Helsinki University Biomedical Dissertations No. 179 [602180]

Helsinki University Biomedical Dissertations No. 179
GENETIC VARIANTS PREDISPOSING TO
CARDIAC ARRHYTHMIA DISORDERS
AND SUDDEN CARDIAC DEATH
Annukka Lahtinen
Research Programs Unit, Molecular Medicine
and
Institute of Clinical Medicine, Department of Medicine
University of Helsinki
Helsinki, Finland
ACADEMIC DISSERTATION
To be publicly discussed, with the permission of
the Faculty of Medicine, University of Helsinki,
in Lecture Hall 2, Biomedicum Helsinki, Haartmaninkatu 8,
on December 5th, 2012, at 12 noon.
Helsinki 2012

2Supervisors Annukka Marjamaa, MD, PhD
Department of Medicine
University of Helsinki
Helsinki, Finland
Professor Kimmo Kontula, MD, PhD
Department of Medicine
University of Helsinki
Helsinki, Finland
Reviewers Docent Juhani Junttila, MD, PhD
Department of Internal Medicine
University of Oulu and
Oulu University Hospital
Oulu, Finland
Docent Samuli Ripatti, PhD
Institute for Molecular Medicine Finland
FIMM
University of Helsinki
Helsinki, Finland
Opponent Professor Antti Sajantila, MD, PhD
Department of Forensic Medicine
Hjelt Institute
University of Helsinki
Helsinki, Finland
ISSN 1457-8433
ISBN 978-952-10-8381-5 (paperback)
ISBN 978-952-10-8382-2 (PDF)
http://ethesis.helsinki.fi
Helsinki University Print
Helsinki 2012

TABLE OF CONTENTS
3TABLE OF CONTENTS
LIST OF ORIGINAL PUBLICATIONS ………………………………………………………………….5
ABBREVIATIONS …………………………………………………………………………………………………6
ABSTRACT ……………………………………………………………………………………………………………8
INTRODUCTION …………………………………………………………………………………………………10
REVIEW OF THE LITERATURE ………………………………………………………………………..12
1. Molecular basis of inherited cardiac arrhythmia disorders …………………………………….12
1.1. Cardiomyopathies……………………………………………………………………………………..12
1.2. Cardiac ion channel disorders……………………………………………………………………..13
2. Arrhythmogenic right ventricular cardiomyopathy (ARVC)………………………………….17
2.1. Cell-cell junctions of cardiomyocytes ………………………………………………………….17
2.2. Clinical features of ARVC …………………………………………………………………………19
2.3. Genetics of ARVC…………………………………………………………………………………….22
2.4. Syndromic forms of ARVC………………………………………………………………………..26
3. Long QT syndrome (LQTS) ……………………………………………………………………………..27
3.1. Cardiac ion channels …………………………………………………………………………………27
3.2. Clinical features of LQTS ………………………………………………………………………….28
3.3. Genetics of LQTS……………………………………………………………………………………..30
3.4. Genetics of QT interval and LQTS modifier genes………………………………………..33
4. Sudden cardiac death (SCD)……………………………………………………………………………..35
4.1. Epidemiology and clinical risk factors of SCD ……………………………………………..35
4.2. Genetics of SCD……………………………………………………………………………………….37
5. Methods of studying the genetics of cardiac arrhythmia and SCD………………………….40
5.1. Candidate gene approach……………………………………………………………………………40
5.2. Linkage and association studies ………………………………………………………………….41
5.3. Future directions……………………………………………………………………………………….42
AIMS OF THE STUDY …………………………………………………………………………………………43
MATERIALS AND METHODS …………………………………………………………………………….44
1. Patient and control samples (I-III) ……………………………………………………………………..44
2. Population cohorts and autopsy materials (II, IV-VI)……………………………………………44
3. Phenotypic characterization (I-VI) …………………………………………………………………….45
4. Molecular genetic studies (I-VI)………………………………………………………………………..46
5. Microscopic analyses (I, II) ………………………………………………………………………………47
6. Statistical analyses (I-VI) …………………………………………………………………………………48
RESULTS …………………………………………………………………………………………………………….50
1. Desmosomal mutations in ARVC patients and families………………………………………..50
2. Effects of desmosomal mutations at the cellular level…………………………………………..53
3. Desmosomal variants in the Finnish population…………………………………………………..55
4.KCNE1 D85N as a sex-specific disease-modifying variant in LQTS………………………56
5. QT interval and QT score in SCD …………………………………………………………………………58
6. Common variants and cardiovascular risk factors in SCD …………………………………….59
7. Rare arrhythmia-associated mutations in the Finnish population……………………………62

TABLE OF CONTENTS
4DISCUSSION ………………………………………………………………………………………………………. 63
1. Desmosomal defects underlying Finnish ARVC…………………………………………………. 63
1.1. Desmosomal mutations and their cellular consequences ……………………………….. 63
1.2. Desmosomal mutations at the population level…………………………………………….. 65
2. Common genetic variants modulating QT interval and LQTS phenotype ………………. 66
2.1. Genetic components of QT interval ……………………………………………………………. 66
2.2. Modifier genes in LQTS …………………………………………………………………………… 67
3. Genes, QT interval, and SCD…………………………………………………………………………… 68
4. Genetic arrhythmia susceptibility variants in SCD………………………………………………. 69
4.1. Common genetic variants and SCD ……………………………………………………………. 69
4.2. Rare arrhythmia-associated mutations and SCD…………………………………………… 71
5. SCD risk prediction………………………………………………………………………………………… 72
6. Study limitations…………………………………………………………………………………………….. 73
CONCLUSIONS ………………………………………………………………………………………………….. 74
ACKNOWLEDGEMENTS ………………………………………………………………………………….. 75
REFERENCES ……………………………………………………………………………………………………. 77

LIST OF ORIGINAL PUBLICATIONS
5LIST OF ORIGINAL PUBLICATIONS
The thesis is based on the following original publications, which are referred to in the text
by Roman numerals I-VI. In addition, some unpublished data are presented.
I Lahtinen AM *, Lehtonen A*, Kaartinen M, Toivonen L, Swan H, Widén E,
Lehtonen E, Lehto VP, Kontula K. Plakophilin-2 missense mutations in
arrhythmogenic right ventricular cardiomyopathy. International Journal of
Cardiology 2008; 126(1):92-100.
II Lahtinen AM , Lehtonen E, Marjamaa A, Kaartinen M, Heliö T, Porthan K,
Oikarinen L, Toivonen L, Swan H, Jula A, Peltonen L, Palotie A, Salomaa
V, Kontula K. Population-prevalent desmosomal mutations predisposing to
arrhythmogenic right ventricular cardiomyopathy. Heart Rhythm 2011;
8(8):1214-1221.
III Lahtinen AM , Marjamaa A, Swan H, Kontula K. KCNE1 D85N
polymorphism – a sex-specific modifier in type 1 long QT syndrome?
BMC Medical Genetics 2011; 12:11.
IV Noseworthy PA*, Havulinna AS*, Porthan K, Lahtinen AM , J u l a A ,
Karhunen PJ, Perola M, Oikarinen L, Kontula KK, Salomaa V, Newton-
Cheh C. Common genetic variants, QT interval, and sudden cardiac death
in a Finnish population-based study. Circulation Cardiovascular Genetics
2011; 4(3):305-311.
V Lahtinen AM *, Noseworthy PA*, Havulinna AS, Jula A, Karhunen PJ,
Kettunen J, Perola M, Kontula K, Newton-Cheh C, Salomaa V. Common
genetic variants associated with sudden cardiac death: the FinSCDgen
study. PloS One 2012; 7(7):e41675.
VI Lahtinen AM , Havulinna AS, Noseworthy PA, Jula A, Karhunen PJ, Perola
M, Newton-Cheh C, Salomaa V, Kontula K. Prevalence of arrhythmia-
associated gene mutations and risk of sudden cardiac death in the Finnish
population. Submitted.
* Equal contribution.
The original publications are reproduced with the permission of the copyright holders.

ABBREVIATIONS
6ABBREVIATIONS
ARVC arrhythmogenic right ventricular cardiomyopathy
ARVD arrhythmogenic right ventricular dysplasia
CACNA1C voltage-gated L-type calcium channel Į1C subunit gene
CASQ2 calsequestrin 2 gene
CDKN2A, 2B cyclin-dependent kinase inhibitor 2A and 2B genes
CHD coronary heart disease
CI confidence interval
CPVT catecholaminergic polymorphic ventricular tachycardia
DSC2 desmocollin-2 gene
DSG2 desmoglein-2 gene
DSP desmoplakin gene
ECG electrocardiogram
GPD1L glycerol-3-phosphate dehydrogenase 1-like gene
GWA genome-wide association
HR hazard ratio
HSDS Helsinki Sudden Death Study
ICa,L L-type calcium current
IK1 inward rectifier potassium current
IKr rapidly activated delayed rectifier potassium current
IKs slowly activated delayed rectifier potassium current
INa sodium current
INCX sodium-calcium exchanger current
Ito transient outward potassium current
JUP plakoglobin gene
KCNE1, 2, 3 voltage-gated potassium channel, Isk-related family, member 1, 2, and 3
genes
KCNH2 voltage-gated potassium channel, subfamily H (eag-related), member 2
gene
KCNJ2, 5 inwardly-rectifying potassium channel, subfamily J, member 2 and 5
genes
KCNQ1 voltage-gated potassium channel, KQT-like subfamily, member 1 gene
LQTS long QT syndrome
minK voltage-gated potassium channel, subfamily E, member 1
MiRP1 minK-related peptide 1
MLPA multiplex ligation-dependent probe amplification
NOS1AP nitric oxide synthase 1 (neuronal) adaptor protein gene
PCR polymerase chain reaction
PIRA primer-induced restriction analysis
PITX2 paired-like homeodomain 2 gene
PKP2 plakophilin-2 gene
PLN phospholamban gene
QTc QT interval corrected for heart rate according to Bazett’s formula
QT Nc QT interval nomogram-corrected for heart rate
QT score QT genotype score calculated for each individual to aggregate the genetic
information of a number of QT interval-prolonging variants
RR relative risk
RYR2 cardiac ryanodine receptor gene
SCD sudden cardiac death

ABBREVIATIONS
7SCN1B voltage-gated sodium channel, type I, ȕ subunit gene
SCN5A voltage-gated sodium channel, type V, Į subunit gene
SNP single nucleotide polymorphism
TASTY Tampere Autopsy Study
TGFB3 transforming growth factor ȕ3 gene
TMEM43 transmembrane protein 43 gene
WDR48 WD repeat domain 48 gene
In addition, standard one-letter abbreviations are used for nucleotides and amino acids.

ABSTRACT
8ABSTRACT
Arrhythmogenic right ventricular cardiomyopathy (ARVC) and long QT syndrome (LQTS)
are inherited cardiac arrhythmia disorders that predispose to ventricular tachycardia and
sudden cardiac death (SCD). In ARVC, structural and electrical abnormalities of the heart
occur together with progressive replacement of the right ventricular myocardium by adipose
and fibrous tissue. Mutations in desmosomal cell adhesion genes are estimated to account
for approximately half of all ARVC cases. LQTS is a cardiac channelopathy manifesting
with a prolonged QT interval in a structurally normal heart. Disease-causing mutations delay
the repolarization of the ventricular myocardium by disturbing the function of cardiac ion
channels. The aims of this study were to identify genetic variants predisposing to ARVC,
LQTS, and SCD and to assess their prevalence and clinical significance in the Finnish
population.
A total of 33 ARVC probands were screened for mutations in desmosomal genes by direct
sequencing. Six mutations, five not previously reported in ARVC, were identified in 18% of
the cases. Immunohistochemistry and electron microscopy revealed disorganization of the
intercalated disk structure of mutation carriers, but ARVC families demonstrated reduced
disease penetrance. The combined carrier frequency of the desmosomal mutations identified
in this study was 1:250 in four Finnish population cohorts (total n = 27 670). One in 340
individuals in the general population carried the Finnish ARVC founder mutation PKP2
Q59L. Compared with the proposed ARVC population prevalence of 1:1000-1:5000, an
unexpectedly large number of individuals could be at risk of developing ARVC, and thus,
potentially life-threatening arrhythmias in Finland. However, another trigger is likely to be
needed for disease expression.
KCNE1 D85N is associated with a 10-ms QT interval prolongation in the general
population. To study its effect on the LQTS phenotype, its presence was assayed in 712
carriers of the four Finnish LQTS founder mutations KCNQ1 G589D, KCNQ1 IVS7-2A>G,
KCNH2 L552S, and KCNH2 R176W. KCNE1 D85N was associated with a 26-ms
prolongation of QT interval in males with KCNQ1 G589D, representing thus a potential sex-
specific disease-modifying factor in LQTS.

ABSTRACT
9Associations between 14 QT-prolonging single nucleotide polymorphisms (SNPs), QT
interval, and SCD were investigated in two Finnish population cohorts (total n = 6808). The
QT score aggregating the genetic information of the 14 QT-associated SNPs explained 8.6% of
the variation in QT interval. A 10-ms prolongation of QT interval was associated with a
19% increased risk of SCD, and the association between a diagnostic QT interval threshold
(>450 ms in males and >470 ms in females) and risk of SCD was verified. No association
between QT score and risk of SCD was, however, observed.
The association of 28 common and 10 rare candidate gene variants with SCD was studied in
four Finnish population samples and two series of forensic autopsies (total n = 28 323). Two
novel common variants, rs41312391 in SCN5A and rs2200733 in 4q25 near PITX2 , were
associated with risk of SCD. In addition, the associations for rs2383207 in 9p21 as well as
for clinical risk factors for coronary heart disease were replicated. Rare arrhythmia-
associated mutations in desmosomal and ion channel genes had a combined carrier
frequency of 1:130 in the Finnish population and were detected in individual SCD victims.
In conclusion, the high prevalence and reduced disease penetrance of desmosomal mutations
should be considered in counselling of ARVC patients and family members. In LQTS,
KCNE1 D85N provides a potential sex-specific disease-modifying factor for risk
stratification. In addition, two novel genetic risk markers for SCD were identified in this
study, providing novel information for SCD risk prediction and prevention.

INTRODUCTION
10INTRODUCTION
Cardiac arrhythmia disorders present a major risk factor for cardiac arrest and sudden
cardiac death (SCD). Disturbance of heart rhythm may occur due to structural or electrical
heart disease or non-cardiac causes. Inherited forms of arrhythmia disorders are rare but
often severe, and they are involved in a significant proportion of premature sudden deaths of
young adults (Cross et al. 2011). Cardiomyopathies, i.e. disorders of the cardiac muscle, and
channelopathies, i.e. ion channel disorders, represent the major forms of inherited cardiac
arrhythmia disorders.
Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited arrhythmia
disorder characterized by progressive adipose and fibrous tissue replacement of the right
ventricular myocardium (Marcus et al. 2010). It is a common cause of SCD in young
athletes (Thiene et al. 1988). Since the initial description of ARVC, then termed right
ventricular dysplasia (Frank et al. 1978), several pathogenic theories have been suggested.
The first disease locus was identified in 1994 in a linkage study (Rampazzo et al. 1994), but
the reduced penetrance and variable expressivity of ARVC have complicated the discovery
of disease-causing mutations. Autosomal recessive cardiocutaneous syndromes helped in the
establishment of desmosomal gene mutations underlying ARVC (McKoy et al. 2000).
Thereafter, several desmosomal cell adhesion genes have been associated with the
autosomal dominant form of this disorder, but the exact prevalence and significance of these
mutations at the population level have not previously been assessed.
Long QT syndrome (LQTS) is a cardiac channelopathy, first described in the 1950s, when
patients with prolonged QT interval and increased risk of SCD associated with congenital
deafness were documented (Jervell and Lange-Nielsen 1957). The first ion channel genes
involved in LQTS were discovered in 1995 using linkage mapping (Curran et al. 1995,
Wang et al. 1995). Today, 13 loci affecting the function of cardiac ion channels have been
shown to be associated with LQTS. However, as the disease-causing mutations show a
significantly reduced penetrance (Priori et al. 1999), current research efforts have focused
on additional disease-modifying factors in the risk stratification of LQTS.
Approximately half of all cardiovascular deaths occur suddenly (Fox et al. 2004a). In
addition to cardiomyopathies and primary electrical disorders of the heart, coronary heart

INTRODUCTION
11disease (CHD) is the most common disorder underlying SCD (Chugh et al. 2008). For
example, myocardial infarction may provide a substrate for ventricular tachycardia, which
may lead to ventricular fibrillation and cardiac arrest. Risk of SCD is heritable (Jouven et al.
1999, Friedlander et al. 2002), but the genetic variants conveying susceptibility are largely
unknown. Recent advances in molecular genetics have enabled the use of genome-wide
approaches in the discovery of novel candidate genes for SCD (Alders et al. 2009, Arking et
al. 2010, Bezzina et al. 2010, Arking et al. 2011).
The aims of the present study were to identify genetic variants predisposing to cardiac
arrhythmia disorders ARVC and LQTS and to assess the prevalence of arrhythmia
susceptibility variants and their association with risk of SCD in the Finnish population.

REVIEW OF THE LITERATURE
12REVIEW OF THE LITERATURE
1. Molecular basis of inherited cardiac arrhythmia disorders
1.1. Cardiomyopathies
Cardiomyopathies are disorders of the cardiac muscle that may cause heart failure,
ventricular arrhythmias, and sudden cardiac death (SCD). Inheritable cardiomyopathies are
divided into five distinct disease entities: dilated cardiomyopathy, hypertrophic
cardiomyopathy, arrhythmogenic right ventricular cardiomyopathy (ARVC), restrictive
cardiomyopathy, and left ventricular noncompaction cardiomyopathy (Watkins et al. 2011).
Their inheritance is often autosomal dominant, but also autosomal recessive, X-linked, and
mitochondrial inheritance have been reported (Watkins et al. 2011). Various mutations in a
single gene may underlie different disease phenotypes, and even within families, carriers of
the same mutation may suffer from different types of cardiomyopathies (Mogensen et al.
2003).
Dilated cardiomyopathy manifests with left ventricular dilatation, systolic dysfunction, and
myocardial fibrosis. Many disease genes have been identified and they involve distinctive
cellular functions, such as nuclear envelope (lamin A and C) (Fatkin et al. 1999), sarcomere
structure ( ȕ-myosin heavy chain, troponin T, actin) (Olson et al. 1998, Kamisago et al.
2000), force transduction (Cypher/ZASP) (Vatta et al. 2003), cytoskeleton (desmin, į-
sarcoglycan) (Li et al. 1999, Tsubata et al. 2000), cell adhesion (desmoplakin, metavinculin)
(Norgett et al. 2000, Olson et al. 2002), calcium handling (phospholamban) (Haghighi et al.
2003, Schmitt et al. 2003), transcription (Schönberger et al. 2005), and messenger RNA
splicing (Brauch et al. 2009). Despite their functional divergence, many of these mutations
lead to impaired generation or transmission of force and ultimately protein and organelle
degradation and apoptosis (Watkins et al. 2011).
Patients with hypertrophic cardiomyopathy show left ventricular hypertrophy, often
involving the interventricular septum, and impaired diastolic relaxation. Characteristic
features also include myocyte disarray and fibrosis. Disease-causing mutations have been
detected in genes encoding sarcomeric proteins (e.g. ȕ-myosin heavy chain, cardiac myosin-
binding protein C, cardiac troponin T, and Į-tropomyosin) (Geisterfer-Lowrance et al. 1990,
Thierfelder et al. 1994, Bonne et al. 1995, Watkins et al. 1995), and genes involved in

REVIEW OF THE LITERATURE
13energy sensing ( Ȗ2 subunit of adenosine monophosphate-activated protein kinase) (Blair et
al. 2001) and production (mitochondrial transfer RNAs) (Merante et al. 1994) and myogenic
differentiation (muscle LIM protein) (Geier et al. 2008). In contrast to the sarcomeric
mutations observed in dilated cardiomyopathy, those associated with hypertrophic
cardiomyopathy cause increased contractility and energy consumption (Watkins et al. 2011).
The alterations in cardiomyocyte energetics, calcium handling, and signalling pathways
ultimately lead to reduced myocyte relaxation and increased myocyte growth (Watkins et al.
2011).
Restrictive cardiomyopathy presents with reduced ventricular diastolic volume without
abnormalities in systolic function and cardiac morphology. A single sarcomeric mutation in
cardiac troponin I may lead to either restrictive or hypertrophic cardiomyopathy (Mogensen
et al. 2003). Sarcomeric mutations associated with restrictive cardiomyopathy have also
been reported in troponin T (Peddy et al. 2006), Į-cardiac actin (Kaski et al. 2008), and ȕ-
myosin heavy chain (Karam et al. 2008). Desmin mutations may be detected in patients with
both skeletal and cardiac myopathy (Goldfarb et al. 1998, Arbustini et al. 2006).
Clinical findings in left ventricular noncompaction cardiomyopathy are trabeculations of the
left ventricular myocardium and segmental left ventricular wall thickening due to thickened
endocardial layer and thin epicardial layer. Mutations in sarcomeric proteins ( Į-cardiac
actin, ȕ-myosin heavy chain, and cardiac troponin T) may cause this disorder as well as
other types of cardiomyopathies (Hoedemaekers et al. 2007, Klaassen et al. 2008). Disease-
associated mutations have also been detected in the cytoskeletal protein Į-dystrobrevin
(Ichida et al. 2001), as well as in lamin A and C (Hermida-Prieto et al. 2004), Cypher/ZASP
(Vatta et al. 2003), and taffazin (Ichida et al. 2001) proteins.
1.2. Cardiac ion channel disorders
Channelopathies, i.e. ion channel disorders, are caused by mutations in genes encoding ion
channels, their subunits, or associated regulatory proteins. In contrast to cardiomyopathies,
manifesting with structural changes of the heart, cardiac channelopathies involve mainly
electrical instability of the heart, predisposing to ventricular tachyarrhythmias and SCD. The
inheritance is usually autosomal dominant or recessive in nature, but the penetrance may be
variable (e.g. Swan et al. 1999a, Lahat et al. 2001). Cardiac channelopathies include long

REVIEW OF THE LITERATURE
14QT syndrome (LQTS), short QT syndrome, Brugada syndrome, catecholaminergic
polymorphic ventricular tachycardia (CPVT), and atrial fibrillation (Figure 1). In addition,
mutations in the cardiac sodium channel gene SCN5A may cause progressive cardiac
conduction defect (Schott et al. 1999), sick sinus syndrome (Benson et al. 2003), dilated
cardiomyopathy (Bezzina et al. 2003a), and idiopathic ventricular fibrillation (Akai et al.
2000). Also a mutation in the potassium channel gene KCNJ8 has been identified in a
patient with ventricular fibrillation and early repolarization (Haïssaguerre et al. 2009).
Figure 1. Spectrum of cardiac channelopathies caused by different types of ion channel mutations. AF =
atrial fibrillation; BrS = Brugada syndrome; CCD = cardiac conduction defect; CPVT = catecholaminergic
polymorphic ventricular tachycardia; IVF = idiopathic ventricular fibrillation; JLN = Jervell and Lange-
Nielsen syndrome; LQT = long QT syndrome; SQT = short QT syndrome; SSS = sick sinus syndrome.
Adapted from Ruan et al. 2009.
Short QT syndrome is characterized by short QT interval and tall and peaked T waves in
electrocardiogram (ECG) (Gussak et al. 2000). This disorder of shortened cardiac
repolarization predisposes to atrial fibrillation, ventricular tachycardia, and SCD (Gaita et al.
2003). Causative mutations have been reported in the potassium channel genes KCNH2
(Brugada et al. 2004), KCNQ1 (Bellocq et al. 2004), and KCNJ2 (Priori et al. 2005). In short
QT syndrome, the potassium channel mutations cause a gain-of-function defect, whereas
loss of function of the same channels may lead to long QT syndrome, manifesting with
prolonged cardiac repolarization (Curran et al. 1995, Wang et al. 1996, Plaster et al. 2001).

REVIEW OF THE LITERATURE
15Brugada syndrome presents with elevated ST segments and inverted T waves in the right
precordial leads of ECG, associated with increased risk of ventricular fibrillation and SCD
(Brugada and Brugada 1992). Loss-of-function mutations in SCN5A have been reported in
approximately 20% of Brugada syndrome patients (Chen et al. 1998, Kapplinger et al.
2010). Mutations in GPD1L gene encoding glycerol-3-phosphate dehydrogenase 1-like
protein may also cause Brugada syndrome (London et al. 2007). These mutations decrease
inward sodium current by reducing cell surface expression of sodium channels (London et
al. 2007). Disease-causing mutations have also been reported in SCN1B and SCN3B , leading
to decreased sodium current (Watanabe et al. 2008, Hu et al. 2009), as well as in KCNE3
andKCND3 , leading to increased I to potassium current (Delpón et al. 2008, Giudicessi et al.
2011). Brugada syndrome associated with short QT interval is caused by loss-of-function
mutations in the calcium channel genes CACNA1C ,CACNB2b , and CACNA2D1
(Antzelevitch et al. 2007, Burashnikov et al. 2010).
CPVT is a severe disorder causing stress-induced polymorphic ventricular tachycardia
without structural abnormalities of the heart (Leenhardt et al. 1995). The baseline ECG is
typically normal, while exercise stress test shows premature ventricular complexes in a rate-
dependent fashion characteristic of CPVT. After initial mapping to chromosome 1q42-q43
(Swan et al. 1999a), this disorder was revealed to be caused by reduced threshold for
calcium-induced calcium release from the sarcoplasmic reticulum due to dominant
mutations in the RYR2 gene encoding the cardiac ryanodine receptor (Laitinen et al. 2001,
Priori et al. 2001). Recessive and dominant mutations in CASQ2 , which encodes the cardiac
calcium-binding protein calsequestrin, may also cause CPVT (Lahat et al. 2001, Postma et
al. 2002).
Atrial fibrillation is the most common cardiac arrhythmia characterized by rapid fibrillation
of atria and consequent irregular ventricular rate. It is often associated with other
cardiovascular risk factors such as hypertension, heart failure, and valvular disease
(Benjamin et al. 1994). Lone atrial fibrillation, which occurs without overt cardiovascular
disease in patients under 60 years of age, is more rare but has a greater heritability (Fox et
al. 2004b), and therefore, genetic studies have mainly focused on this form of disease. Atrial
fibrillation-associated gene mutations have been reported in several ion channels, but also in
other types of proteins. Gain-of-function mutations in the potassium channel genes KCNQ1
(Chen et al. 2003), KCNE2 (Yang et al. 2004), KCNJ2 (Xia et al. 2005), KCNH2 (Hong et

REVIEW OF THE LITERATURE
16al. 2005), KCNE3 (Lundby et al. 2008), and KCNE5 (Ravn et al. 2008) lead to atrial
arrhythmia, presumably by shortening the atrial action potential and reducing the effective
refractory period (Roberts and Gollob 2010). Loss-of-function mutations in the potassium
channel gene KCNA5 (Olson et al. 2006) and the sodium channel genes SCN5A (Ellinor et
al. 2008), SCN1B , and SCN2B (Watanabe et al. 2009) cause the disease by a different
mechanism, probably by prolonging the atrial action potential and predisposing to early
afterdepolarizations (Roberts and Gollob 2010). Also gain-of-function mutations have been
reported in SCN5A , leading to hyperexcitability (Makiyama et al. 2008, Li et al. 2009b).
Different types of genes associated with atrial fibrillation are GJA5 , encoding the gap
junction protein connexin 40 (Gollob et al. 2006), NPPA , encoding atrial natriuretic peptide
(Hodgson-Zingman et al. 2008), and NUP155, encoding a nucleoporin protein (Zhang et al.
2008). In addition to these rare mutations, several common variants are associated with
increased risk of atrial fibrillation, including those in the chromosomal region 4q25 near
PITX2 (Gudbjartsson et al. 2007), ZFHX3 in 16q22 (Benjamin et al. 2009, Gudbjartsson et
al. 2009), and KCNN3 in 1p21 (Ellinor et al. 2010).

REVIEW OF THE LITERATURE
172. Arrhythmogenic right ventricular cardiomyopathy (ARVC)
2.1. Cell-cell junctions of cardiomyocytes
The intercalated disks, which connect adjacent cardiomyocytes, consist of three types of
adhering junctions: gap junctions, adherens junctions, and desmosomes. Gap junctions
couple the cells electrically, whereas adherens junctions and desmosomes anchor the
cardiomyocytes mechanically by connecting the myofibrils and cytoskeletons of
neighbouring cells. In cardiomyocytes, the components of the different types of junctions
may localize together and form junctions of mixed type called area composita (Borrmann et
al. 2006, Franke et al. 2006).
Gap junctions
Gap junctions are groups of gap junction channels, each composed of two connexons
located in the cell membranes of adjacent cells. Each connexon consists of six connexin
molecules surrounding the central pore (Yeager and Gilula 1992). Ions and small molecules
of up to 1 kD, such as second messengers, can pass through the pore (Elfgang et al. 1995).
The selective permeability is regulated by membrane voltage (Bennett and Verselis 1992),
intracellular pH (Spray et al. 1981), calcium ion concentration (Rose and Loewenstein
1975), and connexin phosphorylation (Swenson et al. 1990). Gap junctions are responsible
for spreading electrical excitation in the heart (Barr et al. 1965). In this organ, three main
types of connexins are expressed: connexin 40, connexin 43, and connexin 45. Connexin 43
is the predominant type in ventricles, but also connexin 40 and connexin 45 are detected in
the atria and atrioventricular conduction system (Vozzi et al. 1999). Mutations in connexin
43 may lead to complex heart malformations (Britz-Cunningham et al. 1995) and mutations
in connexin 40 to atrial fibrillation (Gollob et al. 2006).
Adherens junctions
Adherens junctions attach cells together mechanically and connect the myofibrils to the cell
membrane (Geiger et al. 1980). Components of adherens junctions also participate in signal
transduction and gene expression regulation in the nucleus. For example, ȕ-catenin may be
involved in cell growth control, development, and differentiation (Funayama et al. 1995). N-
cadherin is a transmembrane glycoprotein, which mediates calcium-dependent intercellular
adhesion by homophilic interactions (Nose et al. 1990). N-cadherin interacts with Į -catenin,

REVIEW OF THE LITERATURE
18ȕ-catenin, and plakoglobin ( Ȗ-catenin) by its catenin-binding domain (Stappert and Kemler
1994), and Į-catenin can bind to the actin filament either directly (Rimm et al. 1995) or via
Į-actinin (Knudsen et al. 1995) or vinculin (Watabe-Uchida et al. 1998). The function of
adherens junctions may be regulated by controlling cadherin expression (Steinberg and
Takeichi 1994), lateral clustering of cadherin complexes (Yap et al. 1997), protein-protein
interactions (Reynolds et al. 1994), and protein phosphorylation (Hamaguchi et al. 1993).
Dysfunction of adherens junctions may lead to dilated or hypertrophic cardiomyopathy
(Olson et al. 2002, Vasile et al. 2006a, Vasile et al. 2006b).
Desmosomes
Desmosomes form dense membrane-associated plaques that anchor intermediate filaments
of the cytoskeleton to the cell membrane (Figure 2). These cell-cell junctions are abundant
in tissues subject to mechanical stress such as the myocardium and epidermis. Desmosomal
components also participate in signalling pathways involved in cell proliferation,
differentiation, and apoptosis (Allen et al. 1996, Hakimelahi et al. 2000, Chidgey et al. 2001,
Merritt et al. 2002). Desmosomal cadherins desmoglein and desmocollin are transmembrane
glycoproteins involved in either heterophilic or homophilic interaction with cadherins of the
neighbouring cell (Chitaev and Troyanovsky 1997, Marcozzi et al. 1998, Syed et al. 2002).
This adhesion is calcium-dependent, but calcium-independent interaction occurs in the
hyperadhesive state of desmosomes (Garrod et al. 2005). Armadillo proteins plakoglobin
and plakophilin interact directly with desmosomal cadherins and desmoplakin (Witcher et
al. 1996, Kowalczyk et al. 1997, Chen et al. 2002). Desmoplakin, in turn, functions as a link
between the desmosomal plaque and intermediate filaments such as desmin in the
myocardium (Kouklis et al. 1994). Plakoglobin and plakophilin are detected also in the
nucleus, where they participate in the Wnt/ ȕ-catenin signalling pathway and transcriptional
regulation (Kolligs et al. 2000, Mertens et al. 2001, Chen et al. 2002). In the cytoplasm,
plakophilin may regulate translation initiation (Wolf et al. 2010) and actin cytoskeleton
organization (Hatzfeld et al. 2000). Desmosomes are regulated by growth factors and serine
and tyrosine phosphorylation by protein kinases (Amar et al. 1999, Gaudry et al. 2001,
Miravet et al. 2003). Desmosomal proteins are also targets for caspase cleavage directing the
cell to apoptosis (Weiske et al. 2001). Mutations in desmosomal genes may cause ARVC as
well as disorders of the skin and hair (McGrath et al. 1997, Armstrong et al. 1999, Gerull et
al. 2004).

REVIEW OF THE LITERATURE
19Figure 2. Schematic representation of desmosomal structure. Adapted from Green and Gaudry 2000.
2.2. Clinical features of ARVC
Arrhythmogenic right ventricular cardiomyopathy (ARVC), also called arrhythmogenic
right ventricular dysplasia (ARVD), is a severe disorder of the myocardium. In ARVC,
ventricular cardiomyocytes are progressively replaced by adipose and fibrous tissue (Nava
et al. 1988, Thiene et al. 1988). This substitution is associated with structural and functional
changes involving predominantly the right ventricle. The structural manifestations include
ventricular dilatation and thinning, hypokinesia, and aneurysms of the ventricular wall,
which are often concentrated in the right ventricular inflow, outflow, and apical regions,
designated the “triangle of dysplasia” (Frank et al. 1978, Marcus et al. 1982, Blomström-
Lundqvist et al. 1988, Lobo et al. 1992, Fontaine et al. 1998). The prevalence of ARVC is
estimated to be between 1:1000 and 1:5000 (Rampazzo et al. 1994, Peters et al. 2004), but
this condition may be underdiagnosed because of its progressive nature and variable
expressivity. The mean age at diagnosis is approximately 30 years, and males are more often
affected than females, with an estimated gender ratio of 1.6:1 (Nava et al. 2000).
Along with the structural changes of the myocardium, ARVC manifests with electrical
instability of the heart. T-wave inversion in right precordial leads, epsilon waves, and
widening of the QRS complex may be detected in resting ECG, and late potentials in signal-
averaged ECG (McKenna et al. 1994). Frequent ventricular premature complexes may be
recorded in Holter monitoring (McKenna et al. 1994). Ventricular tachycardia originating
from the right ventricle is characteristic for ARVC patients and may lead to ventricular
fibrillation and SCD (Marcus et al. 1982, Thiene et al. 1988). The diagnosis is based on

REVIEW OF THE LITERATURE
20classification of clinical findings into major and minor criteria according to the revised Task
Force diagnostic procedure (Marcus et al. 2010), as described in Table 1. Definitive
diagnosis requires fulfilment of 2 major, 1 major plus 2 minor, or 4 minor criteria from
different categories. Borderline diagnosis requires fulfilment of 1 major plus 1 minor, or 3
minor criteria, and possible diagnosis fulfilment of 1 major or 2 minor criteria.
Table 1. Revised Task Force criteria for diagnosis of ARVC (Marcus et al. 2010)
Major criteria Minor criteria
I. Global or regional dysfunction and structural alterations
2D echo: regional RV akinesia, dyskinesia,
or aneurysm; and 1 of the following:
-PLAX RVOT •32 mm (corrected for body
size [PLAX/BSA] •19 mm/m2)
-PSAX RVOT •36 mm (corrected for body
size [PSAX/BSA] •21 mm/m2)
-fractional area change ”33%2D echo: regional RV akinesia or dyskinesia;
and 1 of the following:
-PLAX RVOT •29 to <32 mm (corrected for
body size [PLAX/BSA] •16 to <19 mm/m2)
-PSAX RVOT •32 to <36 mm (corrected for
body size [PSAX/BSA] •18 to <21 mm/m2)
-fractional area change >33% to ”40%
MRI: regional RV akinesia or dyskinesia or
dyssynchronous RV contraction; and 1 of
the following:
-ratio of RV end-diastolic volume to BSA
•110 ml/m2 (male) or •100 ml/m2 (female)
-RV ejection fraction ”40%MRI: regional RV akinesia or dyskinesia or
dyssynchronous RV contraction; and 1 of the
following:
-ratio of RV end-diastolic volume to BSA •100
to <110 ml/m2 (male) or •90 to <100 ml/m2
(female)
-RV ejection fraction >40% to ”45%
RV angiography: regional RV akinesia,
dyskinesia, or aneurysm
II. Tissue characterization of wall
Residual myocytes <60% by morphometric
analysis (or <50% if estimated), with fibrous
replacement of the RV free wall myocardium
in •1 sample, with or without fatty
replacement of tissue on endomyocardial
biopsyResidual myocytes 60% to 75% by
morphometric analysis (or 50% to 65% if
estimated), with fibrous replacement of the
RV free wall myocardium in •1 sample, with
or without fatty replacement of tissue on
endomyocardial biopsy
III. Repolarization abnormalities
Inverted T waves in right precordial leads
(V1-V3) or beyond in individuals >14 years
of age (in the absence of complete RBBB
QRS •120 ms)Inverted T waves in leads V1 and V2 in
individuals >14 years of age (in the absence
of complete RBBB) or in V4, V5, or V6
Inverted T waves in leads V1-V4 in individuals
>14 years of age in the presence of complete
RBBB

REVIEW OF THE LITERATURE
21Major criteria Minor criteria
IV. Depolarization/conduction abnormalities
Epsilon wave (reproducible low-amplitude
signals between end of QRS complex to
onset of the T wave) in the right precordial
leads (V1-V3)Late potentials by SAECG in •1 of 3
parameters in the absence of a QRS duration
of •110 ms on standard ECG
Filtered QRS duration (fQRS) •114 ms
Duration of terminal QRS <40 ȝV (low-
amplitude signal duration) •38 ms
Root-mean-square voltage of terminal 40 ms
”20 ȝV
Terminal activation duration of QRS •55 ms
measured from the nadir of the S wave to the
end of the QRS, including R’, in V1, V2, or
V3, in the absence of complete RBBB
V. Arrhythmias
Non-sustained or sustained VT of LBBB
morphology with superior axis (negative or
indeterminate QRS in leads II, III, and aVF
and positive in lead aVL)Non-sustained or sustained VT of RV outflow
configuration, LBBB morphology with inferior
axis (positive QRS in leads II, III, and aVF
and negative in lead aVL) or of unknown axis
>500 ventricular extrasystoles per 24 h
(Holter)
VI. Family history
ARVC confirmed in a first-degree relative
who meets current Task Force criteriaHistory of ARVC in a first-degree relative in
whom it is not possible or practical to
determine whether the family member meets
current Task Force criteria
ARVC confirmed pathologically at autopsy
or surgery in a first-degree relativePremature sudden death (<35 years of age)
due to suspected ARVC in a first-degree
relative
Pathogenic mutation (associated or
probably associated with ARVC) in the
patient under evaluationARVC confirmed pathologically or by current
Task Force criteria in a second-degree
relative
BSA = body surface area; 2D echo = two-dimensional echocardiography; ECG = electrocardiography;
LBBB = left bundle branch block; MRI = magnetic resonance imaging; PLAX = parasternal long-axis
v i e w ; P S A X = p a r a s t e r n a l sh o r t- a x i s v i e w ; R B B B = r i g h t b u n d l e b r a n ch b l o ck ; R V = r i g h t v e n t r i cu l a r ;
RVOT = RV outflow tract; SAECG = signal-averaged ECG; VT = ventricular tachycardia.
Due to the progressive nature of ARVC, four different clinicopathological phases of disease
can be recognized in the patients (Blomström-Lundqvist et al. 1987, Corrado et al. 1997,
Corrado et al. 2000b). In the concealed phase, only subtle right ventricular abnormalities are
present and the patients are asymptomatic but nevertheless at risk for SCD. In the overt
electrical phase, the patients develop arrhythmias and functional and morphological
abnormalities of the right ventricle. The third phase involves right ventricular failure.
Ultimately, the disorder may lead to biventricular heart failure in the most advanced phase.

REVIEW OF THE LITERATURE
22However, left ventricular involvement may be detected also in earlier phases of the disease
(Sen-Chowdhry et al. 2008). The patients are treated with medication for arrhythmias and
cardiac insufficiency, implantable cardioverter-defibrillator, catheter ablation, and
ultimately cardiac transplantation (Wichter et al. 1992, Corrado et al. 2000a). ARVC
patients are also advised to avoid extreme physical exertion (Sen-Chowdhry et al. 2004).
2.3. Genetics of ARVC
ARVC has been reported to occur familially in 30-70% of cases (Hamid et al. 2002, Dalal et
al. 2005, van Tintelen et al. 2006). The mode of inheritance is usually autosomal dominant,
with markedly reduced penetrance (Nava et al. 1988). However, compound heterozygosity
and digenic heterozygosity are often detected in patients with severe disease (Bhuiyan et al.
2009, den Haan et al. 2009, Bauce et al. 2010). Environmental factors, such as oestrogen,
athletic activity, and viral infections, are suggested to affect the disease penetrance in
addition to genetic variants (Awad et al. 2008). ARVC is a disorder of the desmosome, as
mutations in each of the components of the cardiac desmosomes, plakophilin-2,
desmoplakin, desmoglein-2, desmocollin-2, and plakoglobin, have been documented in
ARVC patients (Table 2). In addition, several chromosomal loci with non-desmosomal or
unknown disease-associated genes have been identified in individual ARVC families.
Several disease mechanisms have been suggested in the pathogenesis of ARVC. Firstly, the
disruption of desmosomal organization by mutations in the desmosomal components may
lead to loss of myocyte adhesion, and consequently, cell death, which is enhanced by
physical strain (Awad et al. 2008, Delmar and McKenna 2010). The myocytes have limited
regenerative capacity, and therefore, their death might lead to a repair mechanism by fibrous
and adipose tissue replacement. The right ventricle may be especially vulnerable to this loss
of myocyte adhesion because of its thin walls and its high ability to dilate (Awad et al. 2008,
Delmar and McKenna 2010). Secondly, desmosomal mutations lead to redistribution of
plakoglobin to the nucleus, where it suppresses the canonical Wnt/ ȕ-catenin signalling
pathway (Garcia-Gras et al. 2006, Asimaki et al. 2009). This causes increased expression of
transcriptional regulators of adipogenesis, which has been suggested to lead to
differentiation of cardiac progenitor cells into adipocytes instead of cardiomyocytes (Garcia-
Gras et al. 2006, Lombardi et al. 2009). Suppression of Wnt/ ȕ-catenin signalling also leads
to increased apoptosis (Longo et al. 2002), which is detected in the myocardium of ARVC

REVIEW OF THE LITERATURE
23patients (Mallat et al. 1996). The third possible disease mechanism involves impairment of
the localization and conductivity of the gap junctional protein connexin 43 due to decreased
expression of plakophilin-2 (Oxford et al. 2007) or disrupted interaction with desmocollin-2
(Gehmlich et al. 2011). This gap junctional remodelling might lead to an increased
propensity for arrhythmias.
ARVC pathogenesis may also involve altered calcium homeostasis or sodium current. A
gain-of-function mutation in plakoglobin creates a novel interaction with histidine-rich
calcium-binding protein, as detected in a yeast-two-hybrid screen (Asimaki et al. 2007). If a
similar defect occurs also in patient cardiomyocytes, it could promote arrhythmias by
disturbed calcium signalling. Plakophilin-2 interacts with the Į subunit of the cardiac
sodium channel (Sato et al. 2009). Loss of plakophilin-2 leads therefore to alterations of the
amplitude and voltage-gating kinetics of the sodium current, which may predispose
desmosomal mutation carriers to reentrant arrhythmias (Sato et al. 2009).
Table 2. Chromosomal loci and genes identified in linkage and association studies of ARVC
ARVC subtype Locus Gene Protein Reference
Autosomal dominant
ARVC1 14q24 TGFB3 transforming
growth factor ȕ3Rampazzo et al. 1994,
Beffagna et al. 2005
ARVC2 1q43 RYR2 cardiac ryanodine
receptorRampazzo et al. 1995,
Tiso et al. 2001
ARVC3 14q12-q22 N/A N/A Severini et al. 1996
ARVC4 2q32.1-q32.3 N/A N/A Rampazzo et al. 1997
ARVC5 3p23 TMEM43 transmembrane
protein 43Ahmad et al. 1998,
Merner et al. 2008
ARVC6 10p12-p14 N/A N/A Li et al. 2000
ARVC7 10q22.3 N/A N/A Melberg et al. 1999
ARVC8 6p24 DSP desmoplakin Rampazzo et al. 2002
ARVC9 12p11 PKP2 plakophilin-2 Gerull et al. 2004
ARVC10 18q12 DSG2 desmoglein-2 Awad et al. 2006a,
Pilichou et al. 2006
ARVC11 18q12 DSC2 desmocollin-2 Syrris et al. 2006b
ARVC12 17q21 JUP plakoglobin Asimaki et al. 2007
ARVC13 2q35 DES desmin Klauke et al. 2010
Autosomal recessive
Naxos disease 17q21 JUP plakoglobin McKoy et al. 2000
Syndromic ARVC 6p24 DSP desmoplakin Alcalai et al. 2003
Syndromic ARVC 18q12 DSC2 desmocollin-2 Simpson et al. 2009
N/A = not available (gene unknown).

REVIEW OF THE LITERATURE
24Plakophilin-2
Plakophilin-2 belongs to the armadillo family of proteins and contains an amino-terminal
head domain and nine armadillo repeat motifs (Mertens et al. 1996). It is expressed in most
desmosome-containing tissues, and in cardiomyocytes, it is the only desmosomal
plakophilin (Mertens et al. 1996, Mertens et al. 1999). This protein is essential for heart
morphogenesis and localization of desmoplakin to cell junctions in mice (Grossmann et al.
2004). Mutations in PKP2 constitute a common cause of ARVC, accounting for
approximately 30% of reported cases (Gerull et al. 2004, Antoniades et al. 2006, Dalal et al.
2006, Pilichou et al. 2006, Syrris et al. 2006a, den Haan et al. 2009, Qiu et al. 2009,
Christensen et al. 2010, Fressart et al. 2010, Xu et al. 2010, Cox et al. 2011). Most mutations
are dominant with significantly reduced penetrance, but recessive and compound
heterozygous mutations have also been identified in several patients (Awad et al. 2006b, Xu
et al. 2010). Large exonic deletions in PKP2 can also be detected in a small number of
patients (Cox et al. 2011).
Desmoplakin
Desmoplakin is a member of the plakin family and forms homodimers via its coiled-coil
alpha-helical rod domain (Kowalczyk et al. 1994). It is expressed in all desmosome-
containing tissues (Leung et al. 2002). Complete loss of desmoplakin is lethal in mice
(Gallicano et al. 1998). Cardiac-restricted heterozygous deletion of desmoplakin leads to a
phenotype resembling ARVC in a mouse model (Garcia-Gras et al. 2006), as does
overexpression of a desmoplakin missense mutation (Yang et al. 2006). DSP mutations can
be detected in approximately 5% of ARVC cases, many of them featuring left ventricular
involvement (Pilichou et al. 2006, Yang et al. 2006, den Haan et al. 2009, Christensen et al.
2010, Fressart et al. 2010, Xu et al. 2010, Cox et al. 2011).
Desmoglein-2
Desmoglein-2 and desmocollin-2 are expressed in all desmosome-containing tissues and are
the only desmosomal cadherins expressed in the heart (Schäfer et al. 1994, Nuber et al.
1995). Desmoglein-2 is needed for embryonic stem cell proliferation in mice (Eshkind et al.
2002). Mice overexpressing a DSG2 missense mutation manifest with features resembling
ARVC and develop myocyte necrosis (Pilichou et al. 2009). DSG2 mutations are detected in
approximately 7% of ARVC patients (Awad et al. 2006a, Heuser et al. 2006, Pilichou et al.

REVIEW OF THE LITERATURE
252006, Syrris et al. 2007, den Haan et al. 2009, Christensen et al. 2010, Fressart et al. 2010,
Xu et al. 2010, Cox et al. 2011).
Desmocollin-2
DSC2 knockdown in zebrafish embryos leads to desmosomal dysfunction and myocardial
contractility defects, suggesting that desmocollin-2 is needed for cardiac morphogenesis and
function (Heuser et al. 2006). DSC2 mutations are rare in ARVC, accounting for only 2% of
reported cases (Heuser et al. 2006, Syrris et al. 2006b, den Haan et al. 2009, Christensen et
al. 2010, Fressart et al. 2010, Xu et al. 2010, Cox et al. 2011).
Plakoglobin
Plakoglobin ( Ȗ-catenin) is a member of the armadillo protein family and contains 13
armadillo repeat motifs (Franke et al. 1989). It is located in both desmosomes and adherens
junctions as well as in the nucleus. Homozygous deletion of JUP is lethal and leads to
severe heart defects in mice (Bierkamp et al. 1996, Ruiz et al. 1996). Heterozygous
plakoglobin-deficient mice develop an ARVC-like phenotype, which is precipitated by
endurance training (Kirchhof et al. 2006). Dominant JUP mutations can be detected in
approximately 1% of ARVC cases (den Haan et al. 2009, Christensen et al. 2010, Fressart et
al. 2010, Xu et al. 2010, Cox et al. 2011).
Other genes associated with ARVC
Mutations in cardiac ryanodine receptor have been identified in families with effort-induced
polymorphic tachycardias (Rampazzo et al. 1995, Tiso et al. 2001), a phenotype resembling
RYR2 -linked CPVT. Mutations in the untranslated region of TGFB3 , encoding the
multifunctional cytokine transforming growth factor ȕ3, have been identified in two ARVC
probands (Beffagna et al. 2005). However, no mutations in the protein-coding region of
TGFB3 have yet been reported in ARVC. Transmembrane protein 43 is a nuclear membrane
protein in many cell types, but in cardiomyocytes, it localizes to the cell membrane
(Bengtsson and Otto 2008, Christensen et al. 2011). Mutations of TMEM43 have been
identified in a fully penetrant and lethal form of ARVC (Merner et al. 2008) and in Emery-
Dreifuss muscular dystrophy-related myopathy (Liang et al. 2011). Mutations in the
intermediate filament protein desmin are associated with skeletal and cardiac myopathy
(Goldfarb et al. 1998), but also with ARVC without skeletal muscle involvement (Klauke et

REVIEW OF THE LITERATURE
26al. 2010). Recently, mutations in TTN, located near the ARVC4 locus and encoding the
sarcomeric protein titin (Taylor et al. 2011), and in PLN, encoding phospholamban (van der
Zwaag et al. 2012), were reported in families with ARVC.
2.4. Syndromic forms of ARVC
Syndromic forms of ARVC involving skin and hair abnormalities follow an autosomal
recessive mode of inheritance and are associated with mutations in the desmosomal genes.
Naxos disease patients suffer from non-epidermolytic palmoplantar keratoderma, woolly
hair, and ARVC (Protonotarios et al. 1986). A homozygous deletion in the last armadillo
repeat of plakoglobin has been reported in all affected cases (McKoy et al. 2000). A
homozygous missense mutation in plakoglobin has been identified in a similar syndrome
with palmoplantar keratoderma, alopecia, and ARVC (Erken et al. 2011). Symptoms of
Carvajal syndrome include epidermolytic palmoplantar keratoderma, woolly hair, and
dilated cardiomyopathy with predominant left ventricular involvement (Carvajal-Huerta
1998). Affected patients are homozygous for a deletion in the carboxy terminus of
desmoplakin (Norgett et al. 2000). A similar syndrome with pemphigous-like skin disorder,
woolly hair, and cardiomyopathy described as ARVC due to predominantly right ventricular
involvement is caused by a homozygous missense mutation in the carboxy terminus of
desmoplakin (Alcalai et al. 2003). A homozygous mutation in desmocollin-2 has been
identified in patients with ARVC, mild palmoplantar keratoderma, and woolly hair
(Simpson et al. 2009).

REVIEW OF THE LITERATURE
273. Long QT syndrome (LQTS)
3.1. Cardiac ion channels
Cardiac action potential is generated by sequential opening and closing of ion channels
located in the plasma membrane of cardiomyocytes (Figure 3). The resting membrane
potential of ventricular cardiomyocytes is negative, approximately -85 mV (Amin et al.
2010). Depolarization is caused by the inward sodium current (I Na). Early repolarization by
the transient outward potassium current (I to) is followed by a plateau phase, in which
potassium outflow by the rapidly (I Kr) and slowly (I Ks) activated delayed rectifier currents is
balanced by the L-type inward calcium current (I Ca,L). The negative membrane potential is
restored in the repolarization phase by the delayed outward rectifier potassium currents after
the inactivation of the I Ca,L channels. In the resting phase, the negative membrane potential
is maintained by the inward rectifier potassium current (I K1). Calcium influx in the plateau
phase initiates calcium-induced calcium release from the sarcoplasmic reticulum through
activation of the cardiac ryanodine receptor complex. The elevation of intracellular calcium
concentration couples excitation with contraction in cardiomyocytes (Amin et al. 2010).
Figure 3. Action potential of a ventricular cardiomyocyte and the main ion currents contributing to each
phase (0-4). Adapted from Amin et al. 2010.

REVIEW OF THE LITERATURE
28Cardiac ion channels consist of pore-forming Į-subunits and accessory ȕ-subunits. The Į-
subunit of sodium and calcium channels comprises four repeats of a domain structure (DI-
DIV), each containing six transmembrane segments (S1-S6) (Gellens et al. 1992). The pore
loop of the channel is located between S5 and S6, and segment S4 is responsible for voltage-
dependent activation (Stühmer et al. 1989). I to, IKr, and I Ks channels consist of a single
domain with six transmembrane segments, and I K1 of a single domain with two
transmembrane segments (MacKinnon 1991). Voltage-gated potassium channel Į-subunits
co-assemble to form a functional tetramer structure (MacKinnon 1991).
The main ion channels of ventricular cardiomyocytes are listed in Table 3. Most of these
channels have several ȕ-subunits or regulatory proteins affecting their function. For
example, minK encoded by KCNE1 and MiRP1 encoded by KCNE2 are required for
generation of I Ksand I Krcurrents (Barhanin et al. 1996, Sanguinetti et al. 1996, McDonald et
al. 1997, Abbott et al. 1999). In addition, cardiac ion channels may be regulated by
membrane voltage, ion concentrations, phosphorylation, second messengers, ligand binding,
channel-blocking agents, and microRNAs (Amin et al. 2010).
Table 3. Main ion currents during the action potential of ventricular cardiomyocytes
Current Į-subunit Į-subunit gene Reference
INa Nav1.5 SCN5A Gellens et al. 1992
Ito,fast Kv4.3 KCND3 Kong et al. 1998
Ito,slow Kv1.4 KCNA4 Tamkun et al. 1991
ICa,L Cav1.2 CACNA1C Schultz et al. 1993
IKr Kv11.1 KCNH2 Warmke and Ganetzky 1994
IKs Kv7.1 KCNQ1 Wang et al. 1996
IK1 Kir2.1 KCNJ2 Raab-Graham et al. 1994
INCX NCX1 SLC8A1 Komuro et al. 1992
3.2. Clinical features of LQTS
Long QT syndrome (LQTS) is a severe electrical disorder of the heart manifesting with risks
of ventricular arrhythmias and SCD despite a structurally normal heart. Jervell and Lange-
Nielsen syndrome was the first reported form of LQTS (Jervell and Lange-Nielsen 1957).
This autosomal recessive disorder features also congenital deafness due to a potassium
secretion defect in the inner ear (Vetter et al. 1996). The estimated prevalence of Jervell and
Lange-Nielsen syndrome is 1:55 000-1:200 000 (Tranebjaerg et al. 1999). Romano-Ward

REVIEW OF THE LITERATURE
29syndrome (Romano et al. 1963, Ward 1964), the autosomal dominant form of LQTS, is
more common, with prevalence estimates ranging from 1:2000 to 1:5000 (Goldenberg and
Moss 2008, Schwartz et al. 2009). The average age of onset is 12 years, but symptoms may
begin any time between early childhood and the age of 40 years (Priori et al. 2003).
LQTS is characterized by prolonged QT interval on ECG, an indication of delayed
repolarization. Ventricular tachycardia typically occurs in the form of torsades de pointes ,
which can be seen in ECG as twisting of the QRS axis and may lead to ventricular
fibrillation and ultimately to SCD (Viskin et al. 1996). The extent of QT prolongation
predicts the risk of cardiac events (Zareba et al. 1995). Since QT interval duration is rate-
dependent, it is generally corrected for heart rate according to Bazett’s formula (QTc =
QT/¥RR) (Bazett 1920). QTc ” 440 ms has often been considered normal (Vincent et al.
1992). A specific diagnostic scoring system, which takes into account both ECG findings
and clinical and family history, is suitable for individuals with borderline prolonged QT
interval or those without symptoms (Schwartz et al. 1993). Exercise stress test is especially
useful for differentiating the subtypes LQT1 and LQT2 (Swan et al. 1999b).
The expressivity and penetrance of LQTS are variable and the symptoms may vary
considerably even within a single family (Priori et al. 1999). Patient history, including
syncope and cardiac arrest, family history, QTc interval duration, and sex, as well as
information on mutated locus and mutation type can be used for risk stratification of LQTS
patients (Moss et al. 2000, Moss et al. 2002, Priori et al. 2003). Arrhythmic events of LQTS
patients can be prevented with beta blocker therapy, or more rarely with left cardiac
sympathetic denervation, and treated with an implantable cardioverter-defibrillator (Zipes et
al. 2006). Lifestyle advice includes avoidance of QT-prolonging medication, electrolyte
disturbances, and adrenergic stimuli (Zipes et al. 2006). Patients with LQT1 are
recommended to refrain from competitive sports and swimming, and patients with LQT2 to
avoid exposure to auditory stimuli, especially during sleep (Schwartz et al. 2001).
Acquired LQTS may be caused by environmental factors, such as QT-prolonging
medication or electrolyte disturbances, which affect the function of cardiac ion channels
(Roden 2004). It may also arise from ischaemic or structural heart disease or congestive
heart failure (Roden 2004). Although acquired LQTS is not usually inherited, certain genetic

REVIEW OF THE LITERATURE
30variants may predispose to it in the presence of an additional trigger (Abbott et al. 1999,
Napolitano et al. 2000, Sesti et al. 2000, Lehtonen et al. 2007).
3.3. Genetics of LQTS
LQTS results from defective function of cardiac ion channels. Thus far, 13 genes have been
reported to be associated with LQTS (Table 4), but many of them are mutated only in
individual families (Bokil et al. 2010). These genes encode the Į-subunits of cardiac ion
channels ( KCNQ1 ,KCNH2 ,SCN5A ,KCNJ2 ,CACNA1C , and KCNJ5 ), but also the
regulatory ȕ-subunits (KCNE1 ,KCNE2 , and SCN4B ), ion-channel scaffolding proteins
(AKAP9 and SNTA1 ), and proteins targeting the ion channels to the cell membrane or
altering the biophysical properties of ion channels ( ANK2 and CAV3 ). Mutations in these
genes are detected in up to 70% of LQTS cases, which is suggestive of additional disease
loci (Napolitano et al. 2005, Bai et al. 2009). Most mutations are of missense type, but also
frameshift, nonsense, and splice site mutations occur frequently. Recently, large copy
number variants in KCNQ1 and KCNH2 were reported to account for approximately 3% of
LQTS cases without point mutations in the known LQTS genes (Barc et al. 2011).
Most LQTS mutations cause either a loss of function in the repolarizing potassium currents
or a gain of function in the depolarizing sodium or calcium currents. Both types of defects
result in prolongation of the repolarization phase. It is also possible that these defects lead to
reduced repolarization reserve manifesting in LQTS symptoms only after an additional
stimulus, such as adrenergic stimulation, bradycardia, or use of QT-prolonging medication,
affects repolarization (Roden 1998). Delayed repolarization predisposes to early and delayed
afterdepolarizations, which trigger a torsades de pointes type of arrhythmia (Antzelevitch
2007). Arrhythmias may also be caused by a reentry mechanism due to increased transmural
dispersion of repolarization (Antzelevitch 2007).

REVIEW OF THE LITERATURE
31Table 4. Genes and related functional defects associated with LQTS
LQTS
subtypeGene Protein Functional
defectTrigger of symptoms Reference
Autosomal dominant
LQT1 KCNQ1 Kv7.1 I KsĻ Exercise, swimming,
emotionWang et al. 1996
LQT2 KCNH2 Kv11.1 I KrĻ Sound, emotion Curran et al. 1995
LQT3 SCN5A Nav1.5 I NaĹ Sleep, rest, emotion Wang et al. 1995
LQT4 ANK2 A n k y r i n B I Na,K, INCX,
INaĻExercise Mohler et al. 2003
LQT5 KCNE1 minK I KsĻ Exercise, emotion Splawski et al. 1997
LQT6 KCNE2 MiRP1 I KrĻ Rest, exercise Abbott et al. 1999
LQT7
(AS)KCNJ2 Kir2.1 I K1Ļ Rest, exercise Plaster et al. 2001
LQT8
(TS)CACNA1C Cav1.2 I Ca,LĹ Exercise, emotion Splawski et al. 2004
LQT9 CAV3 M-Caveolin INaĹ Rest, sleep Vatta et al. 2006
LQT10 SCN4B Navȕ4 I NaĹ Exercise Medeiros-Domingo
et al. 2007
LQT11 AKAP9 Yotiao I KsĻ Exercise Chen et al. 2007
LQT12 SNTA1 Į1-
SyntrophinINaĹ Rest Ueda et al. 2008
LQT13 KCNJ5 Kir3.4 I KAChĻ Exercise, emotion Yang et al. 2010
Autosomal recessive
JLNS1 KCNQ1 Kv7.1 I KsĻ Exercise, swimming,
emotionTyson et al. 1997
JLNS2 KCNE1 minK I KsĻ Exercise, swimming,
emotionTyson et al. 1997
AS = Andersen’s syndrome; JLNS = Jervell and Lange-Nielsen syndrome; TS = Timothy syndrome.
In Finland, the four founder mutations KCNQ1 G589D, KCNQ1 IVS7-2A>G, KCNH2
L552S, and KCNH2 R176W account for the majority of the known genetic spectrum of
LQTS (Piippo et al. 2001, Fodstad et al. 2004). Only 23-38% of the carriers of these
mutations are symptomatic, indicating a role for other genetic and environmental disease-
modifying factors (Fodstad et al. 2004). These founder mutations have clustered in the
Finnish population due to the unique population history of Finland, with small founder
populations, bottleneck effects, and geographic and cultural isolation (Sajantila et al. 1996,
Peltonen et al. 1999). One in 250 Finns carry one of these four mutations, which prolong the
QT interval by 22-50 ms when studied at the population level (Marjamaa et al. 2009b).

REVIEW OF THE LITERATURE
32LQT1
The first LQTS locus was assigned to chromosome 11p15.5 in a linkage study (Keating et
al. 1991). Positional cloning revealed KCNQ1 (also called KVLQT1 ) as the disease-
associated gene (Wang et al. 1996). Mice with a homozygous disruption of the Kcnq1 gene
show a shaker/waltzer phenotype and deafness in addition to prolongation of QT and JT
intervals and abnormal T- and P-wave morphologies, a cardiac phenotype comparable to
Jervell and Lange-Nielsen syndrome (Casimiro et al. 2001). Approximately 50% of LQTS
patients with a genetic diagnosis harbour mutations in KCNQ1 (Napolitano et al. 2005).
LQT2
Chromosome 7q35-q36 was identified as the second loci linked to LQTS (Jiang et al. 1994).
Mapping of KCNH2 (also called HERG , the human ether-a-go-go-related gene) to this
region and detection of mutations in this gene in LQTS patients revealed KCNH2 a s t h e
disease-associated gene in LQT2 (Curran et al. 1995). Homozygous deletion of the
homologous gene in mice leads to elimination of the I Kr current and episodic sinus
bradycardia (Lees-Miller et al. 2003). Approximately 40% of genetically defined LQTS
patients have mutations in KCNH2 (Napolitano et al. 2005). Mutations located in the pore-
forming region have been associated with a more severe disease phenotype than other
mutations (Moss et al. 2002).
LQT3
LQT3 locus in 3p21 was reported together with LQT2 in a linkage study (Jiang et al. 1994).
In a subsequent study, an intragenic deletion of SCN5A was detected as the disease-causing
mutation in two families (Wang et al. 1995). A corresponding heterozygous deletion in mice
causes sudden accelerations in heart rate, lengthening of the action potential, and ventricular
arrhythmias (Nuyens et al. 2001), whereas homozygous disruption of Scn5a leads to
intrauterine lethality (Papadatos et al. 2002). Mutations in SCN5A account for approximately
10% of LQTS patients with a genetic diagnosis (Napolitano et al. 2005).

REVIEW OF THE LITERATURE
333.4. Genetics of QT interval and LQTS modifier genes
QT interval has a heritability of 35-40% in the general populaton (Newton-Cheh et al. 2005,
Li et al. 2009a). Previously, genetic studies of QT interval concentrated mainly on candidate
genes identified through association with LQTS. Consequently, several common variants in
the LQTS genes, such as KCNE1 D85N (rs1805128) and KCNH2 K897T (rs1805123), were
reported to be associated with QT interval duration in the general population (Pietilä et al.
2002, Bezzina et al. 2003b, Gouas et al. 2005, Pfeufer et al. 2005, Newton-Cheh et al. 2007,
Marjamaa et al. 2009a). More recently, genome-wide association (GWA) studies have
enabled the hypothesis-free search of QT interval-associated loci. This approach has
revealed several associated loci of unknown function in addition to the known LQTS loci
(Table 5). The NOS1AP locus in 1q23 consistently shows the statistically strongest
association with QT interval (Arking et al. 2006, Marroni et al. 2009, Newton-Cheh et al.
2009b, Nolte et al. 2009, Pfeufer et al. 2009). NOS1AP encodes the neuronal nitric oxide
synthase 1 adaptor protein, which modulates cardiac repolarization by inhibition of I Ca,L
current and enhancement of I Kr current (Chang et al. 2008). Together, the known loci
explain only up to 10% of the heritability of QT interval (Newton-Cheh et al. 2009b,
Jamshidi et al. 2010), suggesting a role for additional still unknown QT interval-modifying
loci.

REVIEW OF THE LITERATURE
34Table 5. QT interval-associated loci identified in GWA studies ( p < 5×10-8)
Chr. Position* Nearest gene SNP Coded allele† CAF
1p36 6 201 957 RNF207 rs846111 C 0.28
1q23 160 300 514 NOS1AP rs12143842 T 0.26
1q24 167 366 107 ATP1B1 rs10919071 A 0.87
2p22 40 611 295 SLC8A1 rs13017846 A 0.58
3p22 38 568 397 SCN5A rs12053903 T 0.66
6q22 118 787 067 PLN rs11970286 T 0.44
7q36 150 268 796 KCNH2 rs4725982 T 0.22
11p15 2 441 379 KCNQ1 rs2074238 C 0.94
13q13 34 095 789 NBEA rs885170 G 0.19
13q14 47 060 559 SUCLA2 rs2478333 A 0.33
16p13 11 599 254 LITAF rs8049607 T 0.49
16q21 57 124 739 CNOT1 rs37062 A 0.76
17q12 30 348 495 LIG3 rs2074518 C 0.54
17q24 66 006 587 KCNJ2 rs17779747 G 0.65
21q22 34 743 550 KCNE1 rs1805128 A 0.01
References: Marroni et al. 2009, Newton-Cheh et al. 2009b, Nolte et al. 2009, Pfeufer et al. 2009, Kim et
al. 2012.
*According to NCBI genome build 36.
†Allele associated with QT prolongation.
CAF = coded allele frequency in the original study; Chr. = chromosome; GWA = genome-wide
association; SNP = single nucleotide polymorphism.
The QT interval-associated single nucleotide polymorphisms (SNPs) provide intriguing
candidates for studies of LQTS modifier genes that affect the large phenotypic variation
among LQTS mutation carriers. The KCNE1 D85N (rs1805128) minor allele, which
prolongs QT interval up to 10 ms at the population level (Marjamaa et al. 2009a), has been
shown to reduce both I Ks and I Kr currents in vitro and to occur as a second variant in several
LQTS patients (Westenskow et al. 2004, Nishio et al. 2009). In addition, variants in
NOS1AP have been associated with increased risk of cardiac events in LQTS patients (Crotti
et al. 2009, Tomás et al. 2010). A common polymorphism in a LQTS gene may also directly
affect the phenotype caused by a mutation in the same gene. For example, the KCNH2
K897T minor allele has been suggested to accentuate the I Kr reduction caused by a more
severe KCNH2 mutation (Crotti et al. 2005), but the findings on cardiac repolarization are
conflicting both in vitro and in vivo (Laitinen et al. 2000, Scicluna et al. 2008). Interestingly,
the minor allele of SCN5A H558R was shown to rescue the phenotype of a LQTS-causing
SCN5A mutation (Ye et al. 2003).

REVIEW OF THE LITERATURE
354. Sudden cardiac death (SCD)
4.1. Epidemiology and clinical risk factors of SCD
Sudden cardiac death (SCD) is generally defined as an unexpected death occurring within
one hour of the onset of symptoms or, when unwitnessed, within 24 hours of being seen
alive and well (Chugh et al. 2008). Exclusion of non-cardiac causes of death, such as
pulmonary embolism, aortic rupture, or stroke, is essential for the diagnosis. The estimated
yearly incidence of SCD is 50:100 000-80:100 000 in Western countries (Chugh et al. 2008),
and the corresponding figure in Finland has been estimated to be 57:100 000 (Hookana et al.
2011). The incidence of SCD has declined over the past decades due to the improvement of
prevention and treatment strategies for cardiovascular disease, but at the same time, the
occurrence of SCD as a proportion of overall cardiovascular deaths has increased (Fox et al.
2004a). SCD is estimated to account for approximately half of all cardiovascular deaths
(Salomaa et al. 2003, Fox et al. 2004a). Coronary heart disease (CHD) underlies up to 80%
of SCDs (Chugh et al. 2004a, Hookana et al. 2011). Cardiomyopathy is detected in 10-15%
of SCD cases and a congenital abnormality or a structurally normal heart, indicating a
primary arrhythmogenic disorder, is reported in 5-10% (Chugh et al. 2008). Familial
investigation of autopsy-negative SCD cases reveals inherited arrhythmia disorders in
approximately half of the families, including LQTS, Brugada syndrome, CPVT, ARVC, and
hypertrophic cardiomyopathy (Tan et al. 2005, Behr et al. 2008).
Males have a higher risk of SCD than females, and the occurrence peaks in early childhood
and after the age of 45 (Chugh et al. 2004a). Clinical risk factors for CHD predispose also to
SCD. These include smoking, obesity, lack of physical activity, hypertension, diabetes,
hypercholesterolaemia, and family history of CHD (Wannamethee et al. 1995, Jouven et al.
1999). After myocardial infarction, the risk of SCD is highest during the first 30 days,
decreasing gradually thereafter (Adabag et al. 2008), and atrial fibrillation is known to
increase the risk (Pedersen et al. 2006). Other risk factors for SCD are heart failure, left
ventricular dysfunction and hypertrophy, reduced pulmonary vital capacity, elevated heart
rate, abnormal ECG, and abnormal autonomic markers such as decreased heart rate
variability (Adabag et al. 2010a). Prolonged QT interval predisposes to SCD in the general
population (Straus et al. 2006). J-point elevation (Tikkanen et al. 2009) and QRS complex

REVIEW OF THE LITERATURE
36widening (Dhar et al. 2008, Kurl et al. 2012) are also known risk factors for SCD. The SCD
risk is increased in people with low socioeconomic status (Reinier et al. 2006).
SCD is the first manifestation of cardiovascular disease in approximately 50% of cases (Fox
et al. 2004a). In addition, most of the clinical risk factors have a low positive predictive
value for SCD. High-risk criteria, such as myocardial infarction or left ventricular
dysfunction, reveal only a small proportion of potential victims, and the majority of SCDs
occur in risk groups with the lowest incidence (Figure 4) (Myerburg et al. 1998, Huikuri et
al. 2001, Noseworthy and Newton-Cheh 2008). Prevention of fatal arrhythmic events with,
for example, an implantable cardioverter-defibrillator is feasible only in the high-risk groups
(Zipes et al. 2006). Therefore, identification of individuals with a markedly elevated risk of
SCD is essential. Genetic risk markers could provide a means for better risk prediction
together with the clinical risk factors.
Figure 4. Incidence and total number of events for SCD in the different risk groups in the USA. CAD =
coronary artery disease; EF = ejection fraction; MI = myocardial infarction; SCD = sudden cardiac death;
VF = ventricular fibrillation; VT = ventricular tachycardia. Adapted from Myerburg et al. 1998, Huikuri et
al. 2001, and Noseworthy and Newton-Cheh 2008.

REVIEW OF THE LITERATURE
374.2. Genetics of SCD
The risk of SCD is heritable, but the genes involved are largely unknown. Parental history of
SCD approximately doubles the risk of SCD, but if both parents have died suddenly, the risk
of sudden death is 9-fold (Jouven et al. 1999, Friedlander et al. 2002). Sudden death in a
first-degree relative is also associated with an increased risk of ventricular fibrillation during
myocardial infarction and with an elevated risk of dying suddenly during an acute coronary
event (Dekker et al. 2006, Kaikkonen et al. 2006), indicating that genetic variants may
predispose to fatal arrhythmic events during myocardial infarction.
Inherited arrhythmia disorders, such as LQTS, CPVT, and ARVC, may lead to SCD. Rare
mutations in the LQTS genes KCNQ1 ,KCNH2 , and SCN5A , as well as in the CPVT gene
RYR2 , have also been detected in SCD victims without a previously diagnosed electrical
disorder (Table 6). Together, mutations in these genes may occur in up to one-third of
sudden unexplained death victims (Tester et al. 2004, Tester and Ackerman 2007). Recently,
mutations in the ARVC gene PKP2 were also reported in cases of sudden unexplained death
with negative autopsy findings (Zhang et al. 2012) .In addition to the rare mutations, also
common variants in the KCNQ1 and SCN5A ion channel genes are associated with increased
risk of SCD (Burke et al. 2005, Albert et al. 2010). Accordingly, common variants in the
CPVT-associated CASQ2 gene, the Brugada syndrome-associated GPD1L gene, and
NOS1AP , which has previously been associated with QT interval duration, have also been
reported to predispose to SCD (Kao et al. 2009, Westaway et al. 2011). Of the common
variants associated with QRS complex duration, one SNP in the TKT-CACNA1D-PRKCD
locus was reported to be associated also with risk of SCD (Arking et al. 2011).
Sympathetic activation is involved in generation of ventricular arrhythmias and may
ultimately influence the risk of SCD. Therefore, variants in genes affecting the function of
the autonomic nervous system may predispose to SCD. The Q27E polymorphism in ȕ2-
adrenergic receptor alters the agonist-mediated down-regulation of receptor expression
(Green et al. 1994) and is associated with risk of SCD (Sotoodehnia et al. 2006). Į2B-
adrenergic receptor is involved in vasoconstriction, and the variant form with deletion of
three glutamate residues shows impaired agonist-promoted desensitization and increased
risk of SCD (Snapir et al. 2003). Genes involved in angiotensin-converting enzyme-related
pathways may also contribute to the inherited risk of SCD (Sotoodehnia et al. 2009).

REVIEW OF THE LITERATURE
38Table 6. Genetic variants associated with risk of SCD
Chr. Nearest
geneVariant Study
design* Phenotype Reference
Common variants (identified in population-based or case-control studies)
1p13 CASQ2 rs17500488 1 SCD with CAD Westaway et al. 2011
rs3010396 1 SCD with CAD Westaway et al. 2011
rs7366407 1 SCD with CAD Westaway et al. 2011
1q23 NOS1AP rs10918859 1 SCD with CAD Westaway et al. 2011
rs12084280 1 SCD with CAD Westaway et al. 2011
rs12567209 1 SCD Kao et al. 2009
rs16847548 1 SCD Kao et al. 2009
1q24 SELP V168M 1 VF during MI Elmas et al. 2010
2q11 ADRA2B Ins/Del 1 SCD Snapir et al. 2003
2q24 BAZ2B rs4665058 2 SCD Arking et al. 2011
3p21 TKT rs4687718 1 SCD Arking et al. 2011
3p22 GPD1L rs9862154 1 SCD with CAD Westaway et al. 2011
3p22 SCN5A rs11720524 1 SCD Albert et al. 2010
S1103Y 1 SCD Burke et al. 2005
3q24 AGTR1 rs1492099 1 SCA Sotoodehnia et al. 2009
3q27 KNG1 rs710448 1 SCA in women Sotoodehnia et al. 2009
5q33 ADRB2 Q27E 1 SCD Sotoodehnia et al. 2006
7q22 SERPINE1 Ins/Del 1 SCD with CAD Anvari et al. 2001
9p21 CDKN2BAS rs10757274 1 SCD Newton-Cheh et al. 2009a
rs2383207 1 SCD Newton-Cheh et al. 2009a
11p15 KCNQ1 rs2283222 1 SCD Albert et al. 2010
11q23 IL18 rs187238 1 SCD in men Hernesniemi et al. 2008
13q31 GPC5 rs3864180 2 SCA with CAD Arking et al. 2010
13q34 F7 R353Q 1 SCD in men Mikkelsson and Karhunen
2002
15q22 LIPC -480C>T 1 SCD in men Fan et al. 2007
17p13 GP1BA T145M 1 SCD in men <55 y Mikkelsson et al. 2001
17q21 ITGB3 L33P 1 SCD in men <50 y Mikkelsson et al. 2000
21q21 CXADR rs2824292 2 MI with VF Bezzina et al. 2010
Rare variants (identified in individual patients or families)
1q43 RYR2 Several
mutations1 SCD, SUD Tester et al. 2004, Marjamaa
et al. 2011
3p22 SCN5A Several
mutations1 SCD, SUD Tester and Ackerman 2007,
Albert et al. 2008, Adabag et
al. 2010b
7q36 DPP6 N/A 3 VF Alders et al. 2009
7q36 KCNH2 Several
mutations1 SCD, SUD Chugh et al. 2004b, Tester
and Ackerman 2007,
Adabag et al. 2010b
11p15 KCNQ1 Several
mutations1 SUD Tester and Ackerman 2007
12p11 PKP2 Several
mutations1 SUD Zhang et al. 2012
*Study design: 1 = candidate gene study, 2 = genome-wide association study, 3 = genome-wide
haplotype-sharing study.
CAD = coronary artery disease; Chr. = chromosome; Del = deletion; Ins = insertion; MI = myocardial
infarction; N/A = not available (variant unknown); SCA = sudden cardiac arrest; SCD = sudden cardiac
death; SUD = sudden unexplained death; VF = ventricular fibrillation; y = years.

REVIEW OF THE LITERATURE
39Genes controlling thrombosis and atherosclerosis are apparent candidate genes for
myocardial infarction and SCD. A variant in factor VII of the coagulation cascade has been
suggested to be associated with SCD (Mikkelsson and Karhunen 2002), and a deletion
variant in the SERPINE1 gene encoding the plasminogen activator inhibitor-1, which
regulates endogenous fibrinolysis, has been associated with SCD in patients with coronary
artery disease (Anvari et al. 2001). Several variants involved in platelet activation are
associated with ventricular fibrillation during myocardial infarction and SCD (Mikkelsson et
al. 2000, Mikkelsson et al. 2001, Elmas et al. 2010). A promoter variant in the gene
encoding hepatic lipase predisposes to SCD by a mechanism that may involve elevated total
and high-density lipoprotein cholesterol levels (Fan et al. 2007). A promoter variant in the
interleukin 18 gene decreases the expression of this atherogenic cytokine and reduces the
risk of SCD (Hernesniemi et al. 2008). In the 9p21 chromosomal region, the cyclin-
dependent kinase inhibitor genes CDKN2A and CDKN2B as well as the CDKN2B antisense
RNA gene CDKN2BAS appear to be potential candidate genes for myocardial infarction and
SCD (Helgadottir et al. 2007, Newton-Cheh et al. 2009a).
In addition to these associations detected in studies of previously known candidate genes,
GWA studies have revealed novel genes that may contribute to risk of SCD or ventricular
fibrillation during myocardial infarction (Arking et al. 2010, Bezzina et al. 2010, Arking et
al. 2011). The exact pathogenetic mechanisms associated with these variants are still
unknown. Glypican 5, encoded by GPC5 , is a member of the heparan sulphate proteoglycan
family of proteins that modulates vasculogenesis and angiogenesis after ischaemic injury
(Arking et al. 2010). CXADR encodes a viral receptor protein that may be involved in viral
myocarditis and cardiac conduction (Bezzina et al. 2010). BAZ2B is a bromodomain-
containing gene with an unknown function (Arking et al. 2011), and DPP6 encodes a
putative component of the cardiac I to channel (Alders et al. 2009).

REVIEW OF THE LITERATURE
405. Methods of studying the genetics of cardiac arrhythmia and SCD
5.1. Candidate gene approach
The candidate gene approach is used for investigating the association between variants in a
previously identified gene and a disorder or other trait of interest. Candidate genes may be
selected on the basis of functional similarity to genes with a previously detected association
with the same phenotype. In addition to the exact function of the proteins, this similarity
may concern the cellular compartment, the expression pattern, the regulatory network, or the
interaction partners of the proteins. In ARVC, identification of disease-causing mutations in
plakoglobin and desmoplakin (McKoy et al. 2000, Rampazzo et al. 2002) led to the
discovery of this disorder being commonly caused by desmosomal mutations (Gerull et al.
2004). Similarly, initial recognition of ion channel gene mutations associated with LQTS
(Curran et al. 1995, Wang et al. 1995, Wang et al. 1996) revealed many suitable candidate
genes with a similar function.
Candidate genes may also be identified through a known association with another disorder
that shows phenotypic similarities to the disorder being investigated. For example, after the
identification of LQTS-causing mutations in SCN5A (Wang et al. 1995), this gene has been
associated with many other electrical disorders of the heart (Chen et al. 1998, Schott et al.
1999, Akai et al. 2000, Benson et al. 2003, Ellinor et al. 2008). Also genes associated with a
risk factor for a disorder provide potential candidates for future genetic studies.
Accordingly, genes associated with QT interval or QRS complex duration or J-point
elevation could potentially be associated with fatal arrhythmias. For example, NOS1AP , a
QT-modulating gene, has been associated with SCD (Kao et al. 2009).
Initially, candidate genes may be identified based on positional information gained from
linkage studies, as in the case of TMEM43 mutations in ARVC (Merner et al. 2008) and
KCNQ1 mutations in LQTS (Wang et al. 1996). Another possibility is to identify the
causative mutation for the disorder first in an animal model and subsequently to localize a
homologous gene in the human genome. For example, striatin could be considered a novel
candidate gene in ARVC as it was found to be mutated in the canine model of this disorder
(Meurs et al. 2010). The candidate gene approach enables the identification of small relative
risks, but is restricted by the current knowledge of the pathogenetic mechanisms.

REVIEW OF THE LITERATURE
415.2. Linkage and association studies
Linkage studies are conducted to identify a genetic locus that cosegregates with the trait of
interest in pedigrees. This approach takes advantage of the positional information of genetic
loci in chromosomes. The likelihood of linkage between the genetic marker and the genetic
locus to be found is compared with the likelihood of independent assortment between the
two loci by calculating a logarithm of odds score (Morton 1955). Linkage analysis can be
utilized to map both quantitative and qualitative trait loci. Parametric linkage analysis
requires information on the mode of inheritance, trait and marker allele frequencies,
penetrance, and probability of phenocopies. For complex disorders or traits with unknown
parameters of the genetic model, testing increased allele-sharing among affected relative
pairs using non-parametric linkage analysis may be more suitable. Linkage studies can be
restricted to previously defined chromosomal loci or they can cover the whole genome. For
example, the ARVC8 locus was mapped to 6p24 in a genome-wide linkage scan using
microsatellite markers (Rampazzo et al. 2002), and more recently, the LQT13 locus was
mapped to 11q24 with a similar approach (Yang et al. 2010).
Association studies compare genotype frequencies between, for example, cases and controls
or in a family-based approach. Association may be tested directly between a causative
variant and a phenotype, but commonly this type of study takes advantage of linkage
disequilibrium between alleles of a causative variant and alleles of linked genetic markers.
In this way, location of the causative variant near a genetic marker may be assessed based
on the association between the phenotype of interest and a genetic marker. Association
studies may be targeted at a set of candidate genes or at the whole genome. A GWA study is
a hypothesis-free statistical approach suitable for identifying common genetic variants
associated with a complex trait (Manolio 2010). In this approach, a large set of genetic
markers representing the whole genome is screened for associations with a selected trait,
and modest effect sizes may be discovered using large sample sizes. Several candidate genes
for SCD have been located in GWA studies (Arking et al. 2010, Bezzina et al. 2010, Arking
et al. 2011).

REVIEW OF THE LITERATURE
425.3. Future directions
Development of tools for human genetic studies has benefited from the rapid progress
achieved by international projects aimed at revealing the human genomic sequence and
variation. The Human Genome Project assembled the consensus sequence of the human
genome, providing the exact positional and sequence information of each gene
(International Human Genome Sequencing Consortium 2004). The International HapMap
Project established a haplotype map of the human genome as well as a high-resolution map
of common SNPs and copy number polymorphisms (Altshuler et al. 2010). The 1000
Genomes Project aims to provide a more comprehensive map of human genomic variation,
including rare variants with an allele frequency of •1%, by low-coverage whole-genome
sequencing of 2500 samples (1000 Genomes Project Consortium 2010). The 1000 Genomes
Exon Pilot Project collected deep-coverage exome data to identify rare variants with <1%
allele frequency (Marth et al. 2011). The achievements of these international projects clear
the path for future human genetic studies, including identification of novel disease-causing
mutations by whole-genome and whole-exome sequencing. These large-scale sequencing
methods, together with traditional linkage and association studies, provide a powerful tool
for discovering new disease genes and mechanisms in cardiac arrhythmias and SCD.

AIMS OF THE STUDY
43AIMS OF THE STUDY
This study aimed to identify genetic variants predisposing to cardiac arrhythmia disorders,
specifically ARVC and LQTS, as well as their most severe end-point, SCD. Specific aims
were as follows:
1. To reveal the desmosomal mutation spectrum in Finnish ARVC patients and to
assess the pathophysiological consequences of these mutations (Studies I and II).
2. To estimate the population prevalence of Finnish ARVC-associated desmosomal
mutations and to analyse associated electrocardiographic abnormalities (Study II).
3. To investigate potential modifier genes in LQTS, focusing on a common variant with
the largest known QT interval-prolonging effect in the general population (Study III).
4. To evaluate the association of QT interval and QT genotype score (QT score) with SCD
in the Finnish population (Study IV).
5. To investigate the role of common genetic variants predisposing to arrhythmia and
related ECG abnormalities in risk of SCD (Study V).
6. To assess the association of rare Finnish LQTS and ARVC mutations with SCD
(Study VI).

MATERIALS AND METHODS
44MATERIALS AND METHODS
1. Patient and control samples (I-III)
In Studies I and II, the ARVC patient sample consisted of 29 consecutive probands
diagnosed according to the International Task Force criteria (McKenna et al. 1994) between
1998 and 2004 at the Department of Cardiology, University of Helsinki. In addition,
unpublished data were available from four ARVC probands diagnosed between 2004 and
2009. Available family members (n = 42) of five probands were diagnosed according to the
International Task Force criteria or the modified criteria for first-degree family members
(Hamid et al. 2002).
In Study III, the LQTS patient sample comprised 712 carriers of the four Finnish LQTS
founder mutations from 126 families referred to the Laboratory of Molecular Medicine,
University of Helsinki, between 1999 and 2008. This material included all available carriers
ofKCNQ1 G589D (n = 492), KCNQ1 IVS7-2A>G (c.1129-2A>G, n = 66), KCNH2 L552S
(n = 73), and KCNH2 R176W (n = 88), excluding those with QT-prolonging medication at
the time of ECG recording.
Over 250 DNA samples from ethnically matched blood donors were used as controls to
estimate genotype frequencies in the Finnish background population in Studies I and II. The
studies were approved by the Ethics Committee of the Hospital District of Helsinki and
Uusimaa. Written informed consent was obtained from all participating patients and their
family members.
2. Population cohorts and autopsy materials (II, IV-VI)
Health 2000 (Studies II and IV-VI) is a two-stage stratified cluster sample (n = 8028)
representative of the adult (age •30 years) Finnish population and was collected between
2000 and 2001 (Heistaro 2008). The Mini-Finland Health Survey (Studies IV-VI) is a
similar sample (n = 8000) initially collected between 1978 and 1980 from the Finnish
population (Aromaa et al. 1989). DNA was available from a follow-up study of 985
individuals conducted in 2001. FINRISK 1992 (n = 6051), FINRISK 1997 (n = 8446), and
FINRISK 2002 (n = 8648) (Studies V and VI) are Finnish population-based cohorts

MATERIALS AND METHODS
45collected independently of each other at 5-year intervals from the age group of 25-74 years
(Vartiainen et al. 2010). Gene-expression analysis in Study V was performed in a sample of
510 unrelated Finnish individuals recruited as an extension of the FINRISK 2007 study.
The Helsinki Sudden Death Study (HSDS) and the Tampere Autopsy Study (TASTY) are
series of forensic autopsies utilized in Studies V and VI. HSDS comprised all out-of-
hospital deaths of previously healthy men aged 35-69 years (n = 300) autopsied in Helsinki
between 1991 and 1992 (Tyynelä et al. 2009). TASTY included 740 consecutive medico-
legal autopsies of individuals aged ”97 years performed in Tampere between 2002 and 2004
(Kok et al. 2009). Subjects aged >80 or <25 years were excluded from the statistical
analyses of Studies IV-VI.
3. Phenotypic characterization (I-VI)
The information on proband or family member status, occurrence of syncope, use of beta
blocker medication, and pacemaker or implantable cardioverter-defibrillator in Study III was
based on questionnaires at baseline and at follow-up in 2006. The clinical information in the
Health 2000 and FINRISK studies (Studies II and IV-VI) was collected from health
questionnaires and clinical assessment at baseline as well as from registry-based information
on medications, hospitalizations, and causes of death. In the population cohorts, the causes
of death were classified as either probable SCD, possible SCD, unlikely SCD, or unknown
cause of death by two independent physicians reviewing data from baseline examinations,
the Causes of Death Registry, the Hospital Discharge Registry, the Drug Reimbursement
Registry, and the Pharmacy Database as described in Studies IV-VI. In cases of discrepancy,
two additional physicians reviewed the data independently, and final adjudication was
achieved by consensus of all four physicians. In the series of forensic autopsies, causes of
death were classified accordingly based on autopsy results. Probable and possible SCDs
were pooled for the analyses.
Standard 12-lead electrocardiography was used in all ECG measurements. QT interval was
measured manually in lead II in the LQTS patient sample (Study III) and automatically with
manual confirmation as a mean of 12 leads in the Health 2000 study (Studies II and IV). QT
interval was corrected for heart rate by using either linear regression (Studies II and III),
Bazett’s formula (Bazett 1920) (Studies II and III), or the nomogram-correction method

MATERIALS AND METHODS
46(Karjalainen et al. 1994) (Study IV). Heart rate, PQ interval, and QRS duration were
measured automatically as described in Study II.
4. Molecular genetic studies (I-VI)
DNA was extracted from peripheral blood lymphocytes using standard methods (Studies I-
III): the phenol-chloroform method (Blin and Stafford 1976) or the salting-out method using
PureGene DNA Purification Kit (Gentra, Minneapolis, MN, USA). Genomic DNA was
amplified using polymerase chain reaction (PCR) (Mullis et al. 1986) with a primer pair
specific for each exon or mutation under investigation (Studies I-VI).
Disease-causing mutations were searched for in 29 unrelated ARVC patients using direct
sequencing (Sanger et al. 1977) of PKP2b (NM_004572), DSP isoform I (NM_004415),
DSG2 (NM_001943), DSC2a (NM_024422), and DSC2b (NM_004949) exons (Studies I
and II). Four additional probands were screened for mutations in PKP2b exons (Lahtinen
AM et al. unpublished data). PCR products were purified with exonuclease I and shrimp
alkaline phosphatase and sequenced with BigDye Terminator v3.1 and ABI 3730 DNA
Analyzer (Applied Biosystems, Foster City, CA, USA). Electropherograms were analysed
with Sequencher 4.5-4.8 software (Gene Codes Corporation, Ann Arbor, MI, USA).
In Study II, large deletions and duplications were screened using multiplex ligation-
dependent probe amplification (MLPA) detecting relative copy number changes by
measuring the hybridization of sequence-specific probes (Schouten et al. 2002). The Salsa
MLPA kit P168 (MRC Holland, Amsterdam, the Netherlands) includes probes for all PKP2
exons and for selected exons in DSP,JUP,RYR2 , and TGFB3 .
In Study I, haplotype analysis of PKP2 was performed by genotyping of six polymorphic
repeat markers using PCR with fluorescent-labelled primers and capillary electrophoresis
with ABI 3730 DNA Analyzer.
Known genetic variants were detected by a restriction enzyme digestion method in Studies
I-III. In this method, PCR products are cleaved with a restriction endonuclease recognizing
and cleaving either the wild-type or mutated sequence. Resulting DNA fragments are
separated in agarose gel or polyacrylamide gel electrophoresis. In Studies I and II, those

MATERIALS AND METHODS
47variants with no suitable recognition sequence were detected by primer-induced restriction
analysis (PIRA) creating an artificial recognition sequence in the PCR product (Kumar and
Dunn 1989).
Large population cohorts and autopsy materials were genotyped using Sequenom MALDI-
TOF mass spectrometry (Storm et al. 2003) (MassARRAY Analyzer Compact, Sequenom
Inc., San Diego, CA, USA) in Studies II and IV-VI. The Sequenom iPLEX assay can be
used to genotype up to 36 SNPs in a single well by single-base extension of a hybridized
primer and subsequent mass analysis. KCNH2 R176W was genotyped using Custom
TaqMan SNP Genotyping Assay (Applied Biosystems) in Study VI. The discrimination of
alleles with fluorescent-labelled probes was performed using 7900HT Fast Real-Time PCR
System and SDS2.3 software (Applied Biosystems).
In Study V, gene expression analysis was performed to detect potential effects of SNPs on
gene expression regulation. Genome-wide RNA level quantification was achieved in RNA
samples extracted from peripheral blood using Illumina HumanHT-12 Expression
BeadChips (Illumina Inc., San Diego, CA, USA), as described previously (Inouye et al.
2010). Expression intensity of probes within 2 Mb of the SNP in interest were analysed
using linear regression.
5. Microscopic analyses (I, II)
Endomyocardial biopsy samples were obtained from two ARVC patients and two control
patients with hypertrophic cardiomyopathy and CPVT, respectively. Immunohistochemistry
with mouse desmoglein-2, plakophilin-2, plakoglobin, desmoplakin 1/2 (Progen,
Heidelberg, Germany), mouse N-cadherin (Sigma-Aldrich, St. Louis, MO, USA), and rabbit
connexin 43 antibodies (Sigma-Aldrich) was performed using the Advance HRP system
(DakoCytomation, Glostrup, Denmark) or the Vectastain Elite ABC kit (Vector
Laboratories, Burlingame, CA, USA). Quantification of the immunoreaction was achieved
using Alexa Fluor 594 detection (Invitrogen, Carlsbad, CA, USA) and measuring the
integrated signal density using the ImageJ program. Electron microscopy was performed on
endomyocardial tissue samples retrieved from paraffin blocks using a Jeol JEM 1200EX or
Jeol JEM 1400 electron microscope.

MATERIALS AND METHODS
486. Statistical analyses (I-VI)
Hardy-Weinberg equilibrium was confirmed by Chi-square test or, in the case of rare
variants, by Fisher’s exact test. Variants with Hardy-Weinberg p value below the selected
threshold (Studies I-III and VI: 0.05; Study IV: 0.0001; Study V: 0.002) were excluded from
the study. In the genotyping quality control of large population cohorts, selected thresholds
were applied for the genotyping success of variants (Study IV: 80%; Study V-VI: 90%) and
samples (Study IV: 57%; Studies V-VI: 80%). Genotyping quality was also monitored by
using sex markers, duplicate samples, and positive control samples.
Discrete variables were tested by Chi-square test or Fisher’s exact test. The effect of
genotype was analysed using an additive model (number of minor alleles coded as 0, 1, 2).
In Study II, prevalence estimates of mutations were calculated from the weighted study
population, as described previously (Aromaa and Koskinen 2004). In Study VI, prevalence
estimates were calculated with survey-specific sampling weights, and the estimation was
stratified by study, sex, study region, and 10-year age group. In Studies IV and V, QT score
was calculated for each individual to aggregate the information of 12-14 QT interval-
prolonging SNPs. QT score represents the predicted effect of genotype on QT interval. It was
calculated by multiplying the previously reported effect estimate of each SNP in ms
(Newton-Cheh et al. 2009b, Pfeufer et al. 2009) by the number of coded alleles and finally
calculating the sum over all SNPs.
In Studies II and III, the association of genotype with PR interval, QRS duration, QT
interval, and heart rate was investigated by linear regression, adjusting for age, sex, and
heart rate. In Study III, an additional model included also a multiplicative interaction term
between genotype and sex. In Study IV, the association of genotype or QT score w i t h Q T
interval nomogram-corrected for heart rate (QT Nc) was evaluated by linear regression,
adjusting for age, sex, and geographic region. In Studies IV and V, the association of
genotype, QT score, or QT Nc with SCD was studied by Cox proportional hazards model, using
age as the time scale and adjusting for sex and geographic region. In the SCD analyses,
additional adjustments included QT-prolonging and QT-shortening medication, prevalent
CHD, established cardiovascular risk factors, and prevalent heart failure. In Study V, the
autopsy series were analysed using logistic regression by comparing probable and possible
SCDs to unlikely SCDs and adjusting for age at death and sex. The risk estimates were

MATERIALS AND METHODS
49pooled using inverse variance-weighted, fixed-effects meta-analysis. When significant
heterogeneity occurred (I2 > 0.5), random-effects meta-analysis was applied. In Study V, a
similar meta-analysis was performed for all-cause and cardiac mortality, adjusting for sex
and geographic region. The association between baseline cardiovascular risk factors and
SCD was also investigated in Study V using Cox proportional hazards regression. In Study
VI, the association between rare mutations and SCD was analysed using Fisher’s exact test.
Statistical analyses were performed with SPSS 11.0-17.0 (SPSS Inc., Chicago, IL, USA)
and R version 2.11. Two-tailed p < 0.05 was considered statistically significant. Bonferroni-
corrected significance threshold was applied in Studies IV and V.

RESULTS
50RESULTS
1. Desmosomal mutations in ARVC patients and families
Out of the 29 unrelated ARVC patients in Studies I and II, three carried mutations in PKP2 ,
one in DSG2 , and one in DSP (patients A-E, Table 7). No mutations were identified in
DSC2 . Occurrence of large genomic rearrangements in PKP2 was excluded using MLPA.
One of the four additional patients (patient F, Table 7) carried a mutation in PKP2 (Lahtinen
AM et al. unpublished data). In total, six (18%) of the 33 probands carried a desmosomal
mutation. PKP2 mutations accounted for two-thirds of these mutations. All mutations
occurred in an evolutionary conserved region and were absent in 250 control samples.
Pedigree data are presented in Figure 5.
Table 7. ARVC patients with desmosomal mutations
Mutation Proband
Gene Nucleotide* Amino acidAge†Sex Symptoms
A PKP2
PKP2184C>A
1839C>GQ62K
N613K32 M Presyncope at exercise,
VT in LBBB morphology
B PKP2 176A>T Q59L 39 F Syncope at exercise,
VT in LBBB morphology
C PKP2 176A>T Q59L 42 M Syncope at exercise,
VT in LBBB morphology
D DSG2 3059_3062
delAGAGE1020AfsX18 24 M Stress-related VT in LBBB
morphology
E DSP 4117A>G T1373A 29 M VT in LBBB morphology at
exercise
F PKP2 563delT L188PfsX2 29 F VT in LBBB morphology at
exercise originating from right
ventricular apex
*Numbering of nucleotides starts from the methionine translation initiation codon.
†Age at onset of symptoms.
F = female; LBBB = left bundle branch block; M = male; VT = ventricular tachycardia.
Proband A was a compound heterozygous carrier of two PKP2 missense mutations, Q62K
and N613K. This patient presented with arrhythmia, adipose and fibrous tissue replacement
of the myocardium, and right ventricular structural abnormalities typical of ARVC. The
family data suggested a pathogenic role for the novel N613K mutation, but uncertain
pathogenicity for Q62K, which has been reported as an unclassified variant also in other
populations (van Tintelen et al. 2006, Xu et al. 2010). PKP2 Q59L was detected in two
unrelated probands, B and C. Both of them featured arrhythmia, ECG abnormalities, and

RESULTS
51right ventricular structural alterations. Families B and C included a total of ten mutation
carriers, of which two (20%) fulfilled the Task Force diagnostic criteria and one (10%) the
modified diagnostic criteria for first-degree family members. PKP2 Q59L was linked with
an identical haplotype in both families, indicating a common founder individual for these
probands (Study I). PKP2 563delT was detected in a proband who suffered from episodes of
ventricular tachycardia and was treated with an implantable cardioverter-defibrillator
(Lahtinen AM et al. unpublished data). The mutation was also present in her mother, whose
ECG showed inverted T waves in leads V1-V3.
DSG2 3059_3062delAGAG and DSP T1373A were detected in probands D and E,
respectively. The DSG2 four-nucleotide deletion generates a frameshift that deletes the 99
carboxy-terminal amino acids of desmoglein-2. Both of these probands presented with
arrhythmia, ECG abnormalities, and right ventricular structural alterations. Proband D also
featured adipose and fibrous tissue replacement. Of the five deletion carriers in Family D,
three (60%) fulfilled either the Task Force or the modified diagnostic criteria for ARVC.

RESULTS
52Figure 5. Family data of six ARVC probands with desmosomal mutations (Studies I and II, and Lahtinen
AM et al. unpublished data). N/A = no clinical or genetic information available.

RESULTS
532. Effects of desmosomal mutations at the cellular level
Endomyocardial biopsy samples of two ARVC probands (A and D) were analysed in
Studies I and II by immunohistochemical staining of desmosomal proteins and electron
microscopy. The samples of both patients showed adipose and fibrous tissue replacement of
cardiac myocytes characteristic of ARVC.
Plakophilin-2 staining of the sample of proband A with mutations PKP2 Q62K and N613K
showed mild reduction of plakophilin-2 immunoreactive signal as well as less linearly
organized intercalated disk structure. Immunohistochemical stainings of the sample of
proband D with DSG2 deletion showed reduced immunoreactive signal for all desmosomal
proteins assessed: desmoglein-2, plakophilin-2, desmoplakin, and plakoglobin (Figure 6). N-
cadherin, a marker of tissue quality, and the gap junctional protein connexin 43 showed no
reduction compared with the control samples.
Electron microscopic analysis of the intercalated disk area revealed more vacuolated
intercalated disks in both ARVC samples than in the control samples. In the sample of
proband A, fewer desmosomes were detected and some of the desmosomal junctions
appeared small and irregularly oriented. In the sample of proband D, occasional
disorganization of the cell-cell junctions was observed.

RESULTS
54Figure 6. Immunohistochemical staining of a control sample and the ARVC sample with DSG2
3059_3062delAGAG for desmoglein-2 (A-B), plakophilin-2 (C-D), plakoglobin (E-F), desmoplakin (G-H),
and N-cadherin (I-J). The arrows point at selected intercalated disk structures. Bar: 50 ȝm.

RESULTS
553. Desmosomal variants in the Finnish population
The population prevalence and clinical phenotypes of five desmosomal ARVC-related
mutations identified in Studies I and II were investigated in Studies II and VI. In addition,
possible phenotypic associations of two common PKP2 polymorphisms were evaluated in
the Health 2000 population sample in Study II. The combined prevalence of the five
desmosomal mutations was 48 per 10 000 (95% confidence interval [CI] 33-71 per 10 000)
in the Health 2000 study (Study II). A similar prevalence estimate (39 per 10 000, 95% CI
31-50 per 10 000) was detected in Study VI using both Health 2000 and FINRISK
population cohorts (Table 8). The most prevalent mutation was PKP2 Q59L, which was
carried by 29 per 10 000 Finns (95% CI 22-39 per 10 000). The carriers of this mutation
clustered in Southeastern Finland, further supporting the hypothesis that PKP2 Q 59L is a
founder mutation in the Finnish population.
Arrhythmia (self-reported or physician-diagnosed) occurred in 11 (35%) of the total of 31
mutation carriers in the Health 2000 study (Study II). ECG abnormalities characteristic of
ARVC (T-wave inversion in leads V2 and V3 or QRS complex duration •110 ms) were
detected in 6 (19%) carriers. Arrhythmia or ECG abnormalities occurred altogether in 16
carriers (52%), and only one mutation carrier featured both types of clinical characteristics.
APKP2 Q59L carrier had a diagnosis of ventricular tachycardia and encountered SCD at
the age of 46 years. The ECG of one PKP2 Q62K carrier showed frequent premature
ventricular complexes, and one DSP T1373A carrier was diagnosed with paroxysmal
tachycardia. DSP T1373A was associated with PR interval prolongation of 33 ms in the
population sample ( p = 0.005).
The two PKP2 polymorphisms L366P and I531S had similar allele frequencies in the Health
2000 population sample (19.5% and 2.2%, respectively) as in the ARVC patient material
(17% and 2%, respectively). I531S was not associated with arrhythmia or ECG
abnormalities in Study II. The minor allele of L366P was associated with PR interval
prolongation ( p = 0.036) as well as reduced occurrence of arrhythmia in clinical
examination ( p = 0.028) and T-wave inversion in ECG ( p = 0.040).

RESULTS
56Table 8. Desmosomal mutations in the Finnish population
Study II: Health 2000 (n = 6334) Study VI: Health 2000,
FINRISK 1992, 1997,
and 2002 (n = 28 465)Gene Mutation
n n (%) with
arrhythmia or ECG
abnormalities*Prevalence per
10 000 (95% CI)n Prevalence per
10 000 (95% CI)
PKP2 Q59L 19 10 (53) 30.1 (18.3-49.4) 85 29.3 (22.2-38.6)
PKP2 Q62K 6 2 (33) 8.5 (3.7-19.6) 12 4.3 (2.3-8.1)
PKP2 N613K 0 – – 0 –
DSG2 3059_3062
delAGAG1 1 (100) 1.7 (0.2-12.2) 5 2.7 (1.0-7.5)
DSP T1373A 5 3 (60) 8.0 (3.4-19.0) 10 3.8 (1.6-8.7)
Total of all mutations 31 16 (52) 48.5 (33.2-70.8) 112 39.3 (31.0-49.9)
*Arrhythmia in clinical examination, self-reported arrhythmia, T-wave inversion (V2-V3), or QRS complex
•110 ms.
CI = confidence interval; ECG = electrocardiography.
4. KCNE1 D85N as a sex-specific disease-modifying variant in LQTS
Study III assessed the clinical significance of a common QT-prolonging variant, KCNE1
D85N, in 712 carriers of the four Finnish LQTS founder mutations ( KCNQ1 G589D,
KCNQ1 IVS7-2A>G, KCNH2 L552S, and KCNH2 R176W). In this combined patient
group, KCNE1 D85N was associated with a 13-ms prolongation of QT interval (standard
error 6.0 ms, p = 0.028). KCNQ1 G589D was the most prevalent founder mutation (n =
492). In males with KCNQ1 G589D, KCNE1 D85N was associated with a QT interval
prolongation of 26 ms (standard error 8.6 ms, p = 0.003) (Figure 7). Confining the analysis
to males ”16 years of age did not change the result. In females with KCNQ1 G589D, no
association was observed ( p = 0.935). The multiplicative interaction term for KCNE1 D85N
and sex attained a significance of p = 0.028, which indicates that the effect of D85N on QT
interval may be sex-specific in this Finnish LQTS founder mutation group.
The association of KCNE1 D85N with clinical variables reflecting disease severity was
studied in KCNQ1 G589D mutation carriers. The percentage of probands was higher in
KCNE1 D85N heterozygotes (31%) than in non-carriers (12%, p = 0.042) and the
percentage of patients using beta blocker medication was higher in KCNE1 D85N
heterozygotes (81%) than in non-carriers (47%, p = 0.010) (Figure 8). The percentage of
patients having experienced syncope and patients with pacemaker or implantable
cardioverter-defibrillator did not differ significantly between the KCNE1 D85N
heterozygotes (44% and 6.3%, respectively) and non-carriers (36% and 4.5%, respectively).

RESULTS
57Figure 7. Heart rate-corrected QT interval (QTc) in the different KCNE1 D85N genotype classes in
KCNQ1 G589D carrier males (A) and females (B). Box plots show medians and interquartile ranges.
Figure 8. Occurrence of selected clinical variables in the different KCNE1 D85N genotype classes in
KCNQ1 G589D carriers. Percentage of probands (A), patients having experienced syncope (B), patients
with beta blocker medication (C), and patients with pacemaker or implantable cardioverter-defibrillator
(ICD) (D). N indicates the number of individuals with the selected clinical feature out of all individuals in
the corresponding genotype group.

RESULTS
585. QT interval and QT score in SCD
The association of QT score, a genotype score representing the predicted effect of 14 QT
interval-associated SNPs, with SCD was investigated in the Health 2000 and Mini-Finland
population cohorts (n = 6808, n of SCDs = 116) in Study IV. In addition, the relationships
between QT score and QT Nc as well as between QT Nc and SCD were investigated in the Health
2000 cohort (n = 6091, n of SCDs = 99). Thirteen of the QT score SNPs were independently
associated with QT Nc (p < 0.007, Table 9). The effect sizes of individual SNPs ranged from
1 ms to 10 ms. The association between rs17779747 near KCNJ2 and QT Nc was statistically
non-significant after correction for multiple testing ( p = 0.039). The linear QT score was
associated with QT Nc (p < 10-107) and explained 8.6% of QT Nc variation after adjustment for
age, sex, and geographic region.
A 10-ms increase in QT Ncwas associated with a 19% increased risk of SCD in the Health
2000 study (95% CI for hazard ratio [HR] 1.07-1.32, p = 0.002). The association of a
diagnostic threshold for prolonged QT interval (QT Nc > 450 ms for males and > 470 ms for
females) with risk of SCD was also confirmed in this study. The risk of SCD was 1.3%
below and 24% above the threshold QT interval (HR 13.3, 95% CI 4.7-37.7, p = 1×10-6).
Adjustment for prevalent CHD and use of QT-altering medication did not change the
results.
The QT score SNPs were not associated with SCD independently (Table 9) and neither was
the linear QT score, which combines the QT-prolonging effects of these SNPs. The risk of
SCD was suggestively increased in the highest QT score quintile compared with the middle
quintile in Study IV (HR 1.92, 95% CI 1.05-3.58, p = 0.04), but this result was not
replicated in Study V in the meta-analysis of four population cohorts and two series of
forensic autopsies (total n = 28 323, n of SCDs = 716).

RESULTS
59Table 9. Effects of individual SNPs on QT interval and SCD
QTNc SCD Chr. Position* Nearest
geneSNP Coded
alleleCAF
ȕ
(ms)p HR p
1p36 6 201 957 RNF207 rs846111 C 0.25 2.9 8.9×10-121.29 0.08
1q23 160 300 514 NOS1AP rs12143842 T 0.24 5.0 5.1×10-311.26 0.12
1q24 167 366 107 ATP1B1 rs10919071 A 0.89 3.3 7.5×10-80.89 0.58
3p22 38 568 397 SCN5A rs12053903 T 0.59 1.3 6.0×10-41.02 0.89
6q22 118 759 897 PLN rs12210810 G 0.97 3.6 5.9×10-40.96 0.92
6q22 119 100 335 PLN rs11756440 A 0.47 1.9 1.2×10-70.99 0.93
7q36 150 268 796 KCNH2 rs4725982 T 0.28 2.0 2.4×10-61.23 0.16
7q36 150 276 467 KCNH2 rs1805123 A 0.82 2.6 1.3×10-71.12 0.50
11p15 2 441 379 KCNQ1 rs2074238 C 0.91 6.3 1.9×10-240.87 0.51
11p15 2 458 895 KCNQ1 rs12576239 T 0.16 2.6 4.6×10-80.98 0.91
16p13 11 601 037 LITAF rs735951 G 0.58 1.4 1.6×10-41.05 0.72
16q21 57 124 739 CNOT1 rs37062 A 0.74 2.4 1.0×10-80.94 0.69
17q24 66 006 587 KCNJ2 rs17779747 G 0.75 0.88 0.039 1.19 0.29
21q22 34 743 550 KCNE1 rs1805128 A 0.01 10.4 9.0×10-111.11 0.85
*According to NCBI genome build 36.
ȕ = e ffe c t s i z e ; C A F = c o d e d a l l e l e fr e q u e n c y ; Ch r . = ch r o m o s o m e ; H R = h a za r d r a t i o ; Q T Nc = QT
interval nomogram-corrected for heart rate; SCD = sudden cardiac death; SNP = single nucleotide
polymorphism.
6. Common variants and cardiovascular risk factors in SCD
Study V explored the association of 28 common SNPs with increased risk of SCD in four
population cohorts and two series of forensic autopsies (total n = 28 323, n of SCDs = 716).
The candidate SNPs were selected based on previously reported associations with
arrhythmia or related ECG phenotypes such as QT interval. Two SNPs were associated with
SCD after Bonferroni correction for multiple testing: SCN5A rs41312391 (relative risk [RR]
1.27 per minor T-allele, 95% CI 1.11-1.45, p = 3.4×10-4) and rs2200733 in 4q25 (RR 1.28
per minor T-allele, 95% CI 1.11-1.48, p = 7.9×10-4) (Figure 9, Table 10). No heterogeneity
between the different study cohorts was detected for the effect sizes of these two SNPs (I2 =
0.00). Confining the phenotype to probable SCD did not change the relative risk estimates
(RR 1.28, 95% CI 1.11-1.48, p = 6×10-4 for rs41312391 and RR 1.27, 95% CI 1.08-1.49, p =
0.003 for rs2200733). We also replicated the previously reported association of rs2383207
in 9p21 near the CDKN2A and CDKN2B g enes w ith SCD (R R 1.13 per G -allele, 95% CI
1.01-1.26, p= 0.036).
Gene expression analysis in peripheral blood suggested that the minor allele of rs2200733
could be associated with increased expression of the nearest gene PITX2 (p = 0.013), and

RESULTS
60the minor allele of rs41312391 with increased expression of WDR48 (p = 0.037). Both
SNPs, rs41312391 and rs2200733, remained significantly associated with SCD after
additional adjustment for cardiovascular risk factors (high-density lipoprotein-total
cholesterol concentration ratio, systolic blood pressure, prevalent diabetes, body mass index,
current and former cigarette smoking, and leisure-time physical activity) and prevalent CHD
(model 2), QT-prolonging and QT-shortening medication (model 3), and prevalent heart
failure (model 4).
Study V also replicated the association of previously reported cardiovascular risk factors,
including male gender, high systolic blood pressure, prevalent diabetes, current and former
cigarette smoking, Eastern Finnish residency, and low physical activity, with SCD ( p <
0.05). Prevalent coronary heart disease ( p < 2.2×10-16) and digoxin use ( p = 2.7×10-8) were
also associated with increased risk of SCD, whereas QT-prolonging medication was not ( p =
0.57).
Figure 9. Forest plot of the relative risks of the two common SNPs associated with SCD. Point estimates
for relative risk of SCD are represented by squares (area proportional to the inverse-variance weight)
and 95% confidence intervals by horizontal lines. The widest point of the diamond represents the relative
risk estimate in the meta-analysis and the lateral tips show the 95% confidence interval. HSDS = Helsinki
Sudden Death Study; SCD = sudden cardiac death; TASTY = Tampere Autopsy Study.

RESULTS
61Table 10. Results from the SCD meta-analysis of common genetic variants
SCD SNP Coded
alleleCAF FINRISK
1992 HRFINRISK
1997 HRFINRISK
2002 HRHealth
2000 HRHSDS
ORTASTY
ORI2RR 95% CI p
rs846111 C 0.26 0.93 1.05 1.14 1.29 0.88 1.04 0.00 1.06 0.93-1.20 0.39
rs2880058 G 0.36 0.99 1.02 1.19 1.08 0.93 1.16 0.00 1.05 0.94-1.18 0.41
rs12036340 G 0.25 0.95 1.02 1.36 1.29 0.92 1.20 0.00 1.10 0.97-1.25 0.15
rs12143842 T 0.25 0.88 1.06 1.26 1.26 0.91 1.19 0.00 1.08 0.95-1.23 0.25
rs10919071 G 0.11 0.94 0.88 1.48 1.12 0.91 0.80 0.00 1.00 0.83-1.19 0.96
rs1805126 G 0.44 1.22 1.07 1.51 0.99 1.10 1.16 0.00 1.14 1.02-1.28 0.02
rs12053903 C 0.41 1.28 1.04 1.38 0.98 1.12 1.06 0.00 1.12 1.00-1.26 0.045
rs41312391 T 0.20 1.43 1.32 1.41 1.05 1.20 1.09 0.00 1.27 1.11-1.45 3.4×10-4
rs3922844 T 0.26 0.90 1.24 1.04 1.29 0.88 0.96 0.10 1.08 0.95-1.23 0.22
rs6599219 G 0.37 0.91 1.01 1.09 0.80 1.01 1.05 0.00 0.97 0.86-1.08 0.54
rs7372712 T 0.16 0.90 0.88 1.25 1.16 0.84 1.14 0.00 1.01 0.87-1.18 0.86
rs2200733 T 0.16 1.15 1.15 1.55 1.42 1.12 1.58 0.00 1.28 1.11-1.48 7.9×10-4
rs10033464 T 0.17 1.28 0.92 1.19 0.94 1.32 1.20 0.00 1.10 0.94-1.28 0.23
rs1042714 G 0.38 0.78 1.22 0.81 0.96 1.07 1.12 0.44 1.00 0.89-1.12 0.99
rs12210810 C 0.03 0.79 0.77 1.77 1.04 0.52 0.68 0.00 0.94 0.68-1.30 0.71
rs4725982 T 0.29 0.73 1.08 1.00 1.23 1.11 1.66 0.62 1.09 0.89-1.34 0.42
rs1805123 G 0.17 1.39 1.06 0.55 0.89 N/A N/A 0.69 0.97 0.71-1.34 0.87
rs3807375 T 0.44 0.86 1.05 1.09 1.52 1.29 1.49 0.61 1.18 0.98-1.41 0.08
rs2383207 G 0.43 1.04 1.00 1.15 1.06 1.31 1.61 0.31 1.13 1.01-1.26 0.04
rs2074238 T 0.09 0.98 0.77 1.22 1.15 1.03 1.03 0.00 1.00 0.82-1.21 0.98
rs757092 G 0.36 0.89 0.91 0.82 1.25 0.94 0.90 0.03 0.95 0.85-1.07 0.43
rs12576239 T 0.16 1.05 0.73 0.87 0.98 1.17 0.91 0.00 0.92 0.79-1.08 0.32
rs10798 G 0.36 0.89 1.08 1.06 1.15 1.20 0.91 0.00 1.04 0.93-1.17 0.49
rs735951 A 0.42 0.81 1.06 1.00 0.95 0.95 1.00 0.00 0.96 0.86-1.08 0.50
rs37062 G 0.27 1.02 0.95 1.03 1.06 1.27 0.97 0.00 1.03 0.90-1.16 0.71
rs17779747 T 0.25 0.81 1.04 1.03 0.84 0.95 1.05 0.00 0.95 0.83-1.08 0.42
rs1805128 T 0.02 0.89 0.71 0.89 1.11 0.52 1.00 0.00 0.84 0.53-1.34 0.46
rs727957 T 0.17 0.93 0.93 0.89 0.71 0.96 1.11 0.00 0.92 0.79-1.07 0.26
CAF = coded allele frequency; Chr. = chromosome; CI = confidence interval; HR = hazard ratio; HSDS = Helsinki Sudden Death Study; N/A = not available; OR = odds
ratio; RR = relative risk; SCD = sudden cardiac death; SNP = single nucleotide polymorphism; TASTY = Tampere Autopsy Study.

RESULTS
627. Rare arrhythmia-associated mutations in the Finnish population
The prevalence and clinical phenotype, including the occurrence of arrhythmia, heart
failure, and SCD, of ten arrhythmia-associated gene mutations (Table 11) were studied in
the FINRISK 1992, 1997, 2002, and Health 2000 (including the Mini-Finland Health
Survey) population cohorts and two series of forensic autopsies, HSDS and TASTY (Study
VI, total n = 29 290, n of SCDs = 715). The combined prevalence of these ten mutations was
79 per 10 000 individuals in the Finnish population (Table 11). Within Finland, substantial
regional differences in the prevalences of KCNQ1 G589D, KCNH2 L552S, KCNH2
R176W, and PKP2 Q59L were identified. In addition, the municipality of birth of DSP
T1373A and RYR2 R3570W carriers showed marked geographic clustering.
In the population cohorts, 14 (6.5%) of the mutation carriers suffered from arrhythmia and 7
(3.3%) from heart failure based on information obtained from the national health care
registries. Of the 715 probable and possible SCD cases in the population and autopsy
samples, 7 (1.0%) carried one of the ten arrhythmia-associated mutations (Table 11), but
none of the mutations were associated with significantly increased risk of SCD ( p > 0.05).
The yearly incidence of probable and possible SCD was 0.19% for the mutation carriers and
0.18% for non-carriers.
Table 11. Rare arrhythmia mutations in the Finnish population and autopsy samples
FINRISK and Health 2000 population
cohorts (n = 28 465)HSDS and TASTY
(n = 825)Gene Mutation
n Prevalence per
10 000 (95% CI)n of SCD
casesn n of SCD
cases
KCNQ1 G589D 28 9.9 (6.6-14.9) 0 0 –
KCNQ1 IVS7-2A>G 1 0.4 (0.05-2.6) 0 0 –
KCNH2 L552S 26 8.0 (5.1-12.6) 0 1 1
KCNH2 R176W 42 17.6 (12.4-24.8) 1 2 2
PKP2 Q59L 85 29.3 (22.2-38.6) 2 1 0
PKP2 Q62K 12 4.3 (2.3-8.1) 0 0 –
PKP2 N613K 0 – – 0 –
DSG2 3059_3062delAGAG 5 2.7 (1.0-7.5) 0 0 –
DSP T1373A 10 3.8 (1.6-8.7) 0 1 0
RYR2 R3570W 7 2.6 (1.0-6.3) 1 0 –
Total of all mutations 215* 78.5 (66.8-92.3) 4 5 3
*One subject with both PKP2 Q59L and RYR2 R3570W.
CI = confidence interval; HSDS = Helsinki Sudden Death Study; SCD = sudden cardiac death; TASTY =
Tampere Autopsy Study.

DISCUSSION
63DISCUSSION
1. Desmosomal defects underlying Finnish ARVC
1.1. Desmosomal mutations and their cellular consequences
Previous studies have revealed desmosomal mutations in approximately half of the ARVC
patients examined genetically (Pilichou et al. 2006, den Haan et al. 2009, Christensen et al.
2010, Fressart et al. 2010, Xu et al. 2010, Cox et al. 2011). In the Finnish ARVC patient
material, however, only 18% of probands were shown to carry a desmosomal mutation
(Studies I and II, and Lahtinen AM et al. unpublished data). This difference may reflect
population-specificity in the genetics of ARVC as well as still unidentified genes and
pathways in the pathogenesis of this disorder. All mutations in the present study, except
PKP2 Q62K, were novel, which further underlines the population-specific differences in
ARVC.
Compound heterozygosity and digenic heterozygosity of desmosomal mutations have
increasingly been recognized in ARVC patients (Bhuiyan et al. 2009, den Haan et al. 2009,
Bauce et al. 2010). A compound heterozygous patient with PKP2 Q62K and N613K was
reported in Study I. It seems that less severe mutations are unable to cause the disease alone,
requiring an additional trigger, such as another mutation, for disease progression. Since
ARVC is a common cause of death in young athletes (Thiene et al. 1988) and endurance
training accelerates the disease development in a mouse model (Kirchhof et al. 2006),
physical exertion may represent an external factor inducing the disease in susceptible
patients.
PKP2 Q59L was reported as a Finnish ARVC founder mutation with a penetrance of 20% in
Studies I and II. It is located in the conserved HR2 domain within the plakophilin-2 head
domain, which mediates interactions with other desmosomal proteins (Chen et al. 2002).
Functional studies have shown that this mutation disrupts the interaction between
plakophilin-2 and desmoplakin (Hall et al. 2009). Plakophilin-2 with Q62K is degraded
more rapidly than wild-type protein and fails to recruit desmoplakin to desmosomes (Hall et
al. 2009). PKP2 N613K resides in a conserved amino acid sequence, which may participate
in protein binding (Choi and Weis 2005). Study I demonstrated less linear intercalated disks
and irregular desmosomal structures in the cardiomyocytes of the compound heterozygous

DISCUSSION
64carrier of PKP2 N613K and Q62K. These plakophilin-2 missense mutations may thus lead
to decreased cytoskeletal attachment at intercalated disks, which may predispose to tissue
disruption during physical stress. PKP2 563delT abolishes almost half of the plakophilin-2
head domain and all armadillo repeats, potentially having deleterious consequences on the
desmosomal structure and protein interactions.
DSP T1373A may affect the homodimerization of desmoplakin since it is located in the
coiled-coil rod domain of this protein (Study II). DSG2 3059_3062delAGAG truncates the
desmoglein-specific cytoplasmic region, which is involved in interactions with desmosomal
proteins residing in the cytoplasm (Kami et al. 2009). This mutation leads to a diminished
immunoreactive signal for several desmosomal proteins: desmoglein-2, plakophilin-2,
plakoglobin, and desmoplakin (Study II), indicating that the desmosomal structure is
affected as an ensemble. This impairment leads to disorganization of the intercalated disk
structure, as detected in the electron microscopic analyses in Study II. The reduction of
plakoglobin at the desmosomes may disturb Wnt/ ȕ-catenin signalling (Garcia-Gras et al.
2006, Asimaki et al. 2009), which could result in cardiomyocyte apoptosis and replacement
by adipose and fibrous tissue (Figure 10).
Figure 10. Main cellular processes in cardiomyocytes contributing to arrhythmia susceptibility.
Impairment of any of these important cellular functions may predispose to potentially life-threatening
arrhythmias. The functions and interplay of the principal genes in this study are also shown. Ca2+ =
calcium ion; K+ = potassium ion; Na+ = sodium ion.

DISCUSSION
651.2. Desmosomal mutations at the population level
The prevalence of desmosomal mutations in the general Finnish population, 1:200-1:250
(Studies II and VI), is considerable higher than the published estimation of ARVC
prevalence 1:1000-1:5000 (Rampazzo et al. 1994, Peters et al. 2004). Since the prevalence
estimate of desmosomal mutations is based on only five mutations, comprehensive
screening of all desmosomal genes in a population sample could reveal a substantially larger
percentage of mutation carriers. A recent study reported 69 (16%) of 427 control individuals
to carry a rare mutation in a gene associated with ARVC (Kapplinger et al. 2011). The
mutations in these control individuals were randomly distributed along the coding sequence
of the desmosomal genes, whereas mutations in ARVC patients clustered in the PKP2 gene
and the amino-terminal regions of DSG2 and DSP (Kapplinger et al. 2011). In contrast to
these desmosomal mutations in control individuals, which were considered mainly non-
pathogenic background noise (Kapplinger et al. 2011), the desmosomal mutations identified
in Studies I and II were considered probably or potentially harmful based on functional and
family data. Therefore, a large number of individuals in the Finnish population are predicted
to be at risk of developing ARVC or related myocardial abnormalities.
PKP2 Q59L was identified as a Finnish ARVC founder mutation, with a prevalence of
1:340 in the general population (Studies I, II, and VI). This founder effect appears to be
caused by the unique population history of Finland, including a small founder population,
bottleneck effects, and genetic isolation (Sajantila et al. 1996, Peltonen et al. 1999). The
prevalence of desmosomal mutations in other populations remains to be elucidated, but
since ARVC founder mutations have been reported in Dutch and South African populations
(Watkins et al. 2009, van der Zwaag et al. 2010, Kapplinger et al. 2011), prevalent
desmosomal mutations may also occur in other populations.
Approximately half of the mutation carriers in the Health 2000 study cohort presented with
arrhythmia or ECG abnormalities (Study II). This figure encompasses a wide range of
clinical manifestations, including self-reported arrhythmia, ECG alterations suggestive of
cardiac abnormalities, and ventricular tachycardia in a case of SCD. DSP T1373A could
play a role even in atrioventricular conduction since it was associated with PR interval
prolongation. Only one of those 11 mutation carriers with arrhythmia also featured ECG
alterations characteristic of ARVC, although repolarization abnormalities are usually

DISCUSSION
66considered an early marker of ARVC manifestation. This is in line with the recent finding
that T-wave inversion in right precordial leads is not associated with an increased risk of
arrhythmic mortality (Aro et al. 2012). Desmosomal mutations may thus be associated with
a wide spectrum of cardiac abnormalities and may require an additional genetic or
environmental trigger for progression to overt disease. Also protective variants, such as
PKP2 L366P, may affect disease expressivity. Other possible explanations for the low
penetrance of ARVC mutations in the Finnish population sample are late onset of disease
and lack of comprehensive cardiologic examinations in the Health 2000 study.
2. Common genetic variants modulating QT interval and LQTS phenotype
2.1. Genetic components of QT interval
Two GWA studies reported 15 independent SNPs to be associated with QT interval duration
(Newton-Cheh et al. 2009b, Pfeufer et al. 2009). Thirteen of these associations were
replicated with effect estimates to the same direction in the Health 2000 population cohort
(Study IV). The LIG3 SNP rs2074518 was excluded in the quality control of genotyping,
and the KCNJ2 SNP rs17779747 could not be replicated, probably due to limited power to
detect a small effect size. The QT interval-associated loci include cardiac ion channel genes
associated with LQTS: KCNQ1 ,KCNH2 ,SCN5A ,KCNE1 , and KCNJ2 . Not surprisingly,
QT interval-associated SNPs are also found near genes with a known function as regulators
of ion channels: NOS1AP encoding the nitric oxide synthase 1 adaptor protein, PLN
encoding phospholamban, an inhibitor of cardiac muscle sarcoplasmic reticulum calcium
channel, and ATP1B1 encoding the ȕ-subunit of the sodium-potassium transporter. CNOT1 ,
LITAF , and RNF207 represent candidate genes with unestablished roles in cardiac
repolarization.
QT score in Study IV combines the effects of the 14 QT interval-associated SNPs. It explained
8.6% of the variation in QT interval, compared with 5.4-6.5% in the QTGEN Study
(Newton-Cheh et al. 2009b). These common SNPs explain only a minority of the heritability
of QT interval, which has been estimated at 35-40% (Newton-Cheh et al. 2005, Li et al.
2009a). The highest QT score quintile had a 15.6 ms longer group mean QT interval than the
lowest quintile (Study IV), while the corresponding figure was only 10-12 ms in the
QTGEN Study (Newton-Cheh et al. 2009b). These differences may be explained by the use

DISCUSSION
67of manually checked QT interval measurements and QT interval nomogram-corrected for
heart rate in the Health 2000 study. It is also possible that the genetic components of QT
interval in the genetically homogeneous Finnish population slightly differ from those in the
QTGEN study populations. Unlike the ECG measurement of QT interval, QT score i s n o t
dependent on temporal variation in environmental factors such as hormones or medication.
It remains constant throughout a person’s lifetime. QT interval-prolonging variants may
together influence the repolarization reserve, which reflects the ability of the cell to resist
external factors affecting repolarization (Roden 2006). QT score could thus be useful in
prediction of individual susceptibility to QT-prolonging medication or other arrhythmogenic
stimuli.
2.2. Modifier genes in LQTS
The length of QT interval and the severity of symptoms vary considerably even between
carriers of the same LQTS mutation (Fodstad et al. 2004), suggesting the occurrence of
other disease-modifying factors. The minor allele of KCNE1 D85N has been found to
prolong QT interval by 10 ms in the general Finnish population (Marjamaa et al. 2009a). It
has been reported to reduce both I Ks and I Kr currents (Figure 10) and to be associated with a
more severe phenotype in LQTS mutation carriers (Westenskow et al. 2004, Nishio et al.
2009). In Study III, KCNE1 D85N was shown to prolong QT interval in KCNQ1 G589D
male carriers more than in KCNQ1 G589D female carriers or in the general population. The
data also suggested that KCNE1 D85N could be associated with a more severe clinical
phenotype in KCNQ1 G589D carriers. The interaction between KCNQ1 G589D and KCNE1
D85N may arise from the co-assembly of K v7.1 encoded by KCNQ1 and minK encoded by
KCNE1 to form the I Ks channel (Barhanin et al. 1996, Sanguinetti et al. 1996) (Figure 10).
The effect of KCNE1 D 8 5 N i n KCNH2 founder mutation carriers was non-significant in
Study III, but this could be caused by a limited statistical power.
Several potential explanations exist for the suggested sex difference in the effect of KCNE1
D85N. The baseline QT interval in KCNQ1 G589D female carriers is longer than in KCNQ1
G589D male carriers (Piippo et al. 2001, Study III). It is thus possible that KCNE1 D85N
brings out a masked I Ks defect present in males. Another possibility is that sex differences in
KCNE1 expression levels (Drici et al. 2002) may influence the susceptibility to KCNE1
D85N-mediated QT interval prolongation. KCNE1 D85N could also interfere with binding

DISCUSSION
68of hormonal or other sex-specific regulators of I Ks and I Kr channels, as sex hormones have
been reported to regulate cardiac ion channels and to alter their susceptibility to blocking
agents (Busch et al. 1997, Kurokawa et al. 2008).
LQTS modifier variants can be detected in genes encoding cardiac ion channels, such as
K897T in KCNH2 (Crotti et al. 2005) and H558R in SCN5A (Ye et al. 2003), but they may
also occur in genes with a regulatory role in cardiac function. For example, variants in
NOS1AP , which participates in the regulation of cardiac repolarization (Chang et al. 2008),
influence the phenotype of LQTS mutations (Crotti et al. 2009, Tomás et al. 2010). It is
likely that more LQTS modifier genes will be discovered in the future. Large samples of
genetically uniform LQTS patients provide an excellent opportunity to study the effects of
potential modifier variants on QT interval or arrhythmia susceptibility.
3. Genes, QT interval, and SCD
QT interval duration can be used to predict the risk of SCD (Straus et al. 2006). Study IV
showed a 19% increased risk of SCD per 10-ms prolongation of QT interval. The effect was
roughly linear, although familial short QT syndrome also predisposes to arrhythmia and
SCD (Brugada et al. 2004, Hong et al. 2005). However, a markedly shortened QT interval
occurs very rarely in the general population and may not always indicate an increased risk of
cardiac death (Anttonen et al. 2007), which could explain the linear relationship. A
dichotomous QT interval threshold (>450 ms for males and >470 ms for females) is a
widely applied measure of risk of cardiac events in the clinical setting (Straus et al. 2006).
Study IV provided further evidence for the utility of this threshold as a SCD risk predictor
and demonstrated that QT interval is a suitable intermediate phenotype for the genetic
studies of SCD.
The linear QT score was not associated with SCD in Studies IV and V. A suggestive U-shaped
association between the QT score quintiles and SCD was noted in Study IV, but this finding
was not replicated in the large meta-analysis in Study V. This lack of association could
result from limited power to detect small effect sizes (hazard ratio <1.03 per 1-ms change in
QT score in Study V). It is also possible that all QT-prolonging alleles do not contribute to
increasing the risk of SCD. Alternatively, some QT-shortening alleles may increase the risk
of SCD (Brugada et al. 2004) or the effect may depend on the interaction between different

DISCUSSION
69genetic factors (Ye et al. 2003). As depicted in Figure 1, both loss-of-function and gain-of-
function types of mutations in ion channel genes may result in severe cardiac disorders
predisposing to SCD. Therefore, a risk score based on the direct effect on SCD risk is likely
to be more accurate than one based on the QT-prolonging effect of each allele.
4. Genetic arrhythmia susceptibility variants in SCD
4.1. Common genetic variants and SCD
The minor alleles of two novel common variants, rs41312391 and rs2200733, were
significantly associated with risk of SCD, showing a 27% and 28% increased risk,
respectively (Study V). Additional covariate adjustments indicated that these SNPs may
predispose to fatal arrhythmias independently of CHD and its risk factors, QT-modulating
medication, and heart failure. In addition, the association of rs2383207 with SCD was
replicated ( p = 0.036).
The SNP rs41312391 (IVS24+116G>A) is located in an intron of SCN5A . Evidence on the
association between this SNP and cardiac repolarization is conflicting, as the minor allele
has been associated with QT interval prolongation in one study (Aydin et al. 2005) and QT
interval shortening in another study (Gouas et al. 2007). This variant is nevertheless in low
linkage disequilibrium with rs12053903 (r2 = 0.36) and rs1805126 (r2 = 0.32), whose minor
alleles are associated with QT interval shortening (Newton-Cheh et al. 2009b, Pfeufer et al.
2009). It is possible that rs41312391 is associated with increased risk of arrhythmia
independently of cardiac repolarization. The gene expression analysis in Study V suggested
that WDR48 , a regulator of chromatin structure, might be involved in this association.
However, SCN5A remains a more likely candidate gene for fatal arrhythmia due to its
reported associations with several cardiac disorders (Wang et al. 1995, Chen et al. 1998,
Schott et al. 1999, Benson et al. 2003, Bezzina et al. 2003a, Ellinor et al. 2008) and SCD
(Burke et al. 2005, Tester and Ackerman 2007, Albert et al. 2010). In fact, the common
SCN5A variant S1103Y is associated with risk of SCD (Splawski et al. 2002, Burke et al.
2005) and sudden infant death syndrome (Plant et al. 2006) in African Americans.
The variant rs2200733 was selected as a candidate SNP based on its previously reported
association with atrial fibrillation (Gudbjartsson et al. 2007), which has been shown to

DISCUSSION
70predispose to SCD after acute myocardial infarction (Pedersen et al. 2006). The results of
Study V provide the first evidence of the association of rs2200733 with SCD. This SNP is
located in 4q25 near PITX2 . The gene expression analyses suggested that the minor allele of
rs2200733 could be associated with increased expression of PITX2 , which encodes a
homeobox transcription factor involved in the generation of left-right asymmetry in cardiac
development (Franco and Campione 2003) and sinoatrial node formation (Mommersteeg et
al. 2007). PITX2 is regulated by the Wnt/ ȕ-catenin signalling pathway involved in cell
proliferation and apoptosis (Kioussi et al. 2002). Deletion of Pitx2c in mice leads to gene
expression changes in several cellular pathways, including apoptosis, cell adhesion, gap
junctions, and cardiac ion channels (Chinchilla et al. 2011, Kirchhof et al. 2011). The
disturbance of these cellular processes implicates a potential link between PITX2 expression
and life-threatening arrhythmia (Figure 10).
The SNP rs2383207 is located in 9p21 and has previously been linked to increased risk of
myocardial infarction and SCD (Helgadottir et al. 2007, Newton-Cheh et al. 2009a). The
proximal cyclin-dependent kinase inhibitor genes CDKN2A and CDKN2B are inv olv ed in
proliferation of aortic smooth muscle cells and CHD (Visel et al. 2010). Adjustment for risk
factors for CHD attenuated the association (Study V), and therefore, it seems likely that the
association between this SNP and SCD is conveyed through development of CHD.
TheNOS1AP variants rs2880058, rs12036340, and rs12143842, previously reported to be
associated with QT interval duration (Marjamaa et al. 2009a, Newton-Cheh et al. 2009b,
Pfeufer et al. 2009), were not significantly associated with SCD in Study V. In the course of
Study V, several other NOS1AP SNPs were reported to be associated with SCD (Kao et al.
2009, Westaway et al. 2011), and the effect of these variants remains to be examined in the
Finnish population. The previously reported association of ADRB2 Q27E (rs1042714) with
SCD (Sotoodehnia et al. 2006) was not replicated in Study V. This could result from
differences in the genetic structure of the study populations or in the SCD case adjudication
protocol. The power to detect a hazard ratio of more than 1.3 was over 99% in Study V, but
limited power could explain the lack of an association for SNPs with a more modest effect
size. In general, varying availability of witness reports and autopsy data has increased
heterogeneity in case definition between different SCD studies, thus complicating the
replication of genetic findings.

DISCUSSION
714.2. Rare arrhythmia-associated mutations and SCD
The ten rare arrhythmia-associated mutations located in coding regions of the KCNQ1 ,
KCNH2 ,PKP2 ,DSG2 ,DSP, and RYR2 genes had a combined carrier frequency of 79 per
10 000 individuals in Finland (Study VI). The prevalence of the four Finnish LQTS founder
mutations (36 per 10 000) corresponded to that previously reported in the general population
(Marjamaa et al. 2009b), and the prevalence of the five Finnish ARVC mutations (39 per
10 000) was in the same range as in Study II. According to these results, as many as 1 in 130
Finns may carry a mutation increasing the susceptibility to severe arrhythmias. The
geographic clustering of these mutations may be caused by founder effects during the
population history of Finland (Peltonen et al. 1999). Long-term genetic drift may also have
shaped the geographic differences in mutation prevalences (Palo et al. 2009). Only a small
proportion of the mutation carriers suffered from arrhythmia (6.5%) or heart failure (3.3%)
based on causes of death, hospitalization records, and special reimbursement eligibility for
specific medications. Thus, the mutation penetrances seem significantly reduced, although
data from extensive cardiologic examinations were not available in Study VI.
In previous studies, mutations in cardiac ion channels KCNQ1 ,KCNH2 ,SCN5A , and RYR2
(Chugh et al. 2004b, Tester et al. 2004, Tester and Ackerman 2007, Albert et al. 2008,
Adabag et al. 2010b, Marjamaa et al. 2011), and recently also in the desmosomal gene
PKP2 (Zhang et al. 2012), have been identified in victims of SCD. In Study VI, 1% of the
SCD victims carried one of the ten rare Finnish arrhythmia-associated mutations. These
mutations seem to be involved in SCD only in rare cases, and future studies may require a
longer follow-up time and a particular focus on cases with sudden arrhythmic death, which
was not feasible in the population-based approach in Study VI. Of the rare mutations, RYR2
R3570W and KCNH2 R176W have been reported in several Finnish SCD cases. RYR2
R3570W, which causes a gain-of-function defect in the cardiac ryanodine receptor, was
initially reported in two Finnish SCD victims (Marjamaa et al. 2011). The exact clinical
significance of this mutation remains uncertain, however, as clinical findings among the
surviving relatives were scarce (Marjamaa et al. 2011). KCNH2 R176W is a potentially
disease-causing LQTS variant that has been found to prolong QT interval by 22 ms in the
general Finnish population (Marjamaa et al. 2009b) and by 32 ms in LQTS families
(Fodstad et al. 2006). Altogether three SCD victims carried KCNH2 R176W in Study VI,
but further studies are needed to confirm the potential role of this mutation in SCD.

DISCUSSION
725. SCD risk prediction
Only a minority of SCDs involve patients classified into high-risk groups, including those
with previously diagnosed myocardial infarction and ventricular tachycardia (Huikuri et al.
2001). Therefore, identification of more sensitive risk markers is essential for more effective
prevention of SCDs. Traditional risk factors for CHD predispose to SCD in the general
population (Wannamethee et al. 1995, Jouven et al. 1999). This was also observed in Study
V, in which male gender, higher systolic blood pressure, prevalent diabetes, current and
former cigarette smoking, low leisure-time physical activity, prevalent CHD, Eastern
Finnish residency, and digoxin use were shown to be associated with increased risk of SCD.
These risk factors may reveal increased risk of cardiovascular disease underlying SCD, but
are not effective in evaluating individual risk of sudden death due to their low positive
predictive value (Huikuri et al. 2001). At the population level, however, treatment of
underlying cardiovascular disease reduces the occurrence of SCD.
Heart failure and left ventricular dysfunction, measured by reduced left ventricular ejection
fraction, can be used to predict risk of SCD from arrhythmia, but these risk markers are not
specific to arrhythmic causes of death (The Multicenter Postinfarction Research Group
1983). Premature ventricular depolarizations and non-sustained ventricular tachycardia may
reflect underlying heart failure, but are not specific markers for SCD (Caruso et al. 1997).
However, sustained polymorphic ventricular tachycardia and ventricular fibrillation predict
high risk of SCD (Huikuri et al. 2001). ECG is useful in diagnosing structural heart disease
and electrophysiological conditions, such as LQTS and ARVC, which predispose to SCD
(Jervell and Lange-Nielsen 1957, Thiene et al. 1988). Even without a diagnosis of a specific
cardiac disorder, several ECG markers provide additional information on arrhythmia
susceptibility. For example, QT interval prolongation, QRS complex widening, and J-point
elevation are associated with increased risk of SCD (Straus et al. 2006, Dhar et al. 2008,
Tikkanen et al. 2009, Kurl et al. 2012, Study IV). Signal-averaged ECG may also indicate
susceptibility to arrhythmia, but its positive predictive value is low (McClements and Adgey
1993). Electrophysiologic testing is a valuable tool for risk stratification in patients with
high risk of ventricular arrhythmias (Moss et al. 1996, Buxton et al. 1999).
Family history of SCD remains a risk factor for SCD even after adjustment for myocardial
infarction and CHD risk factors (Friedlander et al. 1998, Jouven et al. 1999, Friedlander et

DISCUSSION
73al. 2002). This finding indicates the involvement of independent genetic risk factors
predisposing to SCD. At the individual level, molecular screening of mutations in cardiac
ion channel genes may help in evaluating the cause of sudden unexplained death (Lunetta et
al. 2003, Tester et al. 2004, Tester and Ackerman 2007). In this case, identification of a rare
ion channel mutation may also prove effective in evaluating the SCD risk in surviving
family members. Common SCD risk variants are associated with a more modestly increased
risk (Study V), but could be used to predict risk together with clinical risk factors. QT score
did not appear to be a useful predictor of SCD risk in Studies IV and V, but development of
a risk score based on common SCD-associated variants could prove more informative along
with accumulating data on the genetic components of SCD.
6. Study limitations
In Study I, only mutations in the exons of four desmosomal genes were searched for in the
ARVC patients, and therefore, the occurrence of other types of mutations cannot be
excluded. In Study II, only five desmosomal mutations and two common polymorphisms
were assayed in the Health 2000 population sample. Thus, the total prevalence of all
desmosomal mutations in the Finnish population is expected to be higher than 1:250. The
participants of the Health 2000 and FINRISK studies did not undergo comprehensive
cardiologic examinations, and thus, the exact disease penetrance in the mutation carriers
could not be assessed. Future functional and population studies are needed to reveal the
exact clinical significance of the desmosomal mutations identified in Studies I and II.
In Study III, the power to identify an association between KCNE1 D85N and clinical
outcome in LQT2 was limited due to the small number of mutation carriers with D85N.
Despite the large sample size in Studies IV-VI, the power to identify modest effect sizes was
limited. Multiple health care registries and autopsy data were utilized to reliably identify
SCDs, but the association between genetic variants and sudden arrhythmic death could not
be specifically investigated in this population-based approach. As the population samples
consisted only of individuals aged over 25 years, the prevalence and clinical significance of
arrhythmia-associated variants in children and young adults remain to be explored.
Replication studies in independent samples are needed to confirm the novel findings of
Studies II, III, and V. In particular, confirmation of the gene expression results would
require a larger sample size and RNA quantification in cardiac tissue.

CONCLUSIONS
74CONCLUSIONS
Mutations in genes encoding desmosomal proteins account for only approximately one-fifth
of ARVC cases in Finland. Desmosomal mutations are associated with reduced disease
penetrance, and compound heterozygosity may contribute to disease progression. At sites of
cell adhesion, a mutation in a desmosomal protein may disturb the attachment of other
desmosomal proteins and lead to disorganization of the intercalated disk structure. PKP2
Q59L is a novel ARVC founder mutation showing an estimated prevalence of 1:340 in
Finland. In total, at least 1:250 Finns carry a desmosomal mutation predisposing to ARVC
or related functional abnormalities of the heart, but the reduced disease penetrance
associated with these mutations should be considered in counselling of mutation carriers.
Including both desmosomal and ion channel mutations, as many as 1:130 Finns may carry a
mutation increasing the susceptibility to severe arrhythmias.
KCNE1 D85N presents a potential sex-specific disease-modifying factor in LQTS. This
common genetic variant seems to prolong QT interval in males with LQT1, but not in
females with LQT1. It may also be associated with increased disease severity. In the general
population, KCNE1 D85N together with 13 other SNPs explain less than 10% of the
variation in QT interval. These QT interval-associated SNPs as well as still undiscovered
variants present potential LQTS modifiers, and ultimately, this information could be used in
assessment of individual susceptibility to QT-prolonging medication and LQTS.
A 10-ms prolongation of QT interval is associated with a 19% increased risk of SCD in the
Finnish population. A risk score based on QT interval-associated SNPs does not, however,
directly contribute to SCD risk prediction. In contrast, a novel variant in SCN5A and another
in 4q25 near PITX2 , as well as a previously identified variant in 9p21 near the CDKN2A and
CDKN2B genes, are associated with increased risk of SCD. Rare mutations in KCNH2 ,
RYR2 , and PKP2 are carried by individual SCD victims, but future studies are needed to
reveal their significance in sudden death. In the future, a panel of genetic and clinical risk
markers could provide useful information for SCD risk stratification and prevention.

ACKNOWLEDGEMENTS
75ACKNOWLEDGEMENTS
This study was carried out during 2005-2012 in the laboratory of Professor Kimmo Kontula
in the Research Programs Unit, Molecular Medicine, and at the Department of Medicine,
Institute of Clinical Medicine, University of Helsinki. Professors Reijo Tilvis, Olavi
Ylikorkala, and Markku Heikinheimo, the former and current heads of the Institute of
Clinical Medicine, and Professors Kimmo Kontula, Vuokko Kinnula, and Timo Strandberg,
the former and current heads of the Department of Medicine, are acknowledged for
providing excellent research facilities.
This study was financially supported by the Centre of Excellence in Complex Disease
Genetics of the Academy of Finland, the Finnish Cultural Foundation, the Special State
Share of Helsinki University Central Hospital, the Sigrid Jusélius Foundation, and the
Finnish Foundation for Cardiovascular Research.
My deepest gratitude is owed to my supervisors, Dr. Annukka Marjamaa and Professor
Kimmo Kontula, for generous guidance and support over the years. Annukka, you were the
one who first taught me how to do science, everything from lab work to writing a paper.
You were always there for me when I needed help. Kimmo, I cannot thank you enough for
providing me with the opportunity to do research on such an interesting topic and in such a
stimulating environment. I greatly admire your enthusiasm for science and your endless
ability to come up with new ideas and put them into practice to solve clinically relevant
scientific questions.
I thank Docent Juhani Junttila and Docent Samuli Ripatti for carefully reviewing this thesis
and for their invaluable comments and constructive criticism. Carol Ann Pelli is
acknowledged for editing the language of this thesis.
I am indebted to the cardiologists, Docent Heikki Swan, Dr. Maija Kaartinen, Docent Tiina
Heliö, and Docent Lauri Toivonen from the Division of Cardiology, Department of
Medicine, who examined and treated the patients and provided a clinical point of view for
the study. I am also deeply grateful to all of the collaborators in this study, Professor Veikko
Salomaa, Dr. Aki Havulinna, Professor Markus Perola, and Professor Antti Jula from the
National Institute for Health and Welfare, Professor Eero Lehtonen and Professor Veli-
Pekka Lehto from the Department of Pathology, Assistant Professor Christopher Newton-
Cheh and Dr. Peter Noseworthy from the Broad Institute and Massachusetts General
Hospital, Professor Aarno Palotie, Professor Leena Peltonen, Docent Elisabeth Widén, and
Dr. Johannes Kettunen from the Institute for Molecular Medicine Finland, Dr. Kimmo
Porthan and Docent Lasse Oikarinen from the Division of Cardiology, Department of
Medicine, and Professor Pekka Karhunen from the School of Medicine, University of
Tampere. I thank you all for your valuable contributions.
I am most grateful to Susanna Saarinen, Hanna Nieminen, Saara Nyqvist, Sini Weckström,
Hanna Ranne, and Minna Härkönen for skilful technical assistance. I also thank Raija
Selivuo, Jaana Westerback, and Minni Lajunen for help with numerous practical matters.
Kaisa Silander and Antti-Pekka Sarin from the National Institute for Health and Welfare are
acknowledged for help in the genotyping of population samples.
All of the present and former members of Kimmo Kontula’s laboratory are warmly thanked
for their friendship and guidance throughout this project: Michael Backlund, Laura

ACKNOWLEDGEMENTS
76Bouchard, Kati Donner, Heidi Fodstad, Päivi Forsblom, Tuula Hannila-Handelberg, Timo
Hiltunen, Juuso Kaiharju, Kaisa Kettunen, Maarit Lappalainen, Jukka Lehtonen, Marika
Lilja, Maiju Merisalo, Helena Miettinen, Paulina Paavola-Sakki, Kristian Paavonen, Kirsi
Paukku, Kirsi Piippo, Camilla Schalin-Jäntti, Timo Suonsyrjä, and Annaliisa Valtimo. I also
thank the present and former members of Aarno Palotie’s laboratory: Kirsi Alakurtti,
Verneri Anttila, Eija Hämäläinen, Mari Kaunisto, Päivi Tikka-Kleemola, Annika
Wennerström, and Docent Maija Wessman, as well as all other colleagues at Biomedicum,
especially Mubashir Hanif, Kaisa Hynninen, Riina Kandolin, Paula Kokko, Niina Koskipää,
Merja Lahtinen, Susanna Mehtälä, Maria Sandbacka, Tuula Soppela, and Jaana Valkeapää,
for companionship and numerous cheerful moments during lunch and coffee breaks.
My sincere gratitude is due to all patients, family members, and volunteers who participated
in this study.
I warmly thank all of my friends and family members for the leisure-time activities and
unforgettable moments that we have shared. My parents Marja and Jukka are thanked for
believing in me and supporting me in all the paths I have chosen in life. I am deeply grateful
to my grandparents Anja and Mikko for always being there when I have needed it most. My
brother Antti and my sister-in-law Alena are thanked for the many happy moments that we
have spent together. Finally, I thank my nieces Alisia and Emilia for being the most precious
things in my life. You have taught me to look at life from an entirely different perspective.
You truly mean the world to me.
Helsinki, October 2012

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