaction of genes with the environment is complex and profoundly affects phenotype. Genetic cardiovascular conditions may show incomplete or age-related penetrance, whereby individuals may not show signs of the condition until a certain age, or until environmental or lifestyle factors precipitate the manifestation of physical signs of the disease, e.g. hypertrophic cardiomyopathy (HCM) usually manifests in early adolescence during periods of rapid somatic growth. Genetic CVDs also demonstrate variable expressivity, whereby different family members with the same genetic mutation express the disease in different ways (i.e. varying severity). Expressivity is dependent upon environmental, lifestyle and background genetic modulators. Genetic cardiovascular conditions demonstrate clinical and genetic overlap, which may complicate the interpretation of genetic test results. Despite these caveats, knowledge of the genetic nature of a particular condition may be important--not only may it inform the need for familial screening and therefore early diagnosis and treatment to delay onset of disease, but it may also aid in risk stratification for sudden cardiac death (SCD) in affected individuals and potentially effect treatment and management decisions. [8]

Genetic cardiovascular conditions may be Mendelian (i.e. due to a single-gene defect) or non-Mendelian (i.e. due to complex, common GVs), or mitochondrial (Table 3). Most of these conditions display significant heterogeneity, i.e. locus, allelic and phenotypic. Locus heterogeneity refers to the phenomenon that numerous genes can result in the same clinical phenotype (e.g. >20 genes, mostly of the sarcomere, have been implicated in the aetiology of HCM). Allelic heterogeneity denotes various mutations, or alleles, within a gene, which can cause the same condition. Phenotypic heterogeneity refers to mutations in the same gene causing different phenotypes.

For example, MYH7 gene mutations can cause HCM, dilated cardiomyopathy (DCM), left ventricular non-compaction (LVNC) or restrictive cardiomyopathy (RCM) and can also be associated with myopathy. Similarly, SCN5A gene mutations not only cause arrhythmia syndromes, such as long QT syndrome (LQTS), short QT syndrome (SQTS) and Brugada syndrome, but also DCM. This genetic diversity means that many mutations may be 'private', defined by their presence in only single families, with as many as 60-90% being described as private in the setting of cardiomyopathies. [9] Recurrent or 'founder' mutations for certain conditions have been described, and may have important implications for certain conditions in South Africa (SA) (e.g. LQTS, HCM and familial hypercholesterolaemia (FH)).

Single-gene disorders may be inherited as autosomal dominant (e.g. HCM, DCM, LVNC), autosomal recessive (e.g. LQTS, arrhythmogenic right ventricular cardiomyopathy (ARVC) with Naxos syndrome) or X-linked (e.g. Duchenne muscular dystrophy with cardiac involvement). Importantly, lack of family history does not imply that the condition does not have a genetic basis, as it could represent a de novo mutation, incomplete penetrance or an uninformative family history. [10] Complex traits associated with polygenic states, such as serum low-density lipoprotein (LDL) cholesterol levels and CAD, are extremely common. Therefore, genetic heterogeneity from GVs across the frequency spectrum may be the rule. Consider plasma triglycerides: ~50% of the inter-individual variability in plasma triglycerides is estimated to be based on GVs. [11]

When to suspect an inherited cardiovascular disease

There are several key features of CVD that should raise suspicion of a strong genetic component to disease. These include extreme phenotypes: unexplained severe disease or early age of onset (childhood or early adulthood), presence of a family history, no identifiable environmental or other cause for the condition in an otherwise healthy individual (idiopathic), history of premature SCD, and presence of additional organ involvement or other syndromic features. [4]

Discovering genes for cardiovascular disease

Traditionally, two major approaches for gene discovery in CVD have been used, i.e. linkage analysis and genetic association. The choice of approach has depended on the pattern of segregation of disease--whether consistent with Mendelian ratios or polygenic complexity. [12] For Mendelian forms of CVD, direct DNA sequencing and/or linkage analysis has successfully yielded causal genes and mutations. In 1985, Lehrman et al. [13] sequenced the LDL receptor gene in a patient with homozygous FH and uncovered a 5 kB deletion that eliminated several exons in the first demonstration of a mutation for Mendelian CVD. In 1989, linkage analysis was used to localise the chromosomal position of a causal gene for HCM. [14] Since then, there has been a plethora of discoveries for genes responsible for single-gene CVDs.

Lessons learnt from Mendelian forms of cardiovascular disease

Rare single-gene variants. Since Mendelian diseases are rare, there was initial scepticism regarding whether the genes and mechanisms that cause CVD would inform our understanding of common forms of the disease. Linkage studies have radically transformed our understanding of CVD. Selected examples of Mendelian diseases, the responsible genes, and the gleaned biological and clinical insights are detailed in Table 3. Of note is monogenic severe FH, where the 6 responsible genes have led to fundamental new biological concepts and supported the development of new therapies.

Genotype-phenotype correlations. Since the early discovery of genes responsible for CVD, researchers have been looking for genotype-phenotype correlations to explain the impact of genetic background on physical signs. While there are clear examples where single-gene mutations lead to straightforward genotype-phenotype associations, often other more complex relationships also exist. The complexity arises from 3 distinct genetic phenomena: pleiotropy, penetrance and expressivity (Table 1). Pleiotropy, penetrance, expressivity and non-genetic factors conspire to ensure that even in single-gene disorders, genotype does not 'equal' a specific phenotype, with several important consequences: (i) gene discovery is more difficult, as genotype may not segregate perfectly with phenotype, reducing the power of linkage; (ii) there is intense interest in identifying modifiers--genetic or environmental--that modulate the relationship between genotype and phenotype; and (iii) in many Mendelian diseases, it has been difficult to develop genotype-specific prognostic or therapeutic recommendations. [12]

Tenuous path from gene discovery to understanding disease mechanisms and developing targeted treatment. The ability to identify causative mutations is only the beginning of the pathophysiological link. The underlying signalling pathways activated by the gene and protein need to be understood. Ultimately, putative therapeutic targets must also be exploited. For example, in Marfan's syndrome, the path from the discovery of FBN1 as the causal gene to a breakthrough in the molecular understanding of the disease took more than 2 decades. The importance of the role of transforming growth factor [beta] (TGF-[beta]) was not understood until recently. Microfibrils normally bind the large latent TGF-[beta] complex, and failure of this to occur results in increased TGF-[beta] activation and signalling. Investigators are currently exploring the hypothesis that blocking of TGF-[beta] signalling will ameliorate the growth of aortic aneurysms in Marfan's syndrome. [15]

Lessons learnt from complex polygenic and common forms of cardiovascular disease

GVs across the spectrum of allele frequency contribute to most complex and common diseases, as well as quantitative traits. There is a strong overlap between genome-wide association studies (GWAS) and genes identified through Mendelian families, e.g. 19 genes have been identified as monogenic causes of extremely low or high levels of LDL cholesterol, high-density lipoprotein (HDL) cholesterol and triglycerides; loci harbouring 16 of these genes were also mapped using GWAS. Monogenic and polygenic variants often coexist within the same condition.

GWAS. The proportion of overall phenotypic variance explained by a GV may have little correlation with the ultimate therapeutic or biological value of the gene mapped by the GV. Phenotypic variance explained by a variant is a function of two key parameters, i.e. allele frequency and effect size. For Mendelian diseases, the causal variants typically confer large effects but explain a small proportion of trait variance due to their rare frequencies. GVs from GWAS are common but explain only a small proportion of trait variance due to modest effects. Nevertheless, variants that explain a small proportion of phenotypic variance may provide substantial biological or therapeutic insights. For example, GVs in introns of HMGCR confer a small effect on plasma LDL cholesterol; however, targeting of these genes with statins or ezetimibe has a much more dramatic effect on LDL cholesterol. To date, there have been no rare, large-effect Mendelian mutations described in HMGCR, presumably because such mutations are highly deleterious and not tolerated. Despite the success of GWAS, most mapped loci have been a challenge to move from genomic localisation to understanding the biological mechanism. The reasons for this observation are manifold: (i) in the context of discovery of Mendelian genes, inferring new biology from human genetics takes time and only about 13 years have elapsed since the initial GWAS publications; (ii) gene mapping and experimental follow-up require unique skill sets, expertise and collaboration; and (iii) genetic mapping by association gives us gene regions and not necessarily specific causal GVs or causal genes.

GVs may distinguish causal from non-causal biomarkers. In 1961, Kannel et al. [16] established an association of plasma total cholesterol with future risk of CAD. Since then, hundreds of soluble biomarkers have similarly been associated with risk of CAD. The important question is how many of these biomarkers directly cause CAD and how many simply reflect other causal processes. Both causal and noncausal biomarkers may predict risk for future disease, but only a causal biomarker may be appropriate as a therapeutic target. In Mendelian randomisation, DNA variants are used to address the question of whether an epidemiological association between a risk factor and disease reflects a causal influence of the former on the latter. [17] If, in an adequately powered sample, the predicted association between the GV and disease were not observed, it would argue against a causal role for the GV in the pathogenesis of the disease.

Next-generation sequencing approaches

Next-generation sequencing platforms have markedly decreased the cost of DNA sequencing compared with Sanger sequencing. While whole-exome and whole-genome sequencing have been successfully used to identify new genes for several Mendelian forms of CVD, sequencing just the exome (rather than the entire genome) is justified in the search for genetic causes of rare inherited disorders because most alleles responsible for Mendelian disorders disrupt the protein-coding sequence. The yield of exome sequencing in solving Mendelian disorders is difficult to predict, as negative results have not been routinely reported. [18] It is often difficult to arrive at a single causal mutation after exome sequencing because: (i) the causal GV may not be protein coding (i.e. located in an intron); (ii) the causal GV may be protein coding but the relevant gene not successfully sequenced; (iii) if the causal mutation is not fully penetrant, it will be present in both phenotypically affected and unaffected individuals; (iv) segregation of a phenotype in a family may be due to non-genetic factors (e.g. in a multi-generation family, where multiple individuals are affected with MI, the pattern may be due to a new Mendelian gene or poor lifestyle habits shared by the family); and (v) in contrast to single nucleotide substitutions, methods for identifying small insertion-deletions and copy number changes from short-read sequence data are imprecise. For complex phenotypes (e.g. MI) that arise after decades of pathology, distinguishing functional v. neutral missense mutations is key. While advancements in sequencing techniques, and the development of various pathogenicity prediction tools and publicly available resources on variant frequency in large populations have been informative, clinical applicability remains a challenge.

Functional studies

Although technological advantages provide cumulative information of genetic causality, the prediction of pathogenicity and the interpretation of clinical consequences of a GV remain challenging. Functional assessment is the most powerful and well-validated strategy to elucidate the role of a candidate variant to the disease pathophysiology, as nothing is more convincing than in vivo data derived from animal models used to characterise the clinical phenotype and the mechanism by which the candidate GVs are shown to cause disease. [19] Knock-in and knock-out animal models have been employed for this purpose for decades, but animal models fail to fully recapitulate the human phenotype.

Use of stem cells and programmed cells for understanding CVD. More recently, human adult somatic cells can be reprogrammed into a pluripotent state by activating differentiation transcription factors. Induced pluripotent stem (iPS) cells can differentiate into the specific cell types affected by disease. iPS cells have provided an unprecedented opportunity to investigate the consequences of human GV on cellular phenotypes that may contribute to disease. iPS cells have contributed enormously to understanding the biology of LQTS, HCM, DCM and Noonan syndrome. The ability to refine functional effects of GVs has been advanced through the use of zinc-finger nucleases and transcription activator-like effector nuclease (TALEN) and clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9 (Cas9) technology that specifically introduces or removes GVs in stem cells to reveal the contribution of individual GVs to cellular phenotypes.

Use of zebrafish to study human CVD. Zebrafish possess a variety of features that are advantageous for use as an experimental model organism for the study of CVD. Due to their small size (2-4 cm), zebrafish are easy to handle and one female can produce ~200 eggs per week. Zebrafish embryos develop externally and very rapidly to freely swimming and fed larvae within 5 days. The zebrafish is an excellent system for microscopic applications, as embryos are transparent and numerous transgenic fluorescent reporter lines are available. Because of their suitability for imaging applications, zebrafish are also highly interesting for high-throughput small compound screens. They are ideal for the study of heart development and disease: (i) zebrafish heart development proceeds fast and results in a differentiated two-chambered heart within 48 hours postfertilisation; (ii) zebrafish embryos, in contrast to mammalian or avian embryos, are able to cover their oxygen demand by diffusion during the first days of development and are not dependent on blood circulation. This enables the investigation of gene knock-outs or knock-downs, even if they lead to severe defects of the cardiovascular system; and (iii) humans and zebrafish share a 70% sequence similarity, and 84% of human disease-causing genes can also be found in the zebrafish genome. The different morphology of the zebrafish heart limits the translation of findings into the mammalian system; without a coronary artery system during embryogenesis, zebrafish show a vasculature on the heart surface, which restricts the study of CAD. Functional verification of candidate genes using CRISPR-Cas9 has supported the role of several genes in the pathophysiology of CVD. Zebrafish hearts maintain their ability to regenerate throughout their lifetime, providing novel insights to understanding human cardiac regeneration. [20]

The future of genetic applications in cardiovascular disease

The widespread use and availability of genetic approaches for the diagnosis of CVD have allowed the study of direct and indirect genetic predisposition, prevention of CVD, genetic testing, population screening and precision cardiology, including pharmacogenetics and pharmacogenomics. In the future, the ability to use specific therapies based on specific genotypes will be widespread (as is already the case in oncology), as will gene therapy for specific single-gene disorders. Barriers to implementing these advances in general cardiology include the inability of physicians to interpret genetic test results and provide appropriate counselling, poor understanding of risk probabilities and growing concern with ethical issues related to emerging genetics and genomics. A major challenge, already evident, is how LMICs will have access to novel and costly research and commercial genetic advances, including genetic screening on a broad scale. A major inequity that needs addressing in the future is the dearth of data on the genetic underpinnings of CVD in SSA. Our group has recently shown that a gene panel with a yield of >60% in a European population has a yield of 18% in South Africans with HCM, highlighting the need to identify genetic causes of CVD in Africans. [21] Similarly, GVs identified in non-SSA populations need to be treated with caution until population screening in local populations has been performed and verified as pathogenic, as it has previously been demonstrated that rare GVs in European populations could represent common GVs in African populations.

Genetic testing recommendations and genetic counselling

Different types of genetic testing include pre-implantation testing, diagnostic testing at any age, carrier testing, pre-symptomatic testing, postmortem testing, pharmacogenomic testing, risk-prediction testing and direct-to-consumer testing. Importantly, all genetic testing should be based on careful case definition and accurate phenotyping. In patients suspected of having familial disease, the first step is to construct a family pedigree, which often informs the pattern of inheritance. If the pedigree suggests a Mendelian pattern of inheritance (e.g. autosomal dominant, autosomal recessive or X-linked recessive), cascade clinical screening is recommended. After informed consent and a comprehensive counselling process by a qualified professional, DNA should be obtained from the proband (clinically affected index patient) for genetic testing. If a causative GV is identified, it can be useful in: (i) clarifying the diagnosis in a person who has or is suspected of having CVD; (ii) identifying the cause of CVD in a family; (iii) providing options for family planning, including pre-implantation genetic diagnosis to avoid having affected offspring; and (iv) predicting which family members are at increased risk of developing CVD by offering predictive testing to unaffected family members. Those family members who have the mutation will require close medical follow-up, as they are at increased risk of developing CVD. Individuals who test negative for the mutation are unlikely to develop the disease and, therefore, do not usually require clinical follow-up and can be reassured. Successful predictive testing in families relies on a high degree of certainty that the GV identified is pathogenic and considered causal of the CVD in a specific family. Interpretation of genetic testing warrants caution. For example, a negative result in a proband means that the laboratory has been unable to identify a disease-causing mutation in the genes evaluated. It is nevertheless possible that the cause of the CVD is genetic. Similarly, the discovery of new GVs should be viewed with caution until these have been subjected to functional testing in large family pedigrees or in independent populations. The physician must always be prepared for the wide range of emotional reactions, including anger, guilt, grief, sadness, fear and anxiety, which accompany a new genetic diagnosis or misdiagnosis.


Technological advances are unravelling the genetic underpinnings of CVD and have ushered in a new era of genetically informed diagnosis of both rare and common forms of CVD. It is important for clinicians to understand the capabilities and limitations of genetic and genomic testing in different clinical settings and to translate these into clinical practice.

DOI: 10.7196/SAMJ.2019.v109i6.14069

Declaration. None.

Acknowledgements. We gratefully acknowledge our patients and their families who teach us daily about the complexities of the genetics of cardiovascular disease.

Author contributions. NL and NABN drafted the manuscript; GS and SMK critically reviewed it and gave input into the writing; and NABN approved the changes and takes responsibility for the content.

Funding. Prof. NAB Ntusi gratefully acknowledges funding support from the National Research Foundation, South African Medical Research Council, GlaxoSmithKline, Newton Foundation (UK), Technology Innovation Fund, Harry Crossley Foundation, American Heart Association, US National Institutes of Health, Bill and Melinda Gates Foundation and Lily and Ernst Hausmann Trust.

Conflicts of interest. None.

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[19.] Bournele D, Beis D. Zebrafish models of cardiovascular disease. Heart Fail Rev 2016;21(6):803-813.

[20.] Zaragoza C, Gomez-Guerrero C, Martin-Ventura JL, et al. Animal models of cardiovascular diseases. J Biomed Biotechnol 2011;2011:497841.

[21.] Ntusi N, Shaboodien G, Badri M, Gumedze F, Mayosi B. Clinical features, spectrum of causal genetic mutations and outcome of hypertrophic cardiomyopathy in South Africans. Cardiovasc J Afr 2016;27(3):152-158.

Accepted 2 April 2019.

N Laing, (1) MSc; S M Kraus, (2,3) FCP (SA); G Shaboodien, (3) PhD; N A B Ntusi, (2,3,4) FCP (SA), MD, DPhil

(1) Division of Human Genetics, Department of Medicine, Faculty of Health Sciences, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa

(2) Division of Cardiology, Department of Medicine, Faculty of Health Sciences, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa

(3) The Cardiovascular Genetics Laboratory, Hatter Institute of Cardiovascular Research in Africa, Department of Medicine, Faculty of Health Sciences, University of Cape Town, South Africa

(4) Cape Universities Body Imaging Centre, Faculty of Health Sciences, University of Cape Town, South Africa

Corresponding author: N A B Ntusi (
Table 1. Glossary of genetic terms used

Allele          An allele is one of the possible forms of a gene. Most
                genes have two alleles, a dominant allele and a
                recessive allele. If an organism is heterozygous for
                that trait, or possesses one of each allele, then the
                dominant trait is expressed. A recessive allele is only
                expressed if an organism is homozygous for that trait,
                or possesses two recessive alleles. Alleles were first
                defined by Gregor Mendel in the law of segregation

Exome           The DNA that is coded for in the exons of genes that
                code for proteins, i.e the 'protein-coding' regions of
                the genome

Expressivity    The degree to which a trait is expressed in an
                individual, i.e. the intensity of the phenotype

Founder         A common mutation that occurs in higher frequency in a
                specific population due to a common ancestor

Genome          All the DNA contained in an organism or a cell,
                including nuclear and mitochondrial DNA

Genotype        The set of genes in our DNA that is responsible for a
                particular trait

Genome-wide     Also known as whole-genome association study, is an
association     observational study of a genome-wide set of genetic
study           variants in different individuals to see if any variant
                is associated with a trait

Haplotype       A haplotype (haploid genotype) is a group of alleles in
                an organism that are inherited together from a single

Heterogeneity   Genetic heterogeneity occurs when mutations at two or
                more genetic loci produce the same or similar
                phenotypes (either biochemical or clinical)

Homozygosity    Genetic homozygosity describes a situation where an
                individual inherits the same alleles for a particular
                gene from both parents

Locus           A locus (plural loci) in genetics is a fixed position
                on a chromosome, such as the position of a gene or a
                marker (genetic marker)

Mendelian       A Mendelian trait is one that is controlled by a single
genetics        locus in an inheritance pattern. In such cases, a
                mutation in a single gene can cause a disease that is
                inherited according to Mendel's laws. Examples include
                sickle cell anaemia and cystic fibrosis. Mendel's
                studies yielded three 'laws' of inheritance: the law of
                dominance, the law of segregation and the law of
                independent assortment. Each of these can be understood
                through examining the process of meiosis

Mendelian       A method of using measured variation in genes of known
randomisation   function to examine the causal effect of a modifiable
                exposure on disease in observational studies

Penetrance      The probability of a gene or trait being expressed

Phenotype       The physical expression, or characteristics, of that
                trait due to a specific genotype

Pleiotropy      The ability of a single gene to influence multiple
                phenotypic traits

Table 2. Classification of genetic variants

                                      Utility for       Utility
Category       Definition             proband           for family

Pathogenic     Loss-of-function and   Establishes       Can be used for
               pathogenic variants    diagnosis, may    predictive
               shown to cause the     inform            genetic testing
               disease through co-    management
               segregation and
               linkage analysis or
               functional studies
               in large families

Likely         Evidence for the       Possibly          Predictive
pathogenic     causal role based on   suggests          genetic testing
               statistical            diagnosis, may    of unaffected
               enrichment in small    inform            relatives
               families and trios     management or     should be
               with the disease.      lead to           approached with
               Robust linkage is      additional        great caution,
               hindered by small      diagnostic        may be combined
               size of families or    tests             with phenotypic
               sporadic nature of                       evaluation and
               the disease. To                          surveillance
               reduce the
               possibility of
               random co-
               segregation in small
               families, findings
               require testing for
               replication in

Variant        Case-control studies   Unknown           Should not be
of unknown     will show an                             used for
significance   association between                      predictive
               genetic variants and                     genetic testing
               the phenotype.                           Testing of
               Replication in an                        affected
               independent study                        relatives for
               population is                            segregation may
               necessary. Disease-                      provide
               associated variants                      evidence of
               may be in linkage                        causality
               disequilibrium with
               the actual
               pathogenic variants,
               requiring additional
               studies to confirm
               actual pathogenicity

Likely         These variants may     None              No option for
benign         affect expression                        predictive
               levels, structure                        genetic
               and function of                          testing, rely
               proteins, but have                       on longitudinal
               not been associated                      phenotypic
               with disease                             evaluation

Benign         Comprises the vast     None              No option for
               majority of genetic                      predictive
               variants in the                          genetic
               genome, which have                       testing, rely
               absolutely no impact                     on longitudinal
               on protein                               phenotypic
               expression,                              evaluation
               structure and
               function, and no
               impact on phenotype

Adapted from Marian et al.[6] and Cirino et al.[7]

Table 3. Classification of genetic cardiovascular disease

Type              disorder          Specific diagnosis

Mendelian         Cardiomyopathy    HCM
(single-gene)                       DCM
disorders                           ARVC
                  Channelopathies   LQTS
                                    Brugada syndrome
                  Lipid disorders   Familial

                  Syndromic         Rasopathies: Noonan
                  disorders         syndrome (also with
                                    multiple lentigines
                                    and-or loose anagen
                                    hair), Costello syn-
                                    drome, cardiofacial
                                    cutaneous syndrome
                                    Muscular dystrophy
                                    Myotonic dystrophy
                  Vascular          Marfan syndrome,
                  disorders         Loeys-Dietz
                                    syndrome, familial
                                    aortic aneurysm,
                                    vascular Ehlers-
                                    Danlos syndrome
                  Systemic          Glycogen storage
                  disorders         disorders: Pompe
                                    (type II), Forbes
                                    (type IIIa), Danon
                                    (type IIb)
                                    Lysosomal storage:
                                    Familial amyloidosis

Non-Mendelian     Coronary artery   Myocardial
(multiple-gene)   disease           infarction
disorders         Hypertension      Arterial
                  heart disease
                  Valvular heart    RHD
                  disease           BAV
                  Plasma LDL        LDL cholesterol
                  cholesterol       levels

disorder          Specific diagnosis      Phenotype

Cardiomyopathy    HCM                     Hypertrophic cardiomyopathy
                  DCM                     DCM
                  ARVC                    Arrhythmogenic cardiomyopathy
                  LVNC                    Cardiomyopathy with LVNC
Channelopathies   LQTS                    Ventricular arrhythmia
                  SQTS                    Ventricular arrhythmia
                  Brugada syndrome        Ventricular arrhythmia
                  CPVT                    Ventricular arrhythmia
Lipid disorders   Familial                Premature myocardial
                  hypercholesterolaemia   infarction, stroke,
                                          peripheral vascular disease
Syndromic         Rasopathies: Noonan     Cardiomyopathy, supravalvular
disorders         syndrome (also with     pulmonary stenosis,
                  multiple lentigines     coartation of the aorta
                  and-or loose anagen
                  hair), Costello syn-
                  drome, cardiofacial
                  cutaneous syndrome
                  Muscular dystrophy      Cardiomyopathy
                  Mitochondrial           Dilated cardiomyopathy,
                  disorders               hypertrophic cadiomyopathy
                  Myotonic dystrophy      Myotonic dystrophy
Vascular          Marfan syndrome,        Aortic dilatation/aneurysm/
disorders         Loeys-Dietz             dissection, aortic
                  syndrome, familial      regurgitation, mitral valve
                  aortic aneurysm,        prolapse
                  vascular Ehlers-
                  Danlos syndrome
Systemic          Glycogen storage        Hypertrophic cardiomyopathy
disorders         disorders: Pompe        phenocopy
                  (type II), Forbes
                  (type IIIa), Danon
                  (type IIb)
                  Lysosomal storage:      Hypertrophic cardiomyopathy
                  Anderson-Fabry          phenocopy
                  Infiltration:           Restrictive/hypertrophic
                  Familial amyloidosis    cardiomyopathy
Coronary artery   Myocardial              Myocardial infarction,
disease           infarction              ischaemic LV dysfunction
Hypertension      Arterial                Hypertensive heart disease
Congenital                                Tetralogy of Fallot,
heart disease                             atrioventricular septal
                                          defects, pulmonary atresia,
                                          transposition of the great
Valvular heart    RHD                     Valvular disease
disease           BAV                     Aortic dilatation, BAV and
                                          aortic coarctation
                  MVP                     Mitral valve regurgitation
Plasma LDL        LDL cholesterol         Hyperlipidaemia
cholesterol       levels

disorder          Specific diagnosis      Involved genes

Cardiomyopathy    HCM                     MYH7, MYBPC3, TNNT2, TPM1,
                                          TNNI3, MYL2, ACTC1, TCAP,
                                          MYL3, MYOZ2, TRIM63, TTN,
                                          MYLK2, MYH6, TNNC1, CAV3,
                  DCM                     >50 genes (LMNA, sarcoglycan,
                                          dystrophin, PLN, SERCA, RYR2,
                                          MYH7, TTN, ATC, CSRP3, LDB3,
                                          BM20, TNNT2, TNNI3, TNNC1,
                                          TNNI3K, ANKR1, ES, RYAB,
                                          TCAP, CL, BAG3, SCN5A, ABC9,
                  ARVC                    PKP2, DSP, DSC2, TMEM43,
                                          DSG2, CDH2, PLB, JUP, LMNA,
                                          PLN, TTN
                  LVNC                    TAZ, ACTC1, DTNA, HCN4, LDB3,
                                          LMNA, MIB3, MYBPC3, MYH7,
                                          PRDM16, SCN5A, TNNT2, TMP1
Channelopathies   LQTS                    KCNQ1, SCN5A
                  SQTS                    KCNH2, SCN5A
                  Brugada syndrome        KCNH2, SCN5A
                  CPVT                    RYR2
Lipid disorders   Familial                LDLR, APOB, PCSK9, ARH,
                  hypercholesterolaemia   ABCG5, ABCG8, ANGPTL3, HMGCR
Syndromic         Rasopathies: Noonan     PTPN11, KRAS, SOS1, RAF1,
disorders         syndrome (also with     BRAF, NRAS, HRAS, RIT1, MEK1,
                  multiple lentigines     MEK2, SHOC2
                  and-or loose anagen
                  hair), Costello syn-
                  drome, cardiofacial
                  cutaneous syndrome
                  Muscular dystrophy      Dystrophin
                  Mitochondrial           mtDNA
                  Myotonic dystrophy      DMPK
Vascular          Marfan syndrome,        FBN1, TGFBR1, TGFBR2, SMAD3,
disorders         Loeys-Dietz             ACTA2, MYLK, MYH11, COL3A1
                  syndrome, familial
                  aortic aneurysm,
                  vascular Ehlers-
                  Danlos syndrome
Systemic          Glycogen storage        GAA, LAMP2, GLA, PRKAG2,
disorders         disorders: Pompe        PTPN11, FRDA, DMPK, mtDNA
                  (type II), Forbes
                  (type IIIa), Danon
                  (type IIb)
                  Lysosomal storage:      GLA
                  Infiltration:           Unknown
                  Familial amyloidosis
Coronary artery   Myocardial              PSCR1, SORT1, CELSR2, MYBPHL,
disease           infarction              MIA3, WDR12, MRAS, PHACTR1,
                                          LPA, SLC22A3, MTAP, CDKN2A,
                                          SDF1, SH2B3, HNF1A, PCSK9,
                                          ApoE, LDLR, ApoB
Hypertension      Arterial                SLC12A3, SLC12A1, KCNJ1,
                  hypertension            CLCNKB, NR3C2, SCNN1A,
                                          SCNN1B, SCNN1G CYP11B2,
                                          CYP11B1, HSD11B2, NR3C2,
                                          SCNN1B, SCNN1G, WNK1, WNK4,
                                          KLHL3, CUL3
Congenital                                NKZ2.5, NOTCH1, NOTCH2, FLT4,
heart disease                             NKX2-5, TBX5
Valvular heart    RHD                     MHC
disease           BAV                     NOTCH1
                  MVP                     DCHS1
Plasma LDL        LDL cholesterol         TRIB1, SORT1, GALNT2,
cholesterol       levels                  PPPIR3B, TTC39B, NPC1L1,
levels                                    HMGRCR, CETP

HCM = hypertrophic cardiomyopathy; DCM = dilated cardiomyopathy;
ARVC = arrhythmogenic right ventricular cardiomyopathy; LVNC = left
ventricular non-compaction; LQTS = long QT syndrome; SQTS = short QT
syndrome; CPVT = catecholaminergic polymorphic ventricular tachycardia;
LV = left ventricular; RHD = rheumatic heart disease; BAV = bicuspid
aortic valve; MVP = mitral valve prolapse; LDL = low-density
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Author:Laing, N.; Kraus, S.M.; Shaboodien, G.
Publication:SAMJ South African Medical Journal
Article Type:Report
Date:Jun 1, 2019
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