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Genetic Discoveries in Atrial Fibrillation and Implications for Clinical Practice

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Abstract

Atrial fibrillation (AF) is an arrhythmia with a genetic basis. Over the past decade, rapid advances in genotyping technology have revolutionised research regarding the genetic basis of AF. While AF genetics research was previously largely restricted to familial forms of AF, recent studies have begun to characterise the genetic architecture underlying the form of AF encountered in everyday clinical practice. These discoveries could have a significant impact on the management of AF. However, much work remains before genetic findings can be translated to clinical practice. This review summarises results of studies in AF genetics to date and discusses the potential implications of these findings in clinical practice.

Keywords

Atrial fibrillation, genetics, familial AF, linkage analysis, candidate gene association studies, genome-wide association studies, risk stratification,

Disclosure:The author has no conflicts of interest to declare.

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Accepted:

DOI:https://doi.org/10.15420/aer.2014.3.2.69

Correspondence Details:Saagar Mahida, Leeds General Infirmary, Great George St, Leeds LS1 3EX, UK. E: saagar7m7@yahoo.co.uk

Copyright Statement:

The copyright in this work belongs to Radcliffe Medical Media. Only articles clearly marked with the CC BY-NC logo are published with the Creative Commons by Attribution Licence. The CC BY-NC option was not available for Radcliffe journals before 1 January 2019. Articles marked ‘Open Access’ but not marked ‘CC BY-NC’ are made freely accessible at the time of publication but are subject to standard copyright law regarding reproduction and distribution. Permission is required for reuse of this content.

Atrial fibrillation (AF) is a highly prevalent arrhythmia that represents an important burden on healthcare systems. The presence of AF is associated with an increased risk of conditions such stroke, heart failure and dementia. Further, AF is associated with increased mortality. Over the past half century, significant advances have been made in understanding the pathobiology of AF. Important among these have been the demonstration that AF is a heritable disease and the identification of genetic variants underlying AF. The following review provides an overview of research in AF genetics followed by a discussion on the potential applicability of AF genetics research to clinical practice. The literature review was conducted in the PubMed database between January 1940 and January 2014.

Historical Perspective
The first reports suggesting a genetic basis of AF emerged in the 1940s when Wolff described an AF pedigree with a number of affected siblings.1 In the ensuing decades, multiple rare AF pedigrees with monogenic patterns of inheritance were described. However, it was not until 2003 that the first mutation in an AF family was reported. Using classic genetic techniques, such as linkage analysis and candidate gene screening, Chen et al. identified a potassium channel mutation in a four-generation pedigree with autosomal dominant AF. Over the next few years, much of the research in AF genetics focused on familial forms of the arrhythmia and led to the identification of additional genetic mutations (discussed in more detail in the next section).2–31

Around the same time as the first reports of causative mutations in AF pedigrees, epidemiological studies began to emerge suggesting that the form of AF encountered in everyday clinical practice also has a significant genetic component. The first large population-based study to report familial clustering of AF came from investigators at the Framingham Heart Study. They reported that more than a third of AF cases in the general population have a relative with the arrhythmia.32 Two subsequent studies from Iceland and the US also reported familial aggregation in cohorts of lone AF patients.33,34 More recently, a Danish study involving more than 9,000 lone AF patients demonstrated a strong familial component to the arrhythmia.35

While familial AF is caused by single gene mutations, the form of AF encountered in everyday clinical practice is likely to be a more complex trait, which is caused by multiple genetic variants interacting with environmental factors. The identification of the genetic architecture underlying the common form of AF has represented a challenging task. Candidate gene association studies have attempted to identify common variants underlying AF with limited success.

The recent emergence of next-generation sequencing (NGS) technology has enhanced the ability of researchers to identify genetic variants underlying complex traits. Since 2007, genome-wide association studies (GWAS) have used NGS technology to identify multiple variants underlying AF. NGS technology has also led to the development of exome sequencing and whole genome sequencing, which allow simultaneous sequencing of the entire protein coding region or the whole genome, respectively. These are promising techniques for the identification of causative variants in AF pedigrees as well as AF populations. However, as yet, they have not been widely applied to AF genetics research.

Rare Mutations in Familial Atrial Fibrillation
Linkage analysis and candidate-gene sequencing have identified multiple mutations in monogenic AF families and isolated AF cases.2–31 Linkage analysis involves performing genotyping of markers distributed throughout the genome and investigating the transmission of these markers with disease within a pedigree.36 Markers that transmit closely with disease lie in proximity to the disease-causing mutation. Therefore, identifying a series of markers that transmit closely with disease narrows the search space for the disease-causing variant to a defined subsegment of the genome. The genes within the sub-segment can then be screened to identify a causative mutation. However, conventional genotyping techniques are often time consuming and demanding.
Summary of Monogenic Mutations Associated with Atrial Fibrillation
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The majority of reported mutations for familial forms of AF are located in genes that encode ion channel subunits (see Figure 1 and Table 1). The identification of these mutations led researchers to question whether single-gene mutations in ion channel genes also contribute to the heritability of the common form of AF in the general population. Early reports from candidate-gene screening studies suggest that these mutations are not prevalent in the general population.11,13,30,37–41 Of note, however, the genes identified in AF pedigrees are significantly more likely to harbour rare variants in cohorts of lone AF patients compared with control populations.42
Monogenic Mutations Implicated in Atrial Fibrillation
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While the mutations identified in monogenic AF pedigrees are rare, their identification has provided interesting insights into the pathogenic basis of AF. Both gain-of-function and loss-of-function mutations in genes encoding potassium and sodium channel subunits have been reported to underlie familial AF.2–9,11–13,25,26,29–31,43 Gain-offunction potassium channel mutations are predicted to influence AF by shortening the atrial effective refractory period, an effect that would be predicted to promote atrial re-entry.44 Loss-of-function potassium channel mutations are predicted to promote triggered activity in the atrium, which is also an important contributor to the genesis of AF.8 Gain-of-function sodium channel mutations are predicted to promote triggered activity.25 Loss-of-function sodium channel mutations are predicted to shorten the wavelength of an impulse circulating around a re-entry circuit.45 The potential mechanistic links between non-ion channel mutations and AF pathogenesis are less clearly understood.

Common Genetic Variants and Atrial Fibrillation in the General Population
Identifying the genetic basis of the common form of AF in the general population is a more challenging task. Genetic association studies are valuable tools in this context. In contrast to family-based studies, which investigate co-segregation of genetic variants, population-based association studies investigate the co-occurrence of genetic variants in affected individuals. It is important to emphasise that in contrast to family-based studies, in which the reported mutations have large effect sizes and are directly responsible for causing the trait, association studies identify variants with small effect sizes that confer an increased risk of disease. Functional validation of the role of these variants in disease pathogenesis is typically more difficult.
Summary of Results from Candidate-gene Association Studies
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The majority of early association studies in AF were candidategene association studies.40,46–58 Candidate gene studies focus on specific genes that are selected based on a priori knowledge of their function, and compare the frequency of the variants between cohorts of individuals with and without disease.59 To date, a number of candidate-gene association studies have identified common variants that are more prevalent in AF populations compared with control populations.40,46–58 Often, the candidate genes have been selected based on results of studies in familial AF. As summarised in Table 2, a range of common variants in ion channel and non-ion channel genes have been associated with AF. Overall, however, candidate-gene association studies have been associated with limited success due to a low pre-test probability of the selected variants being involved in disease pathogenesis and poor reproducibility.59

Genetic Variants Identified by Genome-wide Association Studies
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The recent advent of GWAS has led to significant progress in our understanding of the genetic basis of AF. GWAS involve genotyping up to a million common variants, or single nucleotide polymorphisms (SNPs), distributed throughout the genome and comparing their frequency between AF and control cohorts.60 As opposed to candidate gene studies, GWAS are unbiased and therefore may identify previously unsuspected genes that play an important role in disease pathogenesis.
Summary of Results from Genome-wide Association Studies
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Four large GWAS have been performed in AF cohorts to date.61–64 The results of these studies are summarised in Figure 2 and Table 3. The common variants identified by GWAS are located either within or in proximity to compelling candidate genes for AF. The ion channel genes, KCNN3 and HCN4, have been identified at two of the GWAS loci for AF.63,64KCNN3 encodes a calcium-activated potassium channel (SK3 channel), which is abundantly expressed in the atrium.65HCN4 encodes the hyperpolarisation-activated, cyclic nucleotide-gated cation channel 4, which underlies the pacemaker potential.66 Two of the GWAS loci for AF harbour the cardiac transcription factor genes PITX2 and PRRX1. These homeobox transcription factors are critical mediators of cardiac development. PITX2 mediates asymetrical development of the heart and inhibits left-sided pacemaker specification.67–69PRRX1 has been implicated as a mediator of development of the pulmonary veins.70SYNPO2L, MYOZ1 and CAV1 also represent potentially interesting genes at GWAS loci. SYNPO2L and MYOZ1 encode signalling proteins that localise to the Z-disc and modulate cardiac sarcomeric function.71,72CAV1 is an important membrane protein, which plays a role in cellular signalling, and has been demonstrated to interact with ion channels, including HCN4 and KCNN3.73–76

In addition to the variants identified in the aforementioned GWAS for AF, common variants that have previously been implicated in GWAS for Brugada syndrome have also been demonstrated to influence risk in AF. AF is commonly observed as a co-existing condition in pedigrees with Brugada syndrome.77,78 Paradoxically, variants that have been demonstrated to confer increased risk of Brugada syndrome have a protective effect in AF patients.79 Further functional studies are necessary to determine the mechanisms underlying these observations.

While compelling candidate genes have been identified at GWAS loci, it is important to note that the mechanistic link between the GWAS variants and the function of these genes represents a challenge. This point is underscored by the fact that, to date, more than 1,000 GWAS risk variants have been identified for a range of diseases, while only a handful have been comprehensively functionally validated.80 GWAS rarely identify causative genetic variants directly. Rather, the variants identified by GWAS typically act as markers that point to a disease causing variant.60 The prevailing theory regarding the mechanistic link between the causative variants at GWAS loci and disease pathogenesis is that these variants alter the quantity of target gene expression, possibly through altered function of transcription regulatory elements.81

Interestingly, researchers have recently demonstrated that the rare variants identified in population-based genetic studies may contribute to variable penetrance of causal mutations in familial forms of AF. In a study involving 11 AF pedigrees, in whom the causative mutation was known, Ritchie et al. demonstrated that the presence of the risk variants at the 4q25 locus predicted whether mutation carriers developed AF.82 As discussed above, variants at the 4q25/ PITX2 locus have consistently been demonstrated to influence AF in multiple population-based studies. These findings suggest that the heritability of AF is influenced by complex interactions between common and rare variants.

Clinical Relevance
The identification of genetic variants that contribute to AF susceptibility has potentially important implications for management of the arrhythmia. On the one hand, these variants could be of value for determining risk of future AF in asymptomatic individuals. On the other, they could uncover novel molecular targets for pharmacotherapy and potentially be of use in predicting response to therapy in AF patients. The following section discusses the potential utility of genetic information for management of AF patients.

Risk Stratification for Atrial Fibrillation The identification of asymptomatic individuals who are at high risk of developing AF is an important public health concern. Current risk-stratification strategies, which are based mainly on conventional risk factors, are associated with significant limitations.83–86 Following the success of GWAS, the potential use of genotype-based risk stratification for AF has received significant interest. Initial attempts at using GWAS risk variants to predict risk have been associated with limited success. For instance, Smith et al. demonstrated that when considered in combination with conventional risk factors, genotype information from two AF GWAS loci (chromosome 4 and 16) did not have an impact on risk stratification.87 Similarly, Everett et al. reported that the inclusion of 12 risk variants at nine GWAS loci did not significantly enhance risk stratification.88 However, more recently, encouraging results have emerged from a study by Lubitz et al. who demonstrated that when considered in combination, four risk variants at the 4q25/ PITX2 locus and eight variants at other loci resulted in a fivefold gradient in AF risk.89 The results were consistent in cohorts of European and Japanese descent.

It is important to note that the variants identified to date are associated with modest effect sizes and collectively account for only a proportion of the heritability estimated for AF. Before clinically applicable risk stratification algorithms can be developed for AF, a significant proportion of the ‘missing heritability‘ of AF needs to be uncovered. The identification of the missing heritability of complex phenotypes like AF represents a major hurdle. Some of the missing heritability may be accounted for by additional common variants. GWAS are designed to identify common variants; however, some common variants may have been overlooked as current thresholds for statistical significance are high. Potential strategies for the identification of additional common variants include performing GWAS in larger cohorts and cohorts from different ethnic backgrounds.90

A significant proportion of the missing heritability of AF is likely to be accounted for by rare variants and structural variants in the genome.90 Examples of structural variation in the genome include tandem repeat sequences, insertions and deletions, copy number variants, translocations and inversions. GWAS are not designed to identify rare variants or structural variants. As discussed previously, a number of novel genotyping technologies have emerged since GWAS, including exome sequencing and whole genome sequencing. These represent promising techniques for the identification of rare variants underlying complex traits at a population level. While genotyping techniques for the identification of structural variants are currently less effective, they are constantly evolving.

Identification of Novel Therapeutic Targets for Atrial Fibrillation
The identification of the genetic architecture underlying AF has the potential to uncover novel therapeutic targets for the arrhythmia. GWAS are of particular interest in this context as they are agnostic and therefore commonly identify previously unsuspected genes underlying complex traits. As discussed previously, GWAS have identified a number of compelling candidate genes at the AF risk loci. These findings have spawned additional functional studies that have focused on candidate genes and have demonstrated that genes such as KCNN3 and PITX2 influence AF susceptibility.91–96

It is important to note that while the aforementioned studies suggest that candidate genes at GWAS loci potentially influence AF susceptibility, focusing drug development efforts on these candidate genes would be premature. Before GWAS findings can be translated to drug development, more comprehensive functional validation is necessary. GWAS typically characterise the association between marker variants and AF. The aims of post-GWAS analysis include identification of the causative variants at the GWAS loci and characterisation of the mechanistic link between these variants and target genes. A detailed discussion of post-GWAS functional analysis is beyond the scope of this review and has previously been reviewed extensively.81

In addition to GWAS, exome sequencing and whole genome sequencing could potentially identify important therapeutic targets for AF. Population-based exome sequencing projects are already currently under way in AF cohorts and promise to identify multiple additional candidate genes. As is the case with GWAS however, these genes will have to be comprehensively functionally validated. Overall, while findings from population-based genetic studies are promising, it may take more than a decade of research from the discovery of a novel gene to the development of a drug that can be used in clinical practice.97

Genotype-based Prediction of Response to Atrial Fibrillation Therapies
In addition to uncovering novel therapeutic targets for AF, information from genetic studies could potentially be of value in predicting responses to established therapies. The influence of genotype on response to antiarrhythmic drugs has been investigated in two recent studies. In a relatively small cohort of AF patients, Parvez et al. demonstrated that the GWAS risk variants at the 4q25 locus independently predict successful rhythm control with antiarrhythmic therapy.98 The same group also demonstrated that variants in the gene encoding the β1-adrenergic receptor (β1-AR) predict response to rate control therapy with beta blockers.99

Multiple studies have reported that genotype also influences response to anticoagulant therapy. Variants in genes such as VKORC1, CYP2C9 and CYP4F2 have been identified as potential mediators of response to warfarin therapy.100,101 These genes encode proteins that are either involved in the vitamin K pathway or in warfarin metabolism.102 Attempts have been made to incorporate these genes into algorithms designed to predict warfarin response. While some studies have suggested potential clinical utility of these algorithms, the results have not been consistent.102 A more detailed discussion on pharmacogenomics of warfarin therapy is beyond the scope of this review.

The role of genotype-based prediction of therapeutic response has also been investigated for non-pharmacological interventions. Husser et al. demonstrated that the variants at the 4q25 locus predict response to catheter ablation for AF. Specifically the presence of the risk variants predicted both early and late recurrences of AF following pulmonary vein isolation.103 Further evidence linking SNPs at the 4q25 locus and response to catheter ablation came from a more recent study from Shoemaker et al.104 Finally, Parvez et al. reported that SNPs at 4q25 predict recurrence of AF following successful direct current cardioversion.105

Overall, the above studies have demonstrated promising results suggesting that genotypic data can be of value in to predicting response to both pharmacological and non-pharmacological therapies. However, most of these studies have been limited to small numbers of patients and have focused on small numbers of variants. Further research with large, prospective randomised studies that include multiple genetic variants is currently needed to more clearly define the role of genetic data in predicting response.

Conclusions
In recent years, research into the genetic basis of AF has undergone a revolution. Significant progress has been made in identifying the genetic substrate underlying the common form of AF encountered in everyday clinical practice. Genotyping technologies are constantly evolving and promise to uncover more genes and molecular pathways underlying AF. However, while these discoveries are promising, much work remains before they can be translated to the clinic.

References

  1. Wolff L. Familial auricular fibrillation. N Engl J Med 1943;229:396–8.
    Crossref
  2. Chen YH, Xu SJ, Bendahhou S, et al. KCNQ1 gain-of-function mutation in familial atrial fibrillation. Science 2003;299:251–4.
    Crossref | PubMed
  3. Hong K, Piper DR, Diaz-Valdecantos A, et al. De novo KCNQ1 mutation responsible for atrial fibrillation and short QT syndrome in utero. Cardiovasc Res 2005;68:433–40.
    Crossref | PubMed
  4. Das S, Makino S, Melman YF, et al. Mutation in the S3 segment of KCNQ1 results in familial lone atrial fibrillation. Heart Rhythm 2009;6:1146–53.
    Crossref | PubMed
  5. Yang Y, Xia M, Jin Q, et al. Identification of a KCNE2 gain-offunction mutation in patients with familial atrial fibrillation. Am J Hum Genet 2004;75:899–905.
    Crossref | PubMed
  6. Ravn LS, Aizawa Y, Pollevick GD, et al. Gain of function in IKs secondary to a mutation in KCNE5 associated with atrial fibrillation. Heart Rhythm. 2008;5:427–35.
    Crossref | PubMed
  7. Xia M, Jin Q, Bendahhou S, et al. A Kir2.1 gain-of-function mutation underlies familial atrial fibrillation. Biochem Biophys Res Commun 2005;332:1012–9.
    Crossref | PubMed
  8. Olson TM, Alekseev AE, Liu XK, et al. Kv1.5 channelopathy due to KCNA5 loss-of-function mutation causes human atrial fibrillation. Hum Mol Genet 2006;15:2185–91.
    Crossref | PubMed
  9. Yang Y, Li J, Lin X, et al. Novel KCNA5 loss-of-function mutations responsible for atrial fibrillation J Hum Genet 2009;54:277–83.
    Crossref | PubMed
  10. Olson TM, Michels VV, Ballew JD, et al. Sodium channel mutations and susceptibility to heart failure and atrial fibrillation, JAMA 2005;293:447–54.
    Crossref | PubMed
  11. Ellinor PT, Nam EG, Shea MA, et al. Cardiac sodium channel mutation in atrial fibrillation. Heart Rhythm 2008;5:99–105.
    Crossref | PubMed
  12. Makiyama T, Akao M, Shizuta S, et al. A novel SCN5A gainof- function mutation M1875T associated with familial atrial fibrillation, J Am Coll Cardiol, 2008;52:1326–34.
    Crossref | PubMed
  13. Watanabe H, Darbar D, Kaiser DW, et al. Mutations in sodium channel beta1- and beta2-subunits associated with atrial fibrillation, Circ Arrhythm Electrophysiol, 2009;2:268–75.
    Crossref | PubMed
  14. Gollob MH, Jones DL, Krahn AD, et al. Somatic mutations in the connexin 40 gene (GJA5) in atrial fibrillation. N Engl J Med 2006;354:2677–88.
    Crossref | PubMed
  15. Hodgson-Zingman DM, Karst ML, Zingman LV, et al. Atrial natriuretic peptide frameshift mutation in familial atrial fibrillation. N Engl J Med 2008;359:158–65.
    Crossref | PubMed
  16. Postma AV, van de Meerakker JB, Mathijssen IB, et al. A gain-of-function TBX5 mutation is associated with atypical Holt-Oram syndrome and paroxysmal atrial fibrillation. Circ Res, 2008;102:1433–42.
    Crossref | PubMed
  17. Jiang JQ, Shen FF, Fang WY, et al. Novel GATA4 mutations in lone atrial fibrillation. Int J Mol Med 2011;28:1025–32.
    Crossref | PubMed
  18. Wang J, Sun YM, Yang YQ. Mutation spectrum of the GATA4 gene in patients with idiopathic atrial fibrillation. Mol Biol Rep 2012;39:8127–35.
    Crossref | PubMed
  19. Yang YQ, Wang MY, Zhang XL, et al. GATA4 loss-offunction mutations in familial atrial fibrillation. Clin Chim Acta 2011;412:1825–30.
    Crossref | PubMed
  20. Wang XH, Huang CX, Wang Q, et al. A novel GATA5 loss-offunction mutation underlies lone atrial fibrillation. Int J Mol Med 2013;31:43–50.
    Crossref | PubMed
  21. Gu JY, Xu JH, Yu H, Yang YQ. Novel GATA5 loss-of-function mutations underlie familial atrial fibrillation, Clinics (Sao Paulo) 2012;67:1393–9.
    Crossref | PubMed
  22. Li J, Liu WD, Yang ZL, Yang YQ. Novel GATA6 loss-of-function mutation responsible for familial atrial fibrillation. Int J Mol Med 2012;30:783–90.
    Crossref | PubMed
  23. Yang YQ, Li L, Wang J, et al. GATA6 loss-of-function mutation in atrial fibrillation. Eur J Med Genet 2012;55:520–6.
    Crossref | PubMed
  24. Posch MG, Boldt LH, Polotzki M, et al. Mutations in the cardiac transcription factor GATA4 in patients with lone atrial fibrillation. Eur J Med Genet 2010;53:201–3.
    Crossref | PubMed
  25. Li Q, Huang H, Liu G, et al. Gain-of-function mutation of Nav1.5 in atrial fibrillation enhances cellular excitability and lowers the threshold for action potential firing, Biochem Biophys Res Commun 2009;380:132–7.
    Crossref | PubMed
  26. Olson TM, Alekseev AE, Moreau C, et al. KATP channel mutation confers risk for vein of Marshall adrenergic atrial fibrillation. Nat Clin Pract Cardiovasc Med 2007;4:110–6.
    Crossref | PubMed
  27. Beavers DL, Wang W, Ather S, et al. Mutation E169K in junctophilin-2 causes atrial fibrillation due to impaired RyR2 stabilization. J Am Coll Cardiol 2013;62:2010–9.
    Crossref | PubMed
  28. Zhabyeyev P, Hiess F, Wang R, et al. S4153R is a gain-offunction mutation in the cardiac Ca(2+) release channel ryanodine receptor associated with catecholaminergic polymorphic ventricular tachycardia and paroxysmal atrial fibrillation. Can J Cardiol 2013;29:993–6.
    Crossref
  29. Olesen MS, Refsgaard L, Holst AG, et al. A novel KCND3 gainof- function mutation associated with early-onset of persistent lone atrial fibrillation. Cardiovasc Res 2013;98:488– 95.
    Crossref | PubMed
  30. Olesen MS, Bentzen BH, Nielsen JB, et al. Mutations in the potassium channel subunit KCNE1 are associated with earlyonset familial atrial fibrillation. BMC Med Genet 2012;13:24.
    Crossref | PubMed
  31. Lundby A, Ravn LS, Svendsen JH, et al. KCNE3 mutation V17M identified in a patient with lone atrial fibrillation. Cell Physiol Biochem 2008;21:47–54.
    Crossref | PubMed
  32. Fox CS, Parise H, D’Agostino RB, Sr., et al. Parental atrial fibrillation as a risk factor for atrial fibrillation in offspring. JAMA 2004;291:2851–5.
    Crossref | PubMed
  33. Ellinor PT, Yoerger DM, Ruskin JN, MacRae CA. Familial aggregation in lone atrial fibrillation. Hum Genet 2005;118:179–84.
    Crossref | PubMed
  34. Arnar DO, Thorvaldsson S, Manolio TA, et al. Familial aggregation of atrial fibrillation in Iceland. Eur Heart J 2006;27:708–12.
    Crossref | PubMed
  35. Oyen N, Ranthe MF, Carstensen L, et al. Familial aggregation of lone atrial fibrillation in young persons. J Am Coll Cardiol 2012;60:917–21.
    Crossref | PubMed
  36. Ott J. Chapter 5. In: Ott J, ed. Analysis of Human Genetic Linkage. Baltimore: JHU Press, 1999, 53–82.
    Crossref
  37. Ellinor PT, Moore RK, Patton KK, et al. Mutations in the long QT gene, KCNQ1, are an uncommon cause of atrial fibrillation. Heart 2004;90:1487–8.
    Crossref | PubMed
  38. Ellinor PT, Petrov-Kondratov VI, Zakharova E, et al. Potassium channel gene mutations rarely cause atrial fibrillation. BMC Med Genet 2006;7:70.
    PubMed
  39. Otway R, Vandenberg JI, Guo G, et al. Stretch-sensitive KCNQ1 mutation A link between genetic and environmental factors in the pathogenesis of atrial fibrillation? J Am Coll Cardiol 2007;49:578–86.
    Crossref | PubMed
  40. Chen LY, Ballew JD, Herron KJ, et al. A common polymorphism in SCN5A is associated with lone atrial fibrillation. Clin Pharmacol Ther 2007;81:35–41.
    Crossref | PubMed
  41. Darbar D, Kannankeril PJ, Donahue BS, et al. Cardiac sodium channel (SCN5A) variants associated with atrial fibrillation. Circulation 2008;117:1927–35.
    Crossref | PubMed
  42. Olesen MS, Andreasen L, Jabbari J, et al. Very early-onset lone atrial fibrillation patients have a high prevalence of rare variants in genes previously associated with atrial fibrillation. Heart Rhythm 2014;11:246–51.
    Crossref | PubMed
  43. Olson EN. A genetic blueprint for growth and development of the heart. Harvey Lect 2002;98:41–64.
    PubMed
  44. Nattel S. New ideas about atrial fibrillation 50 years on. Nature 2002;415:219–26.
    Crossref | PubMed
  45. Kneller J, Kalifa J, Zou R, et al. Mechanisms of atrial fibrillation termination by pure sodium channel blockade in an ionicallyrealistic mathematical model. Circ Res 2005;96:e35–47.
    Crossref | PubMed
  46. Fatini C, Sticchi E, Genuardi M, et al. Analysis of minK and eNOS genes as candidate loci for predisposition to nonvalvular atrial fibrillation. Eur Heart J 2006;27:1712–8.
    Crossref | PubMed
  47. Sinner MF, Pfeufer A, Akyol M, et al. The non-synonymous coding IKr-channel variant KCNH2-K897T is associated with atrial fibrillation: results from a systematic candidate gene-based analysis of KCNH2 (HERG). Eur Heart J 2008;29:907–14.
    Crossref | PubMed
  48. Lai LP, Su MJ, Yeh HM, et al. Association of the human minK gene 38G allele with atrial fibrillation: evidence of possible genetic control on the pathogenesis of atrial fibrillation. Am Heart J 2002;144:485–90.
    Crossref | PubMed
  49. Ravn LS, Hofman-Bang J, Dixen U, et al. Relation of 97T polymorphism in KCNE5 to risk of atrial fibrillation. Am J Cardiol 2005;96:405–7.
    Crossref | PubMed
  50. Schreieck J, Dostal S, von Beckerath N, et al. C825T polymorphism of the G-protein beta3 subunit gene and atrial fibrillation: association of the TT genotype with a reduced risk for atrial fibrillation. Am Heart J 2004;148:545–50.
    Crossref | PubMed
  51. Nyberg MT, Stoevring B, Behr ER, et al. The variation of the sarcolipin gene (SLN) in atrial fibrillation, long QT syndrome and sudden arrhythmic death syndrome. Clin Chim Acta 2007;375:87–91.
    Crossref | PubMed
  52. Juang JM, Chern YR, Tsai CT, et al. The association of human connexin 40 genetic polymorphisms with atrial fibrillation. Int J Cardiol 2007;116:107–12.
    Crossref | PubMed
  53. Tsai CT, Hwang JJ, Chiang FT, et al. Renin-angiotensin system gene polymorphisms and atrial fibrillation: a regression approach for the detection of gene-gene interactions in a large hospitalized population. Cardiology 2008;111:1–7.
    Crossref | PubMed
  54. Ravn LS, Benn M, Nordestgaard BG, et al. Angiotensinogen and ACE gene polymorphisms and risk of atrial fibrillation in the general population. Pharmacogenet Genomics 2008;18:525–33.
    Crossref | PubMed
  55. Bedi M, McNamara D, London B, Schwartzman D. Genetic susceptibility to atrial fibrillation in patients with congestive heart failure. Heart Rhythm 2006;3:808–12.
    Crossref | PubMed
  56. Fatini C, Sticchi E, Gensini F, et al. Lone and secondary nonvalvular atrial fibrillation: role of a genetic susceptibility. Int J Cardiol 2007;120:59–65.
    Crossref | PubMed
  57. Kato K, Oguri M, Hibino T, et al. Genetic factors for lone atrial fibrillation. Int J Mol Med 2007;19:933–9.
    Crossref | PubMed
  58. Gaudino M, Andreotti F, Zamparelli R, et al. The -174G/C interleukin-6 polymorphism influences postoperative interleukin-6 levels and postoperative atrial fibrillation. Is atrial fibrillation an inflammatory complication? Circulation 2003;108(Suppl. 1):II195–9.
    Crossref | PubMed
  59. Tabor HK, Risch NJ, Myers RM. Candidate-gene approaches for studying complex genetic traits: practical considerations. Nat Rev Genet 2002;3:391–7.
    PubMed
  60. Manolio TA. Genomewide association studies and assessment of the risk of disease. N Engl J Med 2010;363:166–76.
    Crossref | PubMed
  61. Gudbjartsson DF, Arnar DO, Helgadottir A, et al. Variants conferring risk of atrial fibrillation on chromosome 4q25. Nature 2007;448:353–7.
    Crossref | PubMed
  62. Benjamin EJ, Rice KM, Arking DE, et al. Variants in ZFHX3 are associated with atrial fibrillation in individuals of European ancestry. Nat Genet, 2009;41:879–81.
    Crossref | PubMed
  63. Ellinor PT, Lunetta KL, Glazer NL, et al. Common variants in KCNN3 are associated with lone atrial fibrillation. Nat Genet 2010;42:240–4.
    Crossref | PubMed
  64. Ellinor PT, Lunetta KL, Albert CM, et al. Meta-analysis identifies six new susceptibility loci for atrial fibrillation. Nat Genet 2012;44:670–5.
    Crossref | PubMed
  65. Tuteja D, Xu D, Timofeyev V, et al. Differential expression of small-conductance Ca2+-activated K+ channels SK1, SK2, and SK3 in mouse atrial and ventricular myocytes. Am J Physiol Heart Circ Physiol 2005;289:H2714–23.
    Crossref | PubMed
  66. Milanesi R, Baruscotti M, Gnecchi-Ruscone T, DiFrancesco D, Familial sinus bradycardia associated with a mutation in the cardiac pacemaker channel. N Engl J Med 2006;354:151–7.
    Crossref | PubMed
  67. Logan M, Pagan-Westphal SM, Smith DM, et al. The transcription factor Pitx2 mediates situs-specific morphogenesis in response to left-right asymmetric signals, Cell 1998;94:307–17.
    Crossref | PubMed
  68. Mommersteeg MT, Hoogaars WM, Prall OW, et al. Molecular pathway for the localized formation of the sinoatrial node, Circ Res 2007;100:354–62.
    Crossref | PubMed
  69. Wang J, Klysik E, Sood S, et al. Pitx2 prevents susceptibility to atrial arrhythmias by inhibiting left-sided pacemaker specification. Proc Natl Acad Sci U S A 2010;107:9753–8.
    Crossref | PubMed
  70. Ihida-Stansbury K, McKean DM, Gebb SA, et al. Paired-related homeobox gene Prx1 is required for pulmonary vascular development. Circ Res 2004;94:1507–14.
    Crossref | PubMed
  71. Frey N, Olson EN. Calsarcin-3, a novel skeletal musclespecific member of the calsarcin family, interacts with multiple Z-disc proteins. J Biol Chem 2002;277:13998–4.
    Crossref | PubMed
  72. Beqqali A, Monshouwer-Kloots J, Monteiro R, et al. CHAP is a newly identified Z-disc protein essential for heart and skeletal muscle function. J Cell Sci 2010;123:1141–50.
    Crossref | PubMed
  73. Sowa G. Caveolae, caveolins, cavins, and endothelial cell function: new insights. Front Physiol 2012;2:120.
    Crossref | PubMed
  74. Vaidyanathan R, Vega AL, Song C, et al. The interaction of caveolin 3 protein with the potassium inward rectifier channel Kir2.1: physiology and pathology related to long qt syndrome 9 (LQT9). J Biol Chem 2013;288:17472–80.
    Crossref | PubMed
  75. Barbuti A, Scavone A, Mazzocchi N, et al. A caveolin-binding domain in the HCN4 channels mediates functional interaction with caveolin proteins. J Mol Cell Cardiol 2012;53:187–95.
    Crossref | PubMed
  76. Lin MT, Adelman JP, Maylie J. Modulation of endothelial SK3 channel activity by Ca2+-dependent caveolar trafficking. Am J Physiol Cell Physiol 2012;303:C318–27.
    Crossref | PubMed
  77. Makiyama T, Akao M, Tsuji K, et al. High risk for bradyarrhythmic complications in patients with Brugada syndrome caused by SCN5A gene mutations. J Am Coll Cardiol 2005;46:2100–2106.
    Crossref | PubMed
  78. Bordachar P, Reuter S, Garrigue S, et al. Incidence, clinical implications and prognosis of atrial arrhythmias in Brugada syndrome. Eur Heart J 2004;25:879–84.
    Crossref | PubMed
  79. Andreasen L, Nielsen JB, Darkner S, et al. Brugada syndrome risk loci seem protective against atrial fibrillation. Eur J Hum Genet, 2014 [Epub ahead of print] doi:10.1038/ejhg.2014.46.
    Crossref | PubMed
  80. Hindorff LA, MacArthur J, Morales J, et al. A catalog of published genome-wide association studies. National Human Genome Institute, 2014. Available at: www.genome.gov/gwastudies (accessed 21 July 2014).
     
  81. Freedman ML, Monteiro AN, Gayther SA, et al. Principles for the post-GWAS functional characterization of cancer risk loci. Nat Genet 2011;43:513–8.
    Crossref | PubMed
  82. Ritchie MD, Rowan S, Kucera G, et al. Chromosome 4q25 variants are genetic modifiers of rare ion channel mutations associated with familial atrial fibrillation. J Am Coll Cardiol 2012;60:1173–81.
    Crossref | PubMed
  83. Schnabel RB, Sullivan LM, Levy D, et al. Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study. Lancet 2009;373:739–45.
    Crossref | PubMed
  84. Schnabel RB, Aspelund T, Li G, et al. Validation of an atrial fibrillation risk algorithm in whites and African Americans. Arch Intern Med 2010;170:1909–17.
    Crossref | PubMed
  85. Chamberlain AM, Agarwal SK, Folsom AR, et al. A clinical risk score for atrial fibrillation in a biracial prospective cohort (from the Atherosclerosis Risk in Communities [ARIC] study), Am J Cardiol, 2011;107:85–91.
    Crossref | PubMed
  86. Lubitz SA, Husser D, Genomic risk scores in atrial fibrillation: predicting the unpredictable? Eur Heart J 2013;34:2227–9.
    Crossref | PubMed
  87. Smith JG, Newton-Cheh C, Almgren P, et al. Genetic polymorphisms for estimating risk of atrial fibrillation in the general population: a prospective study. Arch Intern Med 2012;172:742–4.
    Crossref | PubMed
  88. Everett BM, Cook NR, Conen D, et al. Novel genetic markers improve measures of atrial fibrillation risk prediction. Eur Heart J 2013;34:2243–51.
    Crossref | PubMed
  89. Lubitz SA, Lunetta KL, Lin H, et al. Novel genetic markers associate with atrial fibrillation risk in Europeans and Japanese. J Am Coll Cardiol 2014;63:1200–10.
    Crossref | PubMed
  90. Manolio TA, Collins FS, Cox NJ, et al. Finding the missing heritability of complex diseases. Nature 2009;461:747–53.
    Crossref | PubMed
  91. Mahida S, Mills RW, Tucker NR, et al. Overexpression of KCNN3 results in sudden cardiac death. Cardiovasc Res 2014;101:326–34.
    Crossref | PubMed
  92. Zhang XD, Timofeyev V, Li N, et al. Critical roles of a small conductance Ca2+-activated K+ channel (SK3) in the repolarization process of atrial myocytes. Cardiovasc Res 2014;101:317–25.
    Crossref | PubMed
  93. Qi XY, Diness JG, Brundel BJ, et al. Role of smallconductance calcium-activated potassium channels in atrial electrophysiology and fibrillation in the dog. Circulation 2014;129:430–40.
    Crossref | PubMed
  94. Kirchhof P, Kahr PC, Kaese S, et al. PITX2c is expressed in the adult left atrium, and reducing Pitx2c expression promotes atrial fibrillation inducibility and complex changes in gene expression. Circ Cardiovasc Genet 2011;4:123–33.
    Crossref | PubMed
  95. Wang J, Klysik E, Sood S, et al., Pitx2 prevents susceptibility to atrial arrhythmias by inhibiting left-sided pacemaker specification, Proc Natl Acad Sci USA 2010;107:9753–8.
    Crossref | PubMed
  96. Chinchilla A, Daimi H, Lozano-Velasco E, et al. PITX2 insufficiency leads to atrial electrical and structural remodeling linked to arrhythmogenesis. Circ Cardiovasc Genet 2011;4:269–79.
    Crossref | PubMed
  97. Sanseau P, Agarwal P, Barnes MR, et al. Use of genomewide association studies for drug repositioning, Nat Biotechnol 2012;30:317–20.
    Crossref | PubMed
  98. Parvez B, Vaglio J, Rowan S, et al. Symptomatic response to antiarrhythmic drug therapy is modulated by a common single nucleotide polymorphism in atrial fibrillation. J Am Coll Cardiol 2012;60:539–45.
    Crossref | PubMed
  99. Parvez B, Chopra N, Rowan S, et al. A common beta1- adrenergic receptor polymorphism predicts favorable response to rate-control therapy in atrial fibrillation. J Am Coll Cardiol 2012;59:49–56.
    Crossref | PubMed
  100. Lanham KJ, Oestreich JH, Dunn SP, Steinhubl SR. Impact of genetic polymorphisms on clinical response to antithrombotics. Pharmgenomics Pers Med 2010;3:87–99.
    Crossref | PubMed
  101. Takeuchi F, McGinnis R, Bourgeois S, et al. A genome-wide association study confirms VKORC1, CYP2C9, and CYP4F2 as principal genetic determinants of warfarin dose. PLoS Genet 2009;5:e1000433.
    Crossref | PubMed
  102. Turner RM, Pirmohamed M. Cardiovascular pharmacogenomics: expectations and practical benefits. Clin Pharmacol Ther 2014;95:281–93.
    Crossref | PubMed
  103. Husser D, Adams V, Piorkowski C, et al., Chromosome 4q25 variants and atrial fibrillation recurrence after catheter ablation, J Am Coll Cardiol 2010;55:747–53.
    Crossref | PubMed
  104. Shoemaker MB, Muhammad R, Parvez B, et al. Common atrial fibrillation risk alleles at 4q25 predict recurrence after catheter-based atrial fibrillation ablation. Heart Rhythm 2013;10:394–400.
    Crossref | PubMed
  105. Parvez B, Shoemaker MB, Muhammad R et al. Common genetic polymorphism at 4q25 locus predicts atrial fibrillation recurrence after successful cardioversion. Heart Rhythm 2013;10:849–55.
    Crossref | PubMed
  106. Yang Y, Lin X, Yang Y, et al. Novel KCNA5 loss-of-function mutations responsible for atrial fibrillation. J Hum Genet 2009;54:277–83.
    Crossref | PubMed
  107. Christophersen IE, Olesen MS, Liang B, et al. Genetic variation in KCNA5: impact on the atrial-specific potassium current IKur in patients with lone atrial fibrillation. Eur Heart J 2013;34:1517–25.
    Crossref | PubMed
  108. Olesen MS, Yuan L, Liang B, et al. High prevalence of long QT syndrome-associated SCN5A variants in patients with earlyonset lone atrial fibrillation. Circ Cardiovasc Genet 2012;5:450–9.
    Crossref | PubMed
  109. Olesen MS, Jespersen T, Nielsen JB, et al. Mutations in sodium channel beta-subunit SCN3B are associated with early-onset lone atrial fibrillation. Cardiovasc Res 2011;89:786–93.
    Crossref | PubMed
  110. Oberti C, Wang L, Li L, et al. Genome-wide linkage scan identifies a novel genetic locus on chromosome 5p13 for neonatal atrial fibrillation associated with sudden death and variable cardiomyopathy. Circulation 2004;110:3753–9.
    Crossref | PubMed
  111. Sun Y, Yang YQ, Gong XQ, et al. Novel germline GJA5/ connexin40 mutations associated with lone atrial fibrillation impair gap junctional intercellular communication. Hum Mutat 2013;34:603–9.
    Crossref | PubMed
  112. Yang YQ, Liu X, Zhang XL, et al. Novel connexin40 missense mutations in patients with familial atrial fibrillation. Europace 2010;12:1421–7.
    Crossref | PubMed
  113. Yang YQ, Zhang XL, Wang XH, et al. Connexin40 nonsense mutation in familial atrial fibrillation. Int J Mol Med 2010;26:605–10.
    Crossref | PubMed
  114. Christophersen IE, Holmegard HN, Jabbari J, et al. Rare variants in GJA5 are associated with early-onset lone atrial fibrillation. Can J Cardiol 2013;29:111–6.
    Crossref | PubMed
  115. Liang C, Li X, Xu Y, et al. KCNE1 rs1805127 polymorphism increases the risk of atrial fibrillation: a meta-analysis of 10 studies. PLoS One 2013;8:e68690.
    Crossref | PubMed
  116. Wirka RC, Gore S, Van Wagoner DR, et al. A common connexin-40 gene promoter variant affects connexin-40 expression in human atria and is associated with atrial fibrillation. Circ Arrhythm Electrophysiol 2011;4:87–93.
    Crossref | PubMed
  117. Tsai CT, Lai LP, Lin JL, et al. Renin-angiotensin system gene polymorphisms and atrial fibrillation. Circulation 2004;109:1640–6.
    Crossref | PubMed
  118. Kaab S, Darbar D, van Noord C, et al. Large scale replication and meta-analysis of variants on chromosome 4q25 associated with atrial fibrillation. Eur Heart J 2009;30:813–9.
    Crossref | PubMed
  119. Viviani Anselmi C, Novelli V, Roncarati R, et al. Association of rs2200733 at 4q25 with atrial flutter/fibrillation diseases in an Italian population. Heart 2008;94:1394–6.
    Crossref | PubMed
  120. Shi L, Li C, Wang C, et al. Assessment of association of rs2200733 on chromosome 4q25 with atrial fibrillation and ischemic stroke in a Chinese Han population. Hum Genet 2009;126:843–9.
    Crossref | PubMed
  121. Gudbjartsson DF, Holm H, Gretarsdottir S, et al. A sequence variant in ZFHX3 on 16q22 associates with atrial fibrillation and ischemic stroke. Nat Genet 2009;41:876–8.
    Crossref | PubMed
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