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Cardiovascular Research 2005 67(3):397-413; doi:10.1016/j.cardiores.2005.04.005
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Copyright © 2005, European Society of Cardiology

Susceptibility genes and modifiers for cardiac arrhythmias

Stefan Kääba and Eric Schulze-Bahrb,c,d,*

aDepartment of Medicine, Cardiology, Hospital of the Ludwig-Maximilians University, Munich, Germany
bDepartment of Cardiology and Angiology, Hospital of the University of Münster, Germany
cInstitute for Arteriosclerosis Research at the University of Münster, Department Molecular Cardiology, Münster, Germany
dIZKF (Interdisciplinary Center for Clinical Research) of the University of Münster, Germany

* Corresponding author. Molekular-Kardiologie, Institut für Arterioskleroseforschung an der Universität Münster, Domagkstr. 3, D-48149 Münster, Germany. Tel.: +49 251 83 52982; fax: +49 251 83 52980. Email address: heart{at}uni-muenster.de

Received 30 December 2004; revised 5 April 2005; accepted 7 April 2005


   Abstract
Top
 Abstract
1. Introduction
 2. Disease gene mutations...
 3. How to study...
 4. Polymorphic variants in...
 5. Future aspects: integration...
 References
 
The last decade has seen a dramatic increase in the understanding of the molecular basis of arrhythmias. Much of this new information has been driven by genetic studies that focused on rare, monogenic arrhythmia syndromes that were accompanied or followed by cellular electrophysiological or biochemical studies. The marked clinical heterogeneity known from these familial arrhythmia syndromes has led to the development of a multifactorial ("multi-hit") concept of arrhythmogenesis in which causal gene mutations have a major effect on disease expression that is further modified by other factors such as age, gender, sympathetic tone, and environmental triggers. Systematic genetic studies have unraveled an unexpected DNA sequence variance in these arrhythmia genes that has ethnic-specific patterns. Whether this genetic variance may contribute as a second genetic modifier for arrhythmia development is under current investigation. The aim of this article is to review common genetic variation in ion channel genes and to compare these recent findings.

KEYWORDS Genetics; Arrhythmias; Susceptibility; Ion channels; Polymorphisms


   1. Introduction
Top
 Abstract
 1. Introduction
2. Disease gene mutations...
 3. How to study...
 4. Polymorphic variants in...
 5. Future aspects: integration...
 References
 
Cardiac arrhythmias are a major cause of cardiovascular mortality and morbidity. Major improvements in the understanding of the pathophysiology of cardiac arrhythmias in humans have been obtained by the study of monogenic forms of arrhythmias, because the unequivocal identification of arrhythmia genes has led to further knowledge of physiologically relevant genes important for myocellular electric activity (Table 1). Since monogenic arrhythmia syndromes have come under increasing attention of clinicians and basic scientists, research has progressed rapidly. Genotyped families have been systematically characterized on a large-scale basis, and a variable intra- and interfamilial disease expression has been observed. Genetic and basic electrophysiological investigations have unraveled a widespread pathophysiological heterogeneity [1]. As potentially true for the majority of monogenic disorders, an extensive locus and allelic heterogeneity has been demonstrated for arrhythmia syndromes.


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  		Table 1 Human genes associated with inherited forms of cardiac arrhythmias

 
Attempts to predict the phenotypic expression of a gene mutation were not simple because–as shown for the long-QT syndromes (LQTS)–the cardiac risk of affected family members even within a particular family (in general, carrying one and the same mutation) could not be anticipated by the proband–s clinical course [2]. Thus, besides an obvious pattern of autosomal dominant inheritance, the majority of monogenic arrhythmia syndromes turned out to be associated with phenotypic variance that does not follow a Mendelian-like fashion and overtly have features of a "complex phenotype".

In contemporary human molecular medicine and genetics, understanding how DNA variation and other concomitant factors influence disease and naturally occurring phenotypic variation has been one of the major tasks. The complexity beyond this scope lies in the fact that most arrhythmia syndromes and phenotypes are influenced by many genetic, individual, and environmental factors. However, advances have been made in studies with a large number of genotyped arrhythmia patients that finally led to the identification of disease-modifying factors such as gender [3–6], extent of repolarization abnormalities [7], age [4,5], and sympathetic tone and triggers [8]. These individual or environmental factors turned out to be important disease modulators on top of an inherited single (family-specific) gene mutation [9]. The interaction for a mutant gene and individual as well as environmental conditions influencing the clinical course has been extensively shown for LQTS, but also may be true for other arrhythmia syndromes such as Brugada syndrome (BrS) or progressive cardiac conduction disease (PCCD).

To date, it is not clear whether naturally occurring DNA variation in arrhythmia genes (mostly ion channel genes) may serve as an additional co-factor that contributes to phenotypic disease expression. Currently, an increasing amount of genetic variance in these arrhythmia genes has been described and is characterized by ethnic-specific distributions. In some cases, it may still be difficult to distinguish ‘true’ gene mutations from naturally occurring variance, since, on the one hand, incomplete disease penetrance may occur [10] (i.e., a gene mutation does not express phenotypic signs) and, on the other hand, DNA polymorphisms may cause subtle to mild phenotypic effects [11–13]. These observations linking genotypes to phenotypes can only be addressed by the investigation of either large families carrying a particular gene mutation or by studying large genotyped subpopulations.

In the present review, we focused on naturally occurring genetic variation in arrhythmia genes and summarized available genetic data with emphasis on ethnic background of the identified DNA variation, degree of evolutionary conservation, and in vitro studies to assess functional relevance. In this overview, a guide to currently available information is provided that may help clinicians, geneticists, and cellular electrophysiologists to separate mutational from occasional DNA variation.


   2. Disease gene mutations and naturally occurring gene polymorphisms
Top
 Abstract
 1. Introduction
 2. Disease gene mutations...
3. How to study...
 4. Polymorphic variants in...
 5. Future aspects: integration...
 References
 
To date, the portion of identified genes that directly or indirectly contribute to arrhythmogenesis cannot be estimated, but more and more genes have been identified that are associated with monogenic forms of arrhythmias (Table 1). These genes have a frequent DNA sequence variation that is expected approximately 1 in 1000 base pairs of the human genome differing in a polymorphic manner between two chromosomal homologues. A challenge, in particular for arrhythmia genes, is to separate disease-related from disease-unrelated (naturally occurring) DNA variation.

Disease gene mutations can be associated with a recognizable, but variable, clinical presentation (so-called clinical expressivity) or they can be silent (i.e. without obvious signs of disease, so-called incomplete penetrance or non-penetrance) (Fig. 1). As a result, disease-causing mutations will not only be identified in affected family members, but also in apparently healthy mutation carriers of a family. The current estimate of mutation carriers with an incomplete disease penetrance is not exactly known for inherited arrhythmia syndromes; for long-QT syndromes (LQTS) incomplete disease penetrance can be found in 10–20% of mutation carriers. This estimate still may differ between each LQT gene and may have additional gender-specific influences (e.g., in the SCN5A (LQT-3 and BrS-1) gene [14]. In monogenic disorders that follow a typical Mendelian fashion of inheritance (mostly autosomal dominant), a gene mutation (Fig. 2) mostly is characterized by an amino acid alteration which


Figure 1
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  		Fig. 1 Genetic variants (mutations and polymorphisms) in healthy and affected individuals.

 

Figure 2
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  		Fig. 2 Definitions and characteristics of gene mutations and polymorphisms.

 
lhblk affects an evolutionary conserved site within the protein and leads to changes in polarity or hydrophobicity of the protein domain, or that prematurely truncates the amino acid chain,
lhblk typically has a strong association with the clinical phenotype (co-segregation with phenotype in a sufficient large family),
lhblk is absent in a sufficiently large number (e.g., >200) of unaffected and unrelated individuals and typically in all unaffected family members and
lhblk results in an altered in vitro functional assay that is compatible with the expected pathophysiology of the disease.

The majority of published gene mutations for arrhythmia syndromes that can be obtained from specific sites in the web (e.g., http://pc4.fsm.it:81/cardmoc/, http://archive.uwcm.ac.uk/uwcm/mg/hgmd/, or http://www.ssi.dk/en/forskning/lqtsdb/lqtsdb.htm) do not fully meet the mentioned criteria for a mutation classification, since, e.g., in vitro data or functional assays were often not reported. On the protein level, the spectrum of gene mutations is heterogeneous and contains missense mutations (48%), small deletions (<20 nucleotides; 20%), nonsense mutations (12%), splicing mutations (10%), and, less frequently, regulatory, gross rearrangements or repeat expansion mutations [15,16]. There is a highly significant excess of nucleotide transitions (62.5%) that mostly can be attributed to the known hypermutability of CG nucleotides (mutating to TG or CA). Since obvious differences in the degree of DNA methylation of germ cells exist (sperms>>oocytes), mutations of the dinucleotide CG have been reported more from male than female patients in some instances. In general, there is a widespread allelic heterogeneity in arrhythmia genes (Table 1) [1,17,18], and "hot spot" mutations are uncommon. In addition, some of these mutation types (e.g., gross deletions) are undetectable by conventional mutation detection methods (such as DNA sequencing) and, thus, may not have been systematically addressed.

In contrast, gene polymorphisms are defined as having an allele frequency of the minor allele greater than 1% (i.e., at minimum 1 out of 50 unrelated controls is a heterozygous carrier of that allele) and are generally expected to have no evident phenotypic effect. There are about 3,000,000 single-nucleotide polymorphisms (SNPs) in the human genome. Importantly, the definition of a polymorphism does not rely on the location or the type of nucleotide alteration. To be certain that in a given control population the frequency of the minor allele is ≥ 1%, a sample size with more than 230 unrelated individuals (more than 460 chromosomes; alpha error 1%) must be studied [19]. Whenever smaller numbers of control individuals are available, a classification of DNA variants into a mutation or polymorphism can be difficult or misleading. This is potentially true for the H83 allele of the ion channel subunit gene MirP2 [19] that originally has been proposed to cause periodic paralysis [20]. In contrast to disease gene mutations, polymorphisms in the genes for monogenic arrhythmia syndromes are more frequent, and, because of this, it has been questioned whether these may serve as disease modifiers for rare arrhythmia syndromes [21,22] or, more general, whether they may be used as polygenic markers for arrhythmia susceptibility [23].

The different types of polymorphisms are listed in Table 2. Gene polymorphisms may have important effects on the amino acid composition; differences according to their frequency, location within the genomic sequence, and potential impact on phenotype are shown in Table 2. The amino acid alteration of the wild-type protein through a polymorphism may be functionally neutral or (mildly to severely) impaired. Moreover, significant differences in polymorphic gene sites can be found in different ethnic backgrounds [24] and may simply represent a genetic feature of a selected population rather than a susceptibility allele [Splawski, Science 2002]. Therefore, whenever a DNA variation has been identified in a patient with an arrhythmia syndrome, it is crucial to study a control population of the same origin. Differences in the allele frequency of polymorphisms in arrhythmia genes can be taken from Table 3.


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  		Table 2 Types of gene polymorphisms [39]

 

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  		Table 3 Non-synonymous polymorphisms (SNPs) in human arrhythmia genes

 

   3. How to study arrhythmia susceptibility with polymorphic gene variants
Top
 Abstract
 1. Introduction
 2. Disease gene mutations...
 3. How to study...
4. Polymorphic variants in...
 5. Future aspects: integration...
 References
 
In arrhythmia genes, there are several types of polymorphic DNA variations in the human genome: single-nucleotide polymorphisms (SNPs), length (repeat) polymorphisms (e.g., di- or tetranucleotide microsatellite marker), and insertions or deletions ranging from 1 base pair to several thousands of base pairs in size. While intronic and intergenic SNPs account for the majority of total DNA variations [25], thus far mainly exonic SNPs have been described in association with cardiac arrhythmias. Because SNPs are randomly distributed over the whole genome and can be efficiently assessed for homo- or heterozygosity using an automated and high-throughput method, SNPs are preferentially used for genetic studies (Table 3) [26–30]. SNP information can be obtained from web-based catalogues (e.g., dbSNP, Human Gene Variation Database, HapMap, Celera, Genaissance) or for arrhythmia genes from specific sites (http://pc4.fsm.it:81/cardmoc/). Neutral SNPs of arrhythmia genes (i.e., such with no obvious effect on amino acid structure and/or protein function) will not be discussed here, since a direct biological effect for arrhythmogenesis is not present or has not been demonstrated. These polymorphisms, however, may be used as genetic markers within a haplotype block (constellation of physically linked and coinherited polymorphic markers) in which they are linked to a functionally relevant gene variant. A series of non-synonymous SNPs of arrhythmia genes is listed in Table 3 and will be discussed below. On average, 240,000–400,000 of such non-synonymous polymorphisms are expected in the human genome and about 10% will be heterozygous in an individual [31].

To assess whether a polymorphism or a haplotype constellation has a subtle or mild, but not a negligible, effect on phenotype, different types of genetic studies can be used. Population samples of a larger size (>1000 of unrelated patients and/or controls) are generally required to obtain valid genetic information. Two types of studies are commonly performed: association studies with independent (=unrelated) individuals (‘case-control studies’) using SNPs or haplotype information [32] or ‘family-based studies’ (affected sib pairs or twins; [33]). However, the majority of studies are genetic association studies with a case-control design that have produced a bulk of conflicting or discordant data, in particular in polygenic disorders such as hypertension or coronary artery disease. Often, there are obvious problems in replicating results from one to the next association studies [34,35] because initial reports of a potential association of a genetic SNP marker with a phenotypic effect lacked the statistical power necessary to declare statistical significance [36]. Au et al. demonstrated the effect of different allele frequencies in cases and controls on the minimal sample size that was needed to reach an odds ratio of 2.0 (power 0.80) for a reasonable association of a genetic marker with a disease condition [37]. They reported that a prevalence of a marker allele (e.g., minor frequent alleles of SNPs in Table 3) of 10% in controls and 18% in cases may be sufficient when investigated in a setting of 307 individuals per group at minimum. A lower (disease marker) allele frequency in each group and/or a smaller difference of allele frequencies would require significantly larger sample sizes to maintain power in an association study before a conclusive link between a particular genotype and a quantitative phenotypic trait can be drawn.

To date, there is a growing body of citable association studies (>6000 Medline entries), but only a few studies strictly meet criteria to ascertain a (‘true’) genetic association. Proposed guidelines have become available that facilitate quality control of association studies [35,38]. Many of these studies, e.g., those on the ACE I/D genotypes in conjunction with different cardiovascular disorders, are still under debate. The recommendations for case-control studies [26,35,38–40] include the following:

{dagger} Heritability and phenotyping of a trait: the condition or phenotype of interest should clearly have a heritable basis, either due to the presence of monogenic forms or by epidemiological evidence and statistical correlation, and the phenotype should be defined precisely (reliable diagnostic procedures following standardized operating procedures (SOP)),
{dagger} Population stratification: cases and controls should match ethnically (i.e., have the same origin), should have a similar age and gender distribution, and should mandatorily be composed of unrelated individuals/patients, both of large sample sizes, minimizing type I and II errors,
{dagger} Selection of physiologically and genetically meaningful markers: a rationale for selection of candidate loci should be based on previous genetic or functional data, and SNPs may be prioritized (see Table 2) for a shown or a potentially physiological relevance (e.g., in vitro data, SNP gene location, animal models, etc.), but also for genetic reasons (e.g., high frequency of the minor allele to avoid huge sample sizes),
{dagger} Probability: allele and genotype distribution in the patient and control population have to be in Hardy–Weinberg equilibrium, meaning a random mated distribution, and differences in allele distribution should lead to small P values,
{dagger} Replication: initial results of case-control studies should be replicable in another independent study, and achieved genetic/statistical power and odd ratios with confidence intervals should be indicated.

Thus far, only a fraction of genetic association studies have been replicated successfully in independent investigations [40]. This may be due to differences in population stratification and study design, inappropriate marker selection, lack of statistical power (small population sizes, together with higher statistical errors), and other reasons. Currently, there is a tendency to leave case-control studies with single or multiple SNPs in favor of haplotype-based association studies.


   4. Polymorphic variants in arrhythmia genes
Top
 Abstract
 1. Introduction
 2. Disease gene mutations...
 3. How to study...
 4. Polymorphic variants in...
5. Future aspects: integration...
 References
 
In Table 3, we have comprehensively listed non-synonymous SNPs (minor allele frequency ≥ 1%; polymorphism definition) that are currently known from arrhythmia genes; the structure of the wild-type protein is changed. Ackerman et al. reported further non-synonymous DNA variation in these arrhythmia genes with a minor allele frequency <1% that were also classified as polymorphisms [41]; these variants will not be discussed here, because epidemiological and in vitro data is needed for further classification.

Obviously, some SNPs have ethnic-specific allele frequencies (Table 3) (e.g., the SCN5A Y1103 allele, which is absent in the Asian and Caucasian population but occurs frequently in Africa-related populations [42]) whereas others have not. Other examples are the R448 allele and the S643 allele of KCNQ1 or the T897 allele of KCNH2. This observation has a clear impact for the design of case-control studies, because both study groups have to be ethnically matched to elucidate SNP effects on the phenotype (e.g., QT interval duration). We therefore have listed as many reports as are available on SNP allele frequencies with regard to the investigated populations (origin, size) (Table 3); for the majority, SNP allele frequencies did not significantly differ between unrelated populations or independent reports. Minor deviations in SNP allele frequencies (e.g., L1047 allele of KCNH2) are likely to result from different or too small sample sizes. The detection of the SCN5A polymorphism S1103Y and mild in vitro effects on the cardiac sodium channel mediated current INa ("QT interval prolonging properties" by a larger current) lead to the suggestion that this SNP, frequent in Afro-Americans (6.8%), could be an arrhythmia susceptibility for drug-induced QT prolongation [42]. SCN5A-Y1103 was first found in a large, Afro-American family (n = 23 genotyped) with a frequent occurrence of ventricular arrhythmias. Importantly, torsades de pointes or ventricular fibrillation occurred together with administration of several drugs or factors with a proarrhythmic propensity (e.g., amiodarone, erythromycin, bradycardia). Baseline ECGs in 11 of 23 (48%) members (Y1103: n = 6; S1103Y: n = 5; S1103: n = 0) showed a QTc interval ≥ 460 ms1/2, potentially having congenital LQTS; in 5 of 23 (22%) members (Y1103: n = 3; S1103Y: n = 2; S1103: n = 0) showed a QTc interval in the range 440–460 ms1/2. Because of a homozygous Y1103 carrier had a QTc of 420 ms1/2 and a homozygous S1103 carrier only had a QTc of 440 ms1/2 [42], an expected phenotypic effect of the Y1103 allele was not observed, retrospectively, and further large-scale studies in unrelated healthy individuals would be useful to assess whether in a sample of unrelated Afro-American or West African individuals a quantitative effect of the 1103Y allele on the QTc duration can be seen. These data then would encourage genetic studies towards a systematic search for effects of polymorphisms in arrhythmia genes for arrhythmia predisposition in patients with more common cardiac disorders (e.g., ischemic heart disease and LV hypertrophy).

We also investigated the degree of evolutionary conservation of the amino acid that is altered by a SNP (Table 3). Some SNPs are highly conserved between species (e.g., KCNQ1-P448R or SCN5A-S1103Y, both being homologous in Rattus norvegicus and Mus musculus), whereas others are not (e.g., KCNH2-P1047L or KCNQ1-G643S). Currently, no systematic data exists concerning whether this degree of conservation of the amino acid residue is associated with a comparable impairment of protein function, since the latter is a complex result of residue conservation, location in protein domain, and change of the amino acid composition (e.g., in polarity or hydrophobicity). In this line, Kubota et al. reported that the common S643 allele of KCNQ1, present in 11% of the Japanese population, was mostly associated with a milder phenotype, and in voltage-clamp experiments they showed that this variant was able to form functional homomultimeric channels and exhibited a weak dominant negative effect on wild-type channel subunits with at least a 30% IKs current reduction [43]. It is likely that a higher degree of evolutionary conservation of the altered amino acid residue is associated with functional impairment, but–as shown for the S643 allele–sites with a weaker degree of conservation may also exhibit functional effects in vitro.

For only a minority of known non-synonymous SNPs (Table 3) do in vitro data exist that were suggestive of a potential functional relevance (Table 4). For 12 non-synonymous SNPs of the long-QT syndrome genes, in vitro experiments are available. All 12 SNPs have differences in allele frequency depending on the ethnic origin of the study populations (Table 4). Three out of 12 (25%) have been systematically investigated by several (independent) in vitro studies; for 9 (75%) no independent in vitro data exist (one center report). Surprisingly, all 3 non-synonymous SNPs that were studied by several investigators showed no concordant result (Table 4). Under a special focus was the KCNH2-K897T SNP of KCNH2, for which a series of in vitro experiments exist. In Caucasian and US citizens, the allele frequency of the minor (T897) allele is 16–24% [44]; the amino acid residue is conserved between Homo sapiens and Mus musc., but not with Canis fam. (Table 3). The clinical data of T897 carriers are inconsistent: Bezzina et al. reported a shorter QTc interval from healthy Caucasians (females>males) [44], whereas in Fins LQT-1 patients the presence of the T897 allele was associated with a longer QT interval (females) [32]. Also, the in vitro data and IKr current recordings of T897-HERG differed: some investigators found an increase in IKr current (HEK 293; [45]), whereas others found no effect on wild-type current (Xenopus oocytes; [46]) or a current decrease through different gating mechanisms (HEK 293; [47,48]). Taken together, current data are not consistent enough to support a particular role of the KCNH2-T897 allele on repolarization, and further clinical and in vitro studies are needed.


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  		Table 4 Summary of in vitro characterized, non-synonymous polymorphisms in LQT genes (from Table 3)

 
Another potential aspect by which non-synonymous SNPs may exhibit in vitro effects and may act as a disease modifier is probably related to a physical interplay of an SNP and a closely located disease gene mutation that modifies the altered ion channel function. Thus far, a few studies have addressed this issue in the cardiac sodium channel (SCN5A) gene [21,49,50]. Baroudi et al. reasoned that the presence of an SCN5A polymorphism (R1232W) would have an impact on the functional consequences of a heterozygous SCN5A T1620M mutation, both identified in a patient with congenital Brugada syndrome. The T1620M mutation alone produced gating abnormalities in the cardiac sodium channel and thereby changes in INa (tsA201 cells), but when coexpressed with the W1232 allele, double-mutant proteins displayed a completely different cellular phenotype as characterized by a trafficking defect with protein retention within the endoplasmic reticulum. In this line, Ye et al. recently showed that the presence of the H558R polymorphisms in the SCN5A gene was able to restore normal trafficking and normalized INa currents of SCN5A-M1766L mutants that previously have been identified in a family with congenital LQTS [51,52]. Interestingly, Viswanathan et al. first reported that the presence of this variant (558R allele in cis) partially restored normal gating in voltage-dependent activation and inactivation caused by a second SCN5A gene mutation (T512I) [21]. In addition to SNP variants that may modify phenotypic effects of a disease mutation within the same gene, different protein isoforms (that can be mutant or not) may also modulate wild-type or mutant ion channel function. This has been shown for isoforms of SCN5A [53], KCNH2 [54], and KCNQ1 [55]. It is also possible that a polymorphism or variant in another gene will interplay with an identified disease gene mutation and modify its effects. Groenewegen et al. investigated a Dutch family with atrial standstill where affected family members had a novel mutation in the cardiac sodium channel gene SCN5A (D1275N) [50]. Mutant sodium channels showed a small depolarizing shift in activation compared with wild-type channels that is compatible with some discrete conduction delay in carriers of the mutant gene. In addition, in affected family members two closely linked polymorphisms were detected within regulatory regions of the gene for the atrial-specific gap junction protein connexin 40 (CX40) that were homozygous for both polymorphisms. Interestingly, CX40 assays for these rare, homozygous alleles showed a reduction in reporter gene expression compared with the more common CX40 genotype, and, subsequently, it has been proposed that a combined effect of genetic changes in both genes may have accounted for the phenotype. Paulussen et al. recently reported on a mutation analysis in 32 unrelated patients with acquired (drug-related) LQTS; a KCNE1 mutation (in two patients), one LQT-2 mutation, and the KCNE2-T8A polymorphism were found after complete sequencing [56]. The interesting aspect of their study was the investigation of cytochrome p450 genotypes: in 4 out of 32 patients, a drug was administered that was metabolized through the enzyme CYP3A, and these patients had a low activity genotype (CYP3A5). Taken together, these studies shed light on the possible interaction of drugs being slowly metabolized by cytochromes due to the presence of a polymorphic CYP gene variation and potential arrhythmogenic side effects in the setting of acquired LQTS.


   5. Future aspects: integration of individual and environmental determinants to approach arrhythmia susceptibility
Top
 Abstract
 1. Introduction
 2. Disease gene mutations...
 3. How to study...
 4. Polymorphic variants in...
 5. Future aspects: integration...
References
 
Future challenges are to identify all arrhythmia genes relevant for humans and to define their disease and natural sequence variability. In this line, rare arrhythmia syndromes, in the setting of either an electrical or structural heart disease, are powerful to define physiologically important genes for heart rhythm maintenance and propagation. Further screening of the thus identified genes will unravel naturally occurring DNA sequence variation on the genomic level. Thus far, the majority of identified non-synonymous SNPs have a low (<10%) allele frequency of the minor allele, which means that they are potentially not suitable for case-control studies because small differences in the allele frequencies between patients and controls would require samples of a large size to achieve statistically significant results [37]. In general, there is a need to identify frequently occurring, non-synonymous SNP with a more or less proven functional relevance in vitro. Given the current excitement over polymorphisms (SNPs) and cost-effective, high-throughput genotyping, association studies are currently en vogue in academia and industry. Much effort must be put toward well designed and appropriate studies, and such studies should be independently replicated. Standards will evolve as knowledge on complex traits improves; these will further influence the way of conducting association studies in arrhythmias. The definition of haplotype block in arrhythmia genes is another crucial step towards the understanding of the genomic complexity that may modulate disease expression. Recent publications indicate that gene expression itself–another potential co-factor of phenotypic variability–is also in part under genetic control [57–59]. The interrelationship between genomic variance in arrhythmia genes, promoter variability, and alterations in gene transcription is mostly unknown to date.

To assess this obvious difficulty and to improve genotype–phenotype relations, simple genetic or cellular models that focus on a single pathophysiological effect obviously will not reflect phenotypic complexity and variance that can be observed from larger families with heritable arrhythmias. From many studies involving a large sample size of genotyped patients with arrhythmia syndromes, it became clear that other than genetic factors (e.g., gender, age, and autonomous tone) are very important modulators of disease expression and arrhythmia development in these inherited syndromes. Very recently, two groups reported on quantitative effects and heritability of the QT interval in the general population [60,61]. It is unclear to date whether these modulators or genetic cofactors will play a leading role. A network approach (Fig. 3) that will integrate extended and standardized clinical information, comprehensive genetic (e.g., genetic subtype, polymorphisms, haplotype constellation), and other biological data (transciptomics, proteomics) will probably be most useful to assess individual arrhythmia risk in patients with arrhythmia syndromes. Setting up standards of how to prove these variants– functional effects on the genomic, transcriptomic, and proteomic level and to define their involvement in pathophysiological networks is one major challenge in current genomic medicine.


Figure 3
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  		Fig. 3 Individual variation of biological factors that may promote to a certain (arrhythmia) phenotype. Environmental and specific (age, gender, concomitant disease) factors are not shown, but can be assumed to influence variation in gene expression.

 


   Acknowledgements
 
We gratefully acknowledge the excellent support of Ellen Schulze-Bahr, Simone Helms, and Jessica Bertrand for completing genetic data of the manuscript. The work was partially supported by grants from the IZKF (Interdisciplinary Center for Clinical Research)(ESB) of the University of Münster, Germany, from the Deutsche Forschungsgemeinschaft (SFB556-A1, Schu1082/3-1 and 3-2, Kir 653 3-1 and 3-2)(ESB), Bonn, Germany, from the Fondation Leducq (ESB), Paris, France, and from BMBF Grant 01GS0109 (SK) as part of the German National Genome Research Network (NGFN-1).


   Notes
 
Time for primary review 17 days


   References
Top
 Abstract
 1. Introduction
 2. Disease gene mutations...
 3. How to study...
 4. Polymorphic variants in...
 5. Future aspects: integration...
 References
 

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