MINI REVIEW article

Front. Physiol., 27 May 2020

Sec. Cardiac Electrophysiology

Volume 11 - 2020 | https://doi.org/10.3389/fphys.2020.00557

From Genome-Wide Association Studies to Cardiac Electrophysiology: Through the Maze of Biological Complexity

  • 1. Department of Medical Biology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands

  • 2. Department of Experimental Cardiology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands

  • 3. Netherlands Heart Institute, Utrecht, Netherlands

  • 4. ICREC Research Program, Germans Trias i Pujol Health Science Research Institute, Badalona, Spain

  • 5. CIBERCV, Instituto de Salud Carlos III, Madrid, Spain

Abstract

Genome Wide Association Studies (GWAS) have provided an enormous amount of data on genomic loci associated with cardiac electrophysiology and arrhythmias. Clinical relevance, however, remains unclear since GWAS do not provide a mechanistic explanation for this association. Determining the electrophysiological relevance of variants for arrhythmias would aid development of risk stratification models for patients with arrhythmias. In this review, we give an overview of genetic variants related to ECG intervals and arrhythmogenic pathologies and discuss how these variants may influence cardiac electrophysiology and the occurrence of arrhythmias.

Introduction

Genome Wide Association Studies (GWAS) can identify genetic variants associated with phenotypic traits, such as electrocardiographic (ECG) intervals (Table 1). Interpretation of GWAS data relies on identification of the target gene affected by the novel discovered variant. Variants can be present in coding DNA, causing amino acid changes affecting protein function – or in non-coding DNA, altering the behavior of regulatory elements, thereby changing expression levels of its target gene(s) (Figure 1A). For many highly significant GWAS loci the gene causing the association with ECG intervals has not been identified yet. This is mostly because genes in or near these loci do not have a proven association with the phenotype. Overlaying such loci with publicly available genome wide data sets of gene expression (GTEx Consortium, 2013; Heinig, 2018), cardiac transcriptional elements (van Duijvenboden et al., 2016), genomic conformation Hi-C data sets (Montefiori et al., 2018), and other GWAS results will likely provide more insight into potential novel candidate genes regulating cardiac electrophysiology (van Ouwerkerk et al., 2019).

TABLE 1

Associated geneGCT
AF
AGBL41
AKAP61
ARHGAP101
ARHGAP26/NR3C11
ASAH11
ATXN116
BEST31
ClOorfll1
C10orf761
C20orfl661
C6orfl/NUDT31
C9orf31
CAND2113
CASC20/BMP2114
CASZ11
CDK61
CEP681
CGA/ZNF2921
COG51
CREB51
CUL4A1
CYTH11
DGKB1
DNAH101
DPF31
EPHA31
ERBB412526
FBN2/SLC27A61
FBRSL11
FBX0321
GCOM11
GJA513232
GOPC1
GORAB/PRRX1135
GTF2I1
GYPC1
HAND21
HIP1R1
HSPG2/CELA3B1
IGF1R13839
KCND3140
KCNJ514546
KCNN214748
KCNN3/PMVK15051
KDM1B1
KIF3C1
KRR1/PHLDA11
LHX31
LINC00208/GATA41
LINC00326/EYA41
LINC00540/BASP1P11
LINC00927/ARNT21
LOC1005063851
LOC102467213/EFNA51
LRRC74/IRF2BPL1
MAPT161
MBD51
METTL11B/LINC011421
MEX3C1
MIR30B1
MTSS1/LINC009641
MY018B1
NACA1
NEURL113
NUCKS11
OPN1SW1
PAK21
PITX2/C4orf3216969
PKP2170
POLR2A/TNFSF121
PPFIA41
PPP2R3A1
PRDM8/FGF51
PSMB71
PTK21
RBM20181
REEP1/KDM3A1
REEP31
RPS21
SCMH11
SIRT1123
SLC24A2/MLLT31
SLC9B11
SLIT31
SMAD71
SNRNP271
SNX6/CFL21
SORL1/MIR100HG1
SSPN1
SUN11
SYNE21
SYNP02L1
TEX411
THRB1
TUBA81
UBE4B1
USP31
UST1
WDR1115
WIPF1/CHRNA11
WNT8A/NME51
XPOl1
XP071
XXYLT11
YWHAE/CRK1
ZFHX312021
ZNF4621
ZPBP21
PR
ALDH18A1/SORBS129
ARHGAP2429–31
CCNL129
EFHA129
EOMES2934
EPS1529
FAT129
FERMT229
FGFR129
FIGN29
ID22937
KRTCAP229
LRCH129
MED13L29
MEIS129
MKLN129
MYBPHL2952
OBSCN29
PAM29
PDZRN329
SENP22957
SH3PXD2A/OBFC129
SKI29
SOX5295960
TMEM18229
WNT1129,31
XP0429
ZFPM229
HR
ACHE416364
B3GNT741
CD3441
CD4641
CHRM241
CPNE841
FADS141,42
FLRT241
FNDC3B41
RFX441
SLC12A941
TFPI4171
UfSpl41
HRV
NDUFA1165
NEOl65
PPIL165
RGS6657778
QRS
CRIMl7
DKK1783
HAND1/SAP30L7
HEATR5B/STRN7
IGFBP34
NFIA4
SETBP14
TKT/PRKCD/CACNA1D455
VTI1A4
QT
ANKRD97
ATP1B178
ATP2A27910
AZIN17
C30RF757
c6orf2047
CNOT17,1112
CREBBP7
FEN1/FADS27
GBF17
GFRA37
GMPR7
KCNE17,111617
KCNQ17,111819
LAPTM4B7
LIG37,11
LITAF7,11
MKL27
NCOA27
NOS1AP7,112223
RNF2077,112424
SLC4A4727
SLC8A172828
SMARCAD17
SP37
TCEA37
USP50/TRPM7733
AF+PR
CAMK2D1,293636
FRMD4B1,29
MYOCD1,29
NAV21,29
PHLDB21,29
TLE3/UACA1,29
AF+HR
GJAl1,41,424344
GNB41,41
MYH71,4249
AF+QT
KCNH21,7,115354
KCNJ2/CASC171,75556
SPATS2L1,7
AF+QRS
C1orfl85/RNF11/DKN2C/FAF11,4
CASQ21,45858
CDKN1A1,4,30
GOSR21,4
LRIG1/SLC25A261,4
PR+QRS
SNORD56B/SIPA1L14,29
TBX20/HERPUD24,2962
HR+HRV
GNG1141,65
KIAA175541,65
SYT1041,65
QRS+QT
KLF124,7
PRKCA4,7
HR+HRV+AF
HCN41,41,656666
HR+SSS+AF
MYH629,30,41,42,6768
HR+AF+PR
NKX2-5/BNIP11,29,31,417272
AF+PR+OT
CAV1/CAV21,7,29,317374
AF+PR+QRS
TBX5/TBX31,4,29,30,757676
PR+QRS+QT+AF
SCN5A/SCN10A/EXOG1,4,7,11,29,30,757980
HR+AF+PR+QT
TTN/CCDC1411,7,29,4182
HR+HRV+AF+PR
LINC00477/SOX5/BCAT11,29,41,42,655960
HR+AF+QRS+QT
C6orf204/SLC35F1/PLN/BRD7P31,4,7,41,428484

Level of functional studies for GWAS loci associated with ECG intervals.

Column G: GWAS reference. Column C: reference with electrophysiological experiments in cells. Column T reference with electrophysiological experiments in tissue. AF, atrial fibrillation; PR, PR interval; HR, heart rate; HRV, heart rate variability; QRS, QRS duration; QT, QT interval; SSS, sick sinus syndrome. 1Roselli et al., 2018; 2Vikram et al., 2017; 3Liu et al., 2016; 4Sotoodehnia et al., 2010; 5Zhang et al., 2005; 6Inoue et al., 2001; 7Arking et al., 2014; 8Barwe et al., 2009; 9Suarez et al., 2004; 10Shin et al., 2011; 11Newton-Cheh et al., 2009; 12Yamaguchi et al., 2018; 13Sinner et al., 2014; 14Howden et al., 2013; 15Huang et al., 2019; 16Mazhari et al., 2002; 17Drici et al., 1998; 18Moreno et al., 2015; 19Knollmann et al., 2004; 20Kao et al., 2016; 21Cheng et al., 2019; 22Kapoor et al., 2014; 23Sugiyama et al., 2016; 24Roder et al., 2014; 25Yang et al., 2019; 26Zhou et al., 2018; 27Myers et al., 2016; 28Pott et al., 2007; 29van Setten et al., 2018; 30Holm et al., 2010; 31Pfeufer et al., 2010; 32Noureldin et al., 2018; 33Sah et al., 2013; 34Pfeiffer et al., 2018; 35Tucker et al., 2017; 36Purohit et al., 2013; 37Moskowitz et al., 2007; 38Wang et al., 2014; 39Sapra et al., 2014; 40Lugenbiel et al., 2018; 41den Hoed et al., 2013; 42Eijgelsheim et al., 2010; 43Lübkemeier et al., 2013; 44Tuomi et al., 2011; 45Bender et al., 2001; 46Kelly et al., 2014; 47Li et al., 2009; 48Yi et al., 2015; 49Yang et al., 2018; 50Zhang et al., 2014; 51Mahida et al., 2014; 52Barefield et al., 2017; 53Orvos et al., 2019; 54Liu et al., 2017; 55Hattori et al., 2012; 56Zaritsky et al., 2001; 57Dustrude et al., 2013; 58Song et al., 2007; 59Zheng et al., 2002; 60Li et al., 2013; 61Rocher et al., 2010; 62Caballero et al., 2017; 63Amend et al., 2019; 64Masuda and Kawamura, 2003; 65Nolte et al., 2017; 66DiFrancesco and Borer, 2007; 67Holm et al., 2011; 68Herron et al., 2010; 69Syeda et al., 2016; 70Sato et al., 2009; 71Goldfarb et al., 1998; 72Li et al., 2019; 73Barbuti et al., 2012; 74Park et al., 2002; 75Smith et al., 2011; 76Dai et al., 2019; 77Posokhova et al., 2010; 78Posokhova et al., 2013; 79Berecki et al., 2010; 80Zhang et al., 2007; 81van den Hoogenhof et al., 2018; 82Ahlberg et al., 2018; 83Eldabah et al., 2018; 84Bai et al., 2013.

FIGURE 1

Upon discovery, the expression of the target gene is often modified in isolated cardiomyocytes (from an animal model or human induced pluripotent stem cells). These cellular models can be used to assess any electrophysiological phenotypes associated with the potential GWAS candidate. However, ECG intervals reflect the complex electrophysiological interaction of the cardiac cells and tissue structures, complicating extrapolation of single cell measurements to ECG intervals (Opthof et al., 1987). Additionally, discovered variants may affect expression of genes in non-cardiomyocytes, further complicating the interpretation of such experiments in isolated cardiomyocytes (Stroud et al., 2016; Veerman et al., 2016). To overcome these limitations, in vivo models such as transgenic mice are most commonly used to investigate the relation between GWAS variants and electrophysiological phenotypes. However, large electrophysiological differences exist between mice and larger mammals such as human (Boukens et al., 2014), which need to be taken into account when interpreting the resulting data.

In this review, we give an overview of genomic loci related to abnormal cardiac electrophysiology and discuss how these variants may directly or indirectly influence ECG intervals and the occurrence of arrhythmias through their expression pattern and regulation within the heart.

GWAS Loci Related to Heart Rate

The sinus node is located at the border between intercaval area and the right atrium. Action potentials of sinus node cardiomyocytes have low upstroke velocities and occur spontaneously (West, 1955). The mechanism underlying these spontaneous depolarizations is based on the funny current, mediated by the Hyperpolarization-Activated Cyclic Nucleotide-gated channel 4 (HCN4), and the calcium clock mediated by e.g., Ryanodine Receptor 2 (RYR2) and Phospholamban (PLN) (Lakatta and DiFrancesco, 2009). Accordingly, associations between the genetic loci of HCN4 and PLN and heart rate found by GWAS is most likely mediated through variants impacting on the expression and or function of these genes (Eijgelsheim et al., 2010; den Hoed et al., 2013; Nolte et al., 2017). Unfortunately, for the other GWAS loci, the association is not as straightforward. In the proximity of these loci, no genes with a known function in the SAN have been identified. These loci therefore potentially affect SAN function by impacting on other parameters, e.g., electrical coupling with the rest of the atrium and levels of fibrosis (Glukhov et al., 2013).

In smaller mammals – e.g., mice and rabbits – the sinus node is transmural (Bleeker et al., 1980; van Eif et al., 2019), whereas in large mammals – e.g., humans and dogs – it is not (Fedorov et al., 2009, 2010). The geometry of the sinus node affects coupling with atrial myocardium, which influences its electrophysiological behavior (Kirchhof et al., 1987). The delicate interaction between the sinus node and the surrounding atrial myocardium is established by fibrous tissue providing up to five exit pathways in large mammals (Figure 1B; Unudurthi et al., 2014; Csepe et al., 2015; Li et al., 2017). A transitional layer of cells within the exit pathways (Figure 1B) allows for spontaneous depolarization within the sinus node (Joyner and van Capelle, 1986; Kléber and Rudy, 2004). The identity of the transitional cells may depend on the expression of transcription factor Nkx2-5 and GWAS has found variants near NKX2-5 associating with heart rate (Mommersteeg et al., 2007b; den Hoed et al., 2013; Li et al., 2019). Within the exit pathways, high conductance connexins CX43 and CX40 (GJA1 and GJA5) are lower expressed toward the sinus node, whereas the low conductance CX45 is expressed higher toward the sinus node (Chandler et al., 2009, 2011; Allah et al., 2011). This gradual increase of conductance from sinus node toward atrial myocardium enables a proper current-to-load match. Therefore, genes involved in sinus node insulation and exit pathway formation are candidates for being associated with sinus node function (or heart rate) in GWAS. Accordingly, a locus near GJA1 is associated with heart rate in GWAS (den Hoed et al., 2013).

The electrotonic influence of atrial myocardium on sinus node function is important to consider when investigating the relevance of genes provided by GWAS. For instance, common and rare variants in MYH6 – a component of the sarcomere – are associated with heart rate and sick sinus syndrome (Holm et al., 2010, 2011). However, MYH6 is more abundantly expressed in the atrial myocardium than in the sinus node (van Eif et al., 2019). In vitro experiments in atrial-like cells indicate that mutations in MYH6 affect conduction velocity (Ishikawa et al., 2015). It is possible that conduction slowing in the atrium underlies the association between MYH6 and abnormal function of the sinus node. That genes expressed in the atrium but not in the sinus node can affect sinus is further illustrated by Cx40 knock-out mice in which the dominant pacemaker is not always the SAN (Bagwe et al., 2005). Moreover, mutations in SCN5A can result in sick sinus syndrome (Benson et al., 2003) despite the lack of SCN5A expression in the sinus node.

GWAS Loci Related to PR Interval

The PR interval is mainly determined by conduction through the atrioventricular junction (Meijler and Janse, 1988). The atrioventricular junction is a complex anatomical structure which was first described in 1906 by Sunao Tawara, who found cells forming a compact complex network but also small cells that joined into bundles (Tawara, 1906). These cells were later called compact nodal cells and transitional cells, respectively, and both have distinct electrophysiological properties (de Carvalho and de Almeida, 1960; Greener et al., 2011). A layer of transitional cells can be found in rings around the orifices of the AV valves where expression of Cx40 and SCN5A is low (Aanhaanen et al., 2010; Fedorov et al., 2011). This transitional ring lies on top of an atrioventricular nodal ring providing two conducting pathways with different electrophysiological characteristics both connecting the atria with the His bundle (Denes et al., 1973; Figure 1B). The presence of two conduction pathways illustrates the challenge of relating gene expression of single atrioventricular nodal cells to the electrophysiological phenotype of the atrioventricular node. Moreover, in the atrioventricular junction, not only the compact atrioventricular node contributes to atrioventricular delay but also all other cells present within the AV junction – e.g., transitional cells, fibroblasts and macrophages (Hulsmans et al., 2017). Similar to the sinus node, the function of the atrioventricular node depends on the interaction with these different cell-types.

The cells of the atrioventricular junction find their origin in the atrioventricular canal of the embryonic heart. Impulse conduction delay occurs in both the transitional cells and the compact atrioventricular node, therefore, normal development of the atrioventricular canal is crucial for atrioventricular delay in adult hearts (Meijler and Janse, 1988). Accordingly, GWAS for PR interval showed association with 18 of 44 loci which are related to heart development (van Setten et al., 2018), indicating that proper embryonic development is a crucial factor for adult AV conduction. Some of these loci are located near TBX2 and TBX3 – essential transcription factors controlling patterning of the atrioventricular canal during development (Holm et al., 2010; Pfeufer et al., 2010; Aanhaanen et al., 2011; van Setten et al., 2018; Table 1). Other loci are located nearby genes related to electrical function of the adult cardiomyocyte, like SCN5A and CAMK2D or cardiomyocyte contraction, like TTN and MYH6, however, the significance of these associations with PR interval await further investigation (Holm et al., 2010; van Setten et al., 2018).

GWAS Loci Related to QRS Duration

Total ventricular activation time is visualized on the ECG as the duration of the QRS complex, which comprises conduction in the His-Purkinje system and in the ventricular myocardium. The cardiac sodium channel Nav1.5 – encoded by SCN5A – is the major determinant of conduction in these tissues and, accordingly, QRS duration is associated with loci near SCN5A (Papadatos et al., 2002; Sotoodehnia et al., 2010). Variants in the SCN10A gene – encoding the neuronal sodium channel Nav1.8 – associate with QRS duration as well (Sotoodehnia et al., 2010). These variants are located within an enhancer region that modulates expression of SCN5A which could explain a relation with QRS duration (Sotoodehnia et al., 2010; van den Boogaard et al., 2014). Although variants in these region do not always associate with QRS prolongation (Behr et al., 2015). Other variants related to QRS duration are those near or within genes involved in bundle branch development and working myocardial phenotype, like TBX3, TBX5, TBX20, HAND1, DKK1, and NFIA (Moskowitz et al., 2007; Bakker et al., 2008; Singh et al., 2009; Sotoodehnia et al., 2010). In addition variants in calcium handling genes such as PLN, CACNA1D, STRN, PRKCA, and CASQ2, ATP2A2/ANAPC7 are associated with QRS duration (Sotoodehnia et al., 2010; van Setten et al., 2019). These variants could affect calcium homeostasis resulting in reduced sodium current and slow conduction and thereby prolong QRS duration (King et al., 2013).

The Purkinje network activates the ventricular myocardium via Purkinje muscle junctions composed of transitional cells connecting the Purkinje fibers to ventricular cardiomyocytes (Figure 1B; Martinez-Palomo et al., 1970; Tranum-Jensen et al., 1991). Similar to the AV node and the SAN, the connection of Purkinje cardiomyocytes with ventricular cardiomyocytes requires high resistance – preventing current-to-load mismatch (Rohr et al., 1997). We expect that reduction of electrical coupling in these junctions – by e.g., lower expression of CX40 – will delay ventricular activation, which prolongs QRS duration. Purkinje fibers develop from embryonic trabeculae (Jensen et al., 2012) where e.g., Cx40, Scn5a, CnTn2 are abundantly present. The primordial Purkinje trabeculae require further specialization after birth under influence of Nkx2-5 and Irx3 expression and Notch signaling (Zhang et al., 2011; Rentschler et al., 2012). Homozygous Irx3 loss-of-function mice have slowed conduction in Purkinje fibers (Koizumi et al., 2016). We expect that genetic variations affecting expression of these factors – that are involved in development and maturation of the Purkinje fibers – will relate to total ventricular activation time and thereby QRS duration in GWAS.

GWAS Loci Related to QT Interval

The QT interval is a measure of ventricular repolarization and reflects the time between the first moment of activation to the last moment of repolarization. Accordingly, the QT interval relates to action potential duration (APD) but also to differences in regional conduction velocity. Conduction slowing in regions that repolarize late prolongs QT interval whereas conduction slowing in areas that repolarize early may not affect QT interval. The QT interval has an inverse relation with heart rate. This inverse relation results from shortening of the APD at higher rates due to activation of the slowly delayed rectifier current IKs (Boyett and Jewell, 1978; Carmeliet, 2006). QT intervals corrected for heart rate – QTc – or QT interval measured at similar heart rates may increase sensitivity for finding variants related to repolarization (Bazett, 1920). In mice, however, QT interval does not depend on heart rate. Therefore, QT interval – not QTc – should be used as measure for ventricular repolarization in mice (Speerschneider and Thomsen, 2013).

Genome Wide Association Studies for QT interval identified 22 loci (Table 1) of which many are in or near genes encoding for potassium channels or involved in calcium handling (Arking et al., 2014). Increased potassium current shortens QT interval whereas increased calcium current prolongs QT interval (Wemhöner et al., 2015; Landstrom et al., 2016). Nitric Oxide Synthase 1 Adaptor Protein (NOS1AP) – regulating calcium current – is associated with QT interval in GWAS (Arking et al., 2006, 2014). Genetic variation within the NOS1AP gene affects QT interval and is related to arrhythmogenesis in patients with the long QT syndrome (Crotti et al., 2009; Tomás et al., 2010). The effect of NOS1AP on QT interval, however, could also have an extracardiac pathway as its expression is high in brain (Xu et al., 2005) providing the possibility of NOS1AP to affect autonomic modulation of the QT interval (Yagishita et al., 2015).

GWAS Loci Related to Arrhythmias

Atrial Arrhythmias

Atrial fibrillation (AF) is the most common arrhythmia and the prevalence increases with age (Krijthe et al., 2013). AF results from an interplay between electrical (triggers and reentry), structural and hemodynamic remodeling (Schotten et al., 2011). The combination of these pathophysiological changes sets the stage for AF.

The trigger for AF is commonly near the connection between the left atrium and pulmonary veins (PVs) (Haïssaguerre et al., 1998). In humans – at this connection – the four PVs are enclosed by an outer sleeve of myocardium (Saito et al., 2000). PV cardiomyocytes are morphologically similar to atrial cardiomyocytes, but have a different developmental history (Verheule et al., 2002; Mommersteeg et al., 2007a). The formation of PV myocardium in mice highly depends on expression of Pitx2 and Nkx2-5 during development (Mommersteeg et al., 2007a). GWAS in more than 65 thousand AF patients identified 97 loci (Roselli et al., 2018) – including PITX2 and NKX2-5, suggesting a relation of AF with PV formation during development. The myocardial sleeves of the PVs are thinner distal to the left atrium and end in single cardiomyocyte protrusions in the PV (Figure 1B; Verheule et al., 2002). These PV myocytes have a high resting membrane potential and low expression of inward rectifier channels (Ehrlich et al., 2003; Melnyk et al., 2005), setting the stage for spontaneous activity. Accordingly, variants near genes encoding inward rectifier channels – e.g., KCNJ2, KCNJ5 – are associated with AF (Christophersen et al., 2017; Roselli et al., 2018).

Electrical remodeling is a cause and effect of reentry circuits that maintain AF. Reentry is facilitated by slow conduction and short APD (Wiener and Rosenblueth, 1946). Accordingly, GWAS for AF identified six genes encoding for potassium channels all involved in atrial APD: KCND3, KCNH2, KCNN2, and KCNN3 (Roselli et al., 2018). Genes encoding channels involved in conduction – e.g., GJA1, GJA5, SCN5A, and SCN10A are also associated with AF (Christophersen et al., 2017; Roselli et al., 2018). Moreover, transcription factors involved in spatiotemporal expression of these ion channels – TBX3, TBX5, and PITX2 – associate with AF as well (Tao et al., 2014; Nadadur et al., 2016). Novel findings have indicated that not only genes encoding ion channel are related to AF. Titin (TTN) – a large sarcomere protein – is associated with early onset AF (Choi et al., 2018). TTN dysfunction may predispose to AF by increasing myocardial fibrosis and prolonging PR interval, which are both associated with increased risk for AF (Ahlberg et al., 2018).

Ventricular Arrhythmias

Up to 80% of sudden cardiac arrests (SCA) are caused by acute ischemia resulting from coronary artery disease (Huikuri et al., 2001; Fishman et al., 2010). GWAS in patients with coronary artery disease identified 11 variants related to SCA of which eight were near or within genes related to long QT syndrome (Marsman et al., 2014). It is unclear whether these variants predispose to arrhythmias in general or only in the setting of coronary artery disease. A gene that does specifically relate to arrhythmias in the setting of acute myocardial ischemia is CXADR which encodes the CXADR Ig-like cell adhesion molecule (previously named Coxsackie and adenovirus receptor, CAR) (Bezzina et al., 2010). Reduced expression of CAR lowers sodium channel availability – thereby reducing conduction velocity – and facilitates reentry arrhythmias in the setting of ischemia (Marsman et al., 2014). Non-ischemia induced arrhythmias explain 20% of SCA and comprise a variety of arrhythmogenic syndromes which can be related to structural abnormal myocardium or genetic mutations (John et al., 2012). GWAS for SCA identified several genes which all associate with risk factors for SCA such as QRS duration and QT interval (Adabag et al., 2010; Arking et al., 2011; Milano et al., 2016; Ashar et al., 2018).

Genome Wide Association Studies for syncope – which is a common symptom of many arrhythmogenic syndromes – identified a genetic variant close in proximity to the gene zinc finger protein 804a (ZNF804A) (Hadji-Turdeghal et al., 2020). Whether a role of ZNF804A in cardiac arrhythmias or e.g., blood pressure regulation explains this association remains to be investigated.

Ventricular fibrillation is associated with IRX3 (Koizumi et al., 2016), which plays a role in conduction in Purkinje fibers. Ablation of Purkinje muscle junctions is a successful treatment for a subset of patients with idiopathic ventricular fibrillation indicating a role of Purkinje cells in these arrhythmias (Haïssaguerre et al., 2002).

Understanding arrhythmogenic mechanisms can guide interpretation of GWAS derived data. This is exemplified by the Brugada syndrome, which is characterized by ST segment elevation in the right precordial leads and ventricular arrhythmias. Initially, the Brugada syndrome was considered an ion channel disease mainly resulting from dysfunction of the cardiac sodium channel. However, only 20% of patients present with mutations in SCN5A (Antzelevitch et al., 2005). A GWAS in Brugada syndrome patients identified several variants in regulatory DNA controlling SCN5A expression (Bezzina et al., 2013). In order to discover causal variants in GWAS with limited number of patients, knowledge on the arrhythmogenic mechanism of the disease is helpful. Mechanistic studies in hearts from Brugada syndrome patients have suggested that arrhythmias occur due to conduction block in the presence of subtle structural abnormalities (Coronel et al., 2005; Hoogendijk et al., 2010). This suggests that variants near genes involved in the formation of fibrosis (TGFB2) or genes affecting safety factor of cardiac conduction (e.g., SCN5A, GJA1, KCHIP1) are important variants to further investigate.

Conclusion

Most of the variants provided by GWAS lie near or within expected candidate genes potentially explaining the phenotype they are associated with. However, the electrophysiological characterization of the vast majority of associated genes has not shown direct effects on ECG intervals nor on arrhythmia susceptibility. In this review, we emphasize that mechanistic knowledge of the structure-function relations underlying ECG intervals and arrhythmias should be considered when interpreting experimental characterization of these variants, in order to guide clinical applicability of GWAS data.

Statements

Author contributions

KS and BB designed and wrote the manuscript. VM, CG-M, and EL critically revised the manuscript.

Funding

This work was supported by the Dutch Heart Foundation (2016T047 to BB), the Dutch Research Council (NWO Talent Scheme VIDI-91718361 to EL), and the CVON (Dutch cardiovascular allowance RESCUED project to EL).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Summary

Keywords

GWAS, cardiac electrophysiology, arrhythmias, gene expression, genetics

Citation

Scholman KT, Meijborg VMF, Gálvez-Montón C, Lodder EM and Boukens BJ (2020) From Genome-Wide Association Studies to Cardiac Electrophysiology: Through the Maze of Biological Complexity. Front. Physiol. 11:557. doi: 10.3389/fphys.2020.00557

Received

26 February 2020

Accepted

04 May 2020

Published

27 May 2020

Volume

11 - 2020

Edited by

Marcel van der Heyden, University Medical Center Utrecht, Netherlands

Reviewed by

Marina Cerrone, New York University School of Medicine, United States; Morten Salling Olesen, Rigshospitalet, Denmark

Updates

Copyright

*Correspondence: Bastiaan J. Boukens,

This article was submitted to Cardiac Electrophysiology, a section of the journal Frontiers in Physiology

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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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