Introduction
Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice, and is increasing in both incidence and prevalence1, 2. Over two million Americans currently have AF, and estimates project that between 5 and 12 million will be affected by 20501, 2. AF is associated with substantial morbidity, including one third of all strokes in patients above the age of 653 and a two-fold increased risk of mortality4, 5. The costs attributable to the care of individuals with AF are in excess of $6.4 billion per year3.
AF is often associated with hypertension and structural heart disease, and traditionally, has not been considered a genetic condition. However, a number of recent studies have demonstrated that AF and in particular, lone AF, have a substantial genetic basis6-9. Mutations in several ion channels have been identified in individuals with familial AF10-17, although they appear to be rare causes of the arrhythmia18, 19. Recently, a genome wide association study has led to the identification of genetic variants associated with common forms of AF. In the course of this review we will discuss the heritability of AF, the methods employed to identify causal variants underlying AF, and our current understanding of genetic variation implicated in AF.
Atrial fibrillation is a heritable condition
While familial forms of AF have long been reported,20 a genetic predisposition for more common forms of AF has only recently been recognized. In 2003, Fox and coworkers studied more than 5,000 individuals whose parents were enrolled in the original Framingham Heart Study8. Over a 19 year follow up period, they found that the development of AF in the offspring was independently associated with parental AF, particularly if the Offspring cohort was restricted to those under the age of 75 and without antecedent heart disease. Having a parent with AF approximately doubled the four-year risk of developing AF, even after adjustment for risk factors such as hypertension, diabetes mellitus, and myocardial infarction.
Arnar and colleagues similarly described a genetic predisposition to AF in a study of more than 5,000 Icelanders in 20066. After assessing relatedness from a nationwide genealogical database, eighty percent of those with AF were related to another individual with AF. The relative risk of AF for first-degree relatives of a family member with AF was 1.77, when compared to individuals in the general population. The relative risk for AF increased to 4.67 when the sample was restricted to individuals less than 60 years old.
Further evidence of the heritability of AF was demonstrated in a chart review of over two thousand patients with AF referred for evaluation at the Mayo Clinic9. Five percent of subjects had a family history of AF, and the number was as high as 15% among those with lone AF. In 2005, we found that nearly 40% of individuals with lone AF referred to the Arrhythmia Service at Massachusetts General Hospital had at least one relative with the arrhythmia, and a substantial number reported having multiple affected relatives7. In over 90% of cases, AF in the relatives could be verified. To obtain a crude index of heritability, we determined the prevalence of AF among each class of relative compared to that among age and sex-matched subjects. The relative risk of AF was increased among family members, and ranged from 2 fold in fathers to nearly 70 fold in male siblings.
Genetic studies in atrial fibrillation
Once a condition is found to be heritable, there are several techniques that are commonly employed to identify the genetic basis of a disease. These include linkage analysis, candidate gene resequencing and association studies. We will discuss each of these methods in the context of their application to AF.
Linkage analysis
The genes that underlie simple monogenic disorders with a Mendelian pattern of inheritance can be identified using linkage analysis. When passed from generation to generation, genetic markers that lie close together on the same chromosome are likely to be transmitted en bloc in proportion to their proximity to each other. A genome wide search for groups of markers that co-segregate with the disease as it travels through a family tree is performed to identify the approximate location of a genetic disease locus. Linkage studies report a logarithm of the odds or LOD score that reflects the likelihood of two markers or a marker and disease co-segregating when compared with chance alone. A LOD score of 3 or more (or odds of greater than a 1000:1) is considered statistically significant. Traditionally, restriction enzyme sites and microsatellite repeats have been used as genetic markers, but more recently, it has become possible to use single nucleotide polymorphisms, or SNPs21. The ease of use in genotyping has made SNPs the most widely used genetic markers today.
Linkage analysis can be used to narrow the search for a causative gene to a chromosomal locus, or relatively small region of the human genome associated with disease. However, this limited region may still contain hundreds of genes spread over millions of base pairs. Once a genetic locus is identified, online data from human genome databases, developed as a result of the Human Genome Project, are used to identify candidate genes within the locus. These genes are then sequenced in affected individuals in an attempt to identify the sequence variants that correlate with the disease.
Once a base pair change is identified, it is important to differentiate between a mutation and a genetic polymorphism, or common variant in the genome. For a sequence alteration to be considered a mutation it must segregate with the disease, have a plausible mechanism, and not be found in normal controls. Ultimately, the mutation should be sufficient to cause the phenotype, either in a human kindred or in a genetic model organism.
While several genetic loci have been reported in kindreds with Mendelian AF in which specific genetic mutations have yet to be identified,22-25 linkage analysis has facilitated the identification of individual mutations in several cases of familial AF.10, 26
In one such family of Chinese descent, Chen and coworkers identified a mutation in KCNQ110, a potassium channel that underlies the slowly repolarizing current in cardiomyocytes known as IKs (see Figure 1). The investigators were able to map the disease locus to a 12 megabase region on the short arm of chromosome 11, in a four-generation family with AF. The KCNQ1 gene was located within this region, and sequencing revealed a serine to glycine missense mutation at position 140 (S140G) in affected family members. The S140G mutation is located in the first transmembrane-spanning segment27 at the outer edges of the voltage-sensing domain and far from the pore-forming region of the potassium channel structure. The S140G mutation results in a gain of channel function, in contrast to mutations in KCNQ1 associated with the long QT syndrome that typically result in a loss of channel function. In cultured cells, expression of the S140G mutant channel resulted in dramatically enhanced potassium channel currents and markedly altered potassium channel gating kinetics, which would be predicted to increase IKs. Such an increase would be expected to lead to a shortening of the action potential duration and thus predispose atrial myocytes to reentry and subsequent AF (see Figure 1).
While the identification of this mutation provided an initial inroad into the pathogenesis of AF, this family also illustrates our limited understanding of the role of the KCNQ1 channel in atrial versus ventricular repolarization. Specifically, it remains unclear why a mutation that results in an in vitro gain of function in KCNQ1 is associated with delayed ventricular repolarization, as manifested by a prolonged QT interval, in more than half of the individuals with the S140G mutation.
Other gain of function mutations in KCNQ1 have been associated with the short QT syndrome28. Hong and colleagues reported an unusual case of AF detected in utero and confirmed on electrocardiogram upon delivery of the newborn29. The infant's electrocardiogram also displayed a short QT interval. Based on this association, they sequenced the KCNQ1 gene and found a valine to methionine mutation in position 141 (adjacent to the mutated position found by Chen and colleagues). Like the S140G mutation, in vitro expression of V141M mutant channels displayed markedly enhanced current density and altered gating kinetics.
More recently, Hodgson-Zingman and colleagues identified a frameshift mutation in NPPA in a family with AF26. The mutation in NPPA, which encodes atrial natriuretic peptide, resulted in increased levels of a circulating mutant peptide. Electrophysiological assessment in a rat heart model revealed decreased action potential duration, consistent with known mechanisms of reentrant mediated AF30.
Candidate gene studies
A candidate gene can be any gene that is hypothesized to cause a disease. Based on the work relating KCNQ1 to AF, investigators have considered other potassium channels as potential candidate genes for AF and screened for mutations in these genes in cohorts of subjects with AF.
Otway and colleagues examined 50 kindreds with AF and amplified the genes for KCNQ1 and KCNE1-3, accessory subunits of KCNQ114. They found a single mutation in KCNQ1 in only one family—an arginine to cysteine change at amino acid position 14 (R14C) in KCNQ1. Unlike the S140G mutation discovered by Chen and colleagues, R14C had no significant effect on KCNQ1/KCNE1 current amplitudes in cultured cells at baseline. However, upon exposure to hypotonic solution, mutant channels exhibited a marked increase in currents compared to wild type channels. Interestingly, of those who carried the R14C mutation, only those with left atrial dilatation had AF, leading the authors to propose a “two-hit” hypothesis for AF. They also identified a mutation in KCNE2 in two of the kindreds. Like the S140G mutation in KCNQ1, the mutation in KCNE2 (R27C) dramatically increased the amplitude of IKs11.
Finally, other work has suggested that the relationship between potassium channels and AF extends beyond IKs. The work of Xia and colleagues may also implicate KCNJ2, an inward rectifier potassium channel that underlies the IK1 current, in AF12. In their work, a V93I mutation was found in all affected members in one kindred with familial AF. The V93I also lead to gain of function KCNJ2 channels, which increase potassium current amplitudes. However, there is still an incomplete understanding of how an increase in the background current IK1 might lead to atrial arrhythmias.
We have screened our cohort with lone AF for mutations in KCNQ1, KCNJ2, and KCNE1-5 and were unable to find any mutations in these genes18. These findings suggest that potassium channels are an uncommon cause of AF and there is much more to be learned about the diversity of molecular pathways that lead to this arrhythmia.
The genes encoding connexins, gap-junction proteins that mediate the spread of action potentials between cardiac myocytes, have also been examined as potential candidates for AF. Prior work has shown that mice with null alleles of GJA5, the gene for connexin40, exhibit atrial reentrant arrhythmias31. From this work, Gollob and coworkers considered this gene as a potential candidate in individuals with idiopathic AF who underwent pulmonary vein isolation surgery32. An analysis of DNA isolated from their cardiac tissue showed that four of the fifteen subjects had mutations in GJA5 that markedly interfered with the electrical coupling between cells. In three of the patients, DNA isolated from their lymphocytes lacked the same mutation in GJA5 indicating that the connexin40 mutations had been acquired after fertilization or was a somatic mutation. One of the four individuals carried the mutation in both cardiac tissue and lymphocytes consistent with a germline rather than somatic mutation. However, more information about the transmission of AF in relatives of these individuals was not available.
Association studies
Although traditional methods such as linkage analysis can be applied to families where the phenotype and pattern of inheritance are consistent with a monogenic disorder, the mode of transmission for AF is less clear. Association studies have been used in an attempt to identify the genetic basis of AF and other apparently complex traits. In an association study the frequency of a genetic marker, such as a SNP, is compared between individuals with an outcome (cases) and those without an outcome (controls). Over the past ten years, many case-control and cohort studies have been performed in subjects with AF, leading to the identification of variants associated with disease. These studies have typically tested a small number of variants and have been directed at candidate genes previously believed to be involved in AF. Examples include genes encoding products involved in regulation of the renin-angiotensin-aldosterone axis 33-40, calcium handling51, as well as neurohormonal39 and lipoprotein51 pathways. Additionally, genes encoding gap junction proteins45, 46, ion channels15, 44, 47-50, interleukins41, 42, signaling molecules34, 43, 44, and mediators of other molecular pathways52 have been examined (summarized in Table 2). Unfortunately, these studies have been limited by a low prior probability of any polymorphism truly being associated with AF. Further complicating these analyses are the small sample sizes, a lack of replication in distinct populations, as well as phenotypic and genetic heterogeneity.
Table 2.
Gene | Variant | Cases | Controls | OR | P value | Replicated? | Comments | Reference |
---|---|---|---|---|---|---|---|---|
- | rs2200733 | 3,913 | 22,092 | 1.72 | 3.3×10-41 | Yes | Identified in GWAS | 61 |
- | rs10033464 | 3,913 | 22,092 | 1.39 | 6.9×10-11 | Yes | Identified in GWAS | 61 |
ACE | D/D | 51 | 289 | 1.5 | 0.016 | Yes | In patients with CHF | 34 |
ACE | D/D | 404 | 520 | 1.89 | <0.001 | - | 36 | |
Aldosterone synthase | T-344C | 63 | 133 | 2.65 | 0.03 | No | In patients with CHF | 40 |
Angiotensinogen | M235T | 250 | 250 | 2.5 | <0.001 | No | 33 | |
Angiotensinogen | G-6A | 250 | 250 | 3.3 | 0.005 | No | 33 | |
Angiotensinogen | G-217A | 250 | 250 | 2.0 | 0.002 | No | 33 | |
Angiotensinogen | T174M | 968 | 8267 | 1.2 | 0.05 | No | 37 | |
Angiotensinogen | 20 C/C | 968 | 8267 | 1.5 | 0.01 | No | 37 | |
CETP | Taq1B | 97 | 97 | 0.35 | 0.05 | No | 51 | |
Connexin 40 | -44A | 14 | 16 | 5.3 | 0.0019 | Yes | 46 | |
Connexin 40 | -44A, +71G | 173 | 232 | 1.514 | < 0.006 | - | 45 | |
Endothelin-2 | A985G | 26 | 84 | 5.89 | 0.018 | No | In patients with HCM | 39 |
eNOS | 894T/T | 51 | 289 | 3.2 | 0.001 | No | In patients with CHF | 34 |
eNOS | T-786C | 331 | 441 | 1.4 | 0.05 | No | 44 | |
GNB3 | C825T | 291 | 292 | 0.46 | 0.02 | No | 43 | |
Heat shock protein 70 | Met439Thr | 48 | 194 | 2.43 | 0.016 | No | In postoperative CABG patients | 52 |
Interleukin 6 | G-174C | 26 | 84 | 3.25 | 0.006 | No | In postoperative CABG patients | 41 |
Interleukin 10 | A-592C | 196 | 873 | 0.32 | 3.70×10-03 | No | Lone AF | 42 |
KCNE4 | E145D | 142 | 238 | 1.66 | 0.044 | No | 50 | |
KCNE5 | 97T | 158 | 96 | 0.52 | 0.007 | No | 49 | |
KCNH2 | K897T | 1207 | 2475 | 1.25 | 0.00033 | No | 47 | |
MinK | 38G | 108 | 108 | 1.8 | 0.024 | Yes | 48 | |
MinK | 38G | 331 | 441 | 1.73 | <0.005 | - | 44 | |
MMP2 | C1306T | 196 | 873 | 8.1 | 1.26×10-02 | No | Lone AF | 42 |
SLN | G-65C | 147 | 92 | 0.011 | No | 63 |
In recent years, genome wide association studies (GWAS) have been made possible by advancements in genotyping technology that allow investigators to assay hundreds of thousands of SNPs spread over the entire human genome. The studies are typically executed using a case-control study design53. GWAS attempt to identify novel genetic polymorphisms that are significantly more or less common in a group with a disease as compared to a control group. Since the markers are spread over the entire genome, these experiments give no weight to existing candidate genes. Such studies have been used successfully in the past several years to identify potential novel pathways for diabetes54, obesity55, coronary heart disease56, 57, macular degeneration58, and repolarization59.
While GWAS have the potential to identify new pathways for disease, they also have a number of limitations. In particular, with hundreds of thousands of individual associations being tested, these studies have a high likelihood of producing false positive associations. There is still discussion within the field of what the threshold level should be for genome-wide significance60. False positive results can also emerge from population stratification or the failure to properly control for ethnicity, thus resulting in over or under representation of spurious ethnic specific markers. While variations in study design have been proposed in an effort to eliminate false associations, ultimately replication of the associations in other populations is the best method of validation53.
The biological significance of the identified variants is another concern. Most variants found in genetic association studies have been associated with relatively weak effects, with typical odds ratios ranging from ~1.3 to 1.5. While such variants may generate new ideas about disease pathogenesis, understanding the biological mechanisms by which the majority of variants confer disease susceptibility remains challenging.
Recently, a team led by the researchers at deCODE genetics have reported the results of a GWAS for AF. Gudbjartsson and colleagues examined over 300,000 SNPs and identified two polymorphisms on the long arm of chromosome 4 (4q25) that were highly associated (p = 3.3×10-41, and 6.9×10-11, respectively) with AF or atrial flutter in a group of Icelanders61. In order to improve both the validity and generalizability of the findings, the study was replicated in other populations in Iceland, Sweden, the United States and Hong Kong. Neither variant was correlated with obesity, hypertension, or myocardial infarction suggesting that the genetic variants are not associated with AF by affecting those risk factors.
How do the variants on chromosome 4 lead to AF? At present, the mechanism of action of these variants is unclear. Interestingly, these SNPs lie upstream from a gene that could plausibly play a role in the pathogenesis of AF, the paired-like homeodomain transcription factor 2, PITX2. This gene is known to be critical in the development of the left atrium62, pulmonary myocardium, and in the suppression of left atrial pacemakers cells in early development. Future work will hopefully clarify the mechanism underlying the association of these markers with AF.
Refining genetic studies of atrial fibrillation
In order to continue to improve upon the utility of genetic studies for AF we will need to overcome a number of obstacles. A critical step in any genetic study is the ability to correctly assign the diagnosis. AF represents a particular challenge because many individuals are asymptomatic, some have paroxysmal disease, and yet others develop AF late in life. Genotypic and phenotypic heterogeneity further complicate the classification of AF. Rather than a single entity, AF may represent the final common pathway for a number of distinct pathogenic insults such as heart failure, hypertension, or thyroid abnormalities.
In order to address these challenges, we will have to continue to improve upon the characterization and classification of AF. The identification of endophenotypes, or subtle, heritable traits that co-segregate with AF, may help to refine ongoing genetic studies. For AF, endophenotypes such as specific P wave morphologies, pulmonary venous anatomy as assessed by computed tomography or magnetic resonance imaging, or biomarkers that are heritable and easily detectable may be helpful.
Summary
In summary, recent studies of AF have identified mutations in a series of ion channels. However, these channels appear to be relatively rare causes of AF. A recent genome wide association study for AF has identified novel variants associated with the disease, though the mechanism of action for these variants remains unknown. Ultimately, a greater understanding of the genetics of AF should yield insights into novel pathways, therapeutic targets, and diagnostic testing for this common arrhythmia.
Table 1.
Genes | |||||
---|---|---|---|---|---|
Chr | Gene name | Effect | Inheritance | Reference | |
11p15.5 | KCNQ1/KvLQT1 | Increases IKs, Expected to shorten APD | AD | 10,14 | |
21q22.1 | KCNE2/MiRP1 | Increases IKs | AD | 11 | |
17q23.1-24.2 | KCNJ2 | Increases IK1; Expected to shorten APD | AD | 12 | |
12p13 | KCNA5 | Loss of IKur; prolongs APD | AD | 13 | |
3p21 | SCN5A | Hyperpolarizing shift of resting membrane potential; Expected to prolong APD | AD | 15-17 | |
1p36-p35 | NPPA | Results in mutant atrial natriuretic peptide; associated with shortened APD | AD | 26 |
Genetic Loci | Chr | Gene | Comments | Inheritance | Reference |
---|---|---|---|---|---|
5p13 | Unknown | Associated with sudden death | AR | 24 | |
6q14-q16 | Unknown | Overlaps with locus for DCM | AD | 23 | |
10q22-q24 | Unknown | Overlaps with locus for DCM | AD | 22 | |
10p11-q21 | Unknown | AD | 25 |
Abbreviations: Chr, Chromosome; AD, autosomal dominant; AR, autosomal recessive; DCM, dilated cardiomyopathy.
Acknowledgements
This work was supported by an NIH award to Dr. MacRae (HL75431) and a grant to from the Disque Deane Foundation to Dr. Ellinor.
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