Skip main navigation

Epigenetic and Transcriptional Networks Underlying Atrial Fibrillation

Originally published Research. 2020;127:34–50


Genome-wide association studies have uncovered over a 100 genetic loci associated with atrial fibrillation (AF), the most common arrhythmia. Many of the top AF-associated loci harbor key cardiac transcription factors, including PITX2, TBX5, PRRX1, and ZFHX3. Moreover, the vast majority of the AF-associated variants lie within noncoding regions of the genome where causal variants affect gene expression by altering the activity of transcription factors and the epigenetic state of chromatin. In this review, we discuss a transcriptional regulatory network model for AF defined by effector genes in Genome-wide association studies loci. We describe the current state of the field regarding the identification and function of AF-relevant gene regulatory networks, including variant regulatory elements, dose-sensitive transcription factor functionality, target genes, and epigenetic states. We illustrate how altered transcriptional networks may impact cardiomyocyte function and ionic currents that impact AF risk. Last, we identify the need for improved tools to identify and functionally test transcriptional components to define the links between genetic variation, epigenetic gene regulation, and atrial function.

Although historically considered an acquired disease, atrial fibrillation (AF) risk is now understood to include a major heritable component, with evidence including rare mutations in families and association with common variants in the population at large.1–3 Recent work by large consortia have identified over 100 genetic loci associated with AF by genome-wide association studies (GWAS; for detailed review, please see the accompanying review by Roselli and colleagues.).4,5 Together with epidemiological studies, the current model suggests that AF is a complex trait, influenced by a combination of multiple genetic and environmental risk factors that contribute cumulatively to disease predisposition.6–8 Genome-wide polygenic risk scores of common variants have recently been developed that identify individuals at >3-fold increased risk of AF in the general population.9 In the near future, use of such approaches may identify high-risk patients with a combination of inherited susceptibilities and potentially provide preventive treatment or lifestyle adjustments to decrease AF risk. However, the polygenic risk score currently does not help to determine a targeted treatment, as it does not give any specific information on the affected molecular pathways, such as ion flux deficiencies affecting conduction velocity or myocardial automaticity, or ancillary pathologies such as fibrosis.

Most AF-associated GWAS variants reside in the noncoding genome. The majority of disease-associated variants, as well as variants in high linkage disequilibrium, are enriched in predicted transcriptional regulatory elements, implying that the identified GWAS variants alter transcriptional regulation.10–12

A number of loci harboring TFs, including PITX2, ZFHX3, PRRX1, TBX5, NKX2-5, and HAND2, have been linked to AF susceptibility (Supplemental Table 1). While information regarding the actual target genes of the noncoding variation (ie, variant regulatory elements) in most of these loci is still lacking, experimental evidence from animal and cell models with modified transcription factor genes has implicated these transcription factors in normal atrial rhythm control and the pathology of AF (Supplemental Table 1).47–59 These transcription factors target thousands of regulatory elements for genes involved in atrial function, including ion channels, gap junctions, structural/cytoskeletal components, and others.60–64 Thus, growing evidence supports a transcriptional model for the mechanisms underlying GWAS in AF: noncoding variation in regulatory sequences affects expression of genes encoding transcriptional regulators and/or function of regulatory elements of given target genes, altering gene expression and conferring disease susceptibility (Figure 1). Therefore, resolution of the individual components of the GWAS-associated transcriptional networks influenced by genetic variation is a fundamental goal of current research.

Figure 1.

Figure 1. Simplified scheme depicting the relation between noncoding variants clustered in AF-associated regions, regulatory elements, transcription factor (TF) dose, and target genes.A, An atrial fibrillation (AF)-associated genetic locus is depicted with neighboring genes. The major allele of an AF-associated variant lies in a regulatory element, which interacts with transcription factors and cofactors leading to physiological levels of transcription of (a) target gene(s) through physical interaction. On the right, 2 situations are depicted that could result from the presence of a minor (risk) allele in the regulatory element. The risk allele could interfere with binding of the correct TF, leading to diminished expression of target genes. The risk allele could cause altered binding affinity favoring another TF, causing (tissue-specific) gain/loss of expression of target gene(s), which results in AF predisposition. B, Variation in a regulatory element (RE) can lead to the altered expression level of a gene for a TF (yellow) or target effector gene. The changed dose of the TF influences the expression of many effector genes, possibly including its own gene.

In this review, we summarize the progress of the field in linking transcription factor networks to AF and discuss currently employed approaches, progress, and difficulties in identifying the variants and target genes contributing to AF development. We describe the potential role and current knowledge of epigenetics (ie, gene regulation by transcription factors, chromatin states, and conformation) in modulating the effect of variant regulatory elements. Moreover, we discuss the combinatorial effects of multiple common variants that could increase predisposition to AF. Finally, we discuss AF variants in the context of development and aging.

Transcription Factors, Atrial Rhythm, and AF Risk

The identification of noncoding regulatory elements harboring AF risk implies the importance of transcriptional regulators in AF, borne out by the identification of AF risk signals at many transcription factor loci. There are hundreds of validated and suspected transcription factors, some of which have tissue- or condition-specific expression to regulate cell differentiation and developmental programs (reviewed in study by Lambert et al65). The binding of accessible regulatory elements is dependent on both availability and activity (including binding affinity) of transcription factors. Futhermore, transcription factor dose is crucial in the regulation of transcription. For example, mutations or haploinsufficiency of developmentally expressed transcription factors such as TBX5, NKX2-5, PRRX1, HAND2, and PITX2 result in genetic syndromes that have many symptoms including congenital defects of the heart and other tissues and conduction abnormalities (Supplemental Table 1).66–68 Many common variants associated with AF and cardiac conduction are near these and other transcription factor-encoding genes, suggesting that variation in their regulation may impact AF predisposition.

An example of the importance of transcription factor dosage in AF is found in the PITX2 locus. PITX2 expresses 3 isoforms, PITX2A, PITX2B, and PITX2C, the latter being expressed from an independent promoter and representing the major cardiac isoform.69–72 Mouse studies revealed that Pitx2c is required for asymmetrical heart development, imposing left-sided identity on the left atrium and sinus venosus (including suppression of sinoatrial node development at the left side), and for proper development of the pulmonary vein myocardium.73–77 In addition, cardiac expression of PITX2 is almost exclusively restricted to the left atrium in the normal adult heart,70 and its expression increases after myocardial injury in the nenonatal left ventricle.78 Both the left atrium and the sleeves of the pulmonary vein are potential substrates for development of AF, and the pulmonary vein is often the origin of ectopic foci in patients with AF.13 While homozygous Pitx2c mutant mice display embryonic lethality, reduced expression of Pitx2c during development results in AF inducibility upon stimulation, which implicates a developmental role of this gene in AF susceptibility.69–71 One of the key findings of the gene expression studies is that PITX2 is one of the most differentially expressed left atrial genes in healthy adult heart. In addition, genes associated with a right atrial phenotype such as BMP10 (bone morphogenic protein 10) seem to be repressed by PITX2, thus linking PITX2 to left-sided identity of the left atrium.51,70,78 Various possible mechanistic explanations of PITX2 deficiency resulting in AF have been proposed. It has been observed that reduction in Pitx2 resulted in potassium and calcium channel gene dysregulation, ultimately leading to a shortening of the atrial action potential, a depolarized resting membrane potential, and predisposition to AF (Figure 2).13,55Pitx2 may also regulate miR-17-92 and miR-106b-25; when deleted from the mouse genome, AF susceptibility occurred.79 Analysis of gene expression in an atrial-specific knockout of Pitx2 demonstrated similar transcriptional alterations in calcium homeostasis and AF-related microRNAs (Figure 2).80 In addition, through the use of zebrafish and murine models of Pitx2 deficiency, changes in sarcomere length and metabolic dysfunction in cardiomyocytes were linked to AF.51,54,81,82 These findings suggest that, although Pitx2-deficient mice with a predisposition to AF apparently have morphologically normal left atria,13,70,78 there are critical Pitx2 targets beyond ion channels that have important implications for electrophysiology. The postnatal function of Pitx2 was investigated using conditional deletion techniques, resulting in altered expression of genes encoding ion channels, cell junction proteins, calcium handling genes, and transcription factors, several of which were previously implicated by GWAS.51,55,70,78 Importantly, these mice show ECG abnormalities indicative of sinus node dysfunction but surprisingly are not inducible for AF. These conditional deletion studies in mice seem to indicate that Pitx2 also has an important role in the postnatal left atria necessary for maintaining normal sinus rhythm, in addition to its more established developmental functions. Continued studies of the diverse roles of Pitx2 in both the developing and postnatal heart will deepen our understanding of this critical cardiac transcription factor. The multiple roles of Pitx2 are depicted in Figure 2.

Figure 2.

Figure 2. Schematic of PITX2 functions within a left atrial cardiomyocyte. It has been established that TBX5 is a transcriptional activator of PITX2. PITX2 is then capable of regulating gene expression throughout the genome, having both activating and repressing capabilities. Direct PITX2 targets, validated largely by luciferase assays and ChIP-seq, are listed by cellular function. While direct ion channel and gap junction genes were some of the first PITX2 targets discovered, there is now evidence in multiple model organisms that PITX2 is also capable of regulating cardiomyocyte metabolism and the antioxidant response to stress. Furthermore, PITX2 has been shown to directly alter microRNA transcription, which in turn can globally regulate gene expression in the cardiomyocyte. It is important to note that there are additional putative PITX2 targets not listed here that have been discovered through RNA sequencing experiments of PITX2 deficient murine models; however, these transcriptional alterations need further validation to distinguish direct targets of PITX2 from secondary effects. Overall, these transcriptional targets of PITX2 are able to determine critical physiological outputs of the cardiomyocyte, such as regulating cardiac rhythm and sarcomere structure (Illustration Credit: Ben Smith).

TBX5 is another well-studied example of a dose-dependent transcription factor, which is at the center of the cardiac transcription factor network. TBX5 mutations resulting in haploinsufficiency cause Holt-Oram syndrome of congenital upper limb (hand) and heart defects.22,83,84 In contrast, a gain-of-function mutation in a large Dutch family causes atypical Holt-Oram syndrome, without obvious cardiac structural defects, but with over 65% of affected family members displaying early onset AF.23 Regions near TBX5 have been associated with prolongation of the PR-interval in addition to AF risk.4,5,85 Murine models of spontaneous AF are rare; however, conditional deletion of Tbx5 in the adult heart resulted in a model of primary, spontaneous AF.48,86 This model identified Tbx5 as direct activator of Pitx2, with Tbx5 and Pitx2 antagonistically regulating the expression of key ion channel protein-encoding genes such as Scn5a, Gja1, Ryr2, and Atp2a2 (Figures 2 and 3). AF in Tbx5-deficient mice was mechanistically connected to calcium transport mechanisms and expression of Atp2a2, which encodes the main cardiac SERCA2 (sarco/endoplasmic reticulum Ca2+-ATPase).50,52 Genetic deletion of the SERCA2 inhibitor phospholamban (Pln) normalized SERCA function and rescued Tbx5 deficiency-associated AF. Interestingly, variants in the vicinity of PLN have also been associated with AF.4 Moreover, conditional haploinsufficiency of Tbx5 in adult mice caused reduced expression of predicted target genes and a predisposition to AF; additional Pitx2 haploinsufficiency rescued these phenotypes, supporting the notion of antagonistic gene regulation in vivo. These data resulted in a model describing an incoherent feed forward loop driven by TBX5 and modulated by PITX2 (Figure 2). This Tbx5-driven regulatory network was further interrogated using the sequencing of noncoding RNAs to identify regulatory elements crucial for cardiac rhythm homeostasis. This differential deep sequencing approach identified lncRNA-associated regulatory elements, which controlled the expression of calcium handling genes, including Ryr2 and Atp2a2.87 These data elucidated important aspects of the regulatory network driving calcium handling physiology and also identified a lncRNA required for Ryr2 expression.87 This lncRNA was also tightly associated with chromatin and required for stabilization of RNA PolII at the Ryr2 promoter, all of which suggested it had a functional role in gene expression.87 While noncoding RNA transcripts in AF will not be generally discussed in this review, the investigation of functional lncRNAs in future efforts could provide instrumental insight into the maintenance of gene expression for cardiac rhythm.

The cooperative interactions between the transcription factors TBX5, GATA4, and NKX2-5 is key for proper binding at their respective binding sites and cooperative regulation of the expression of the downstream target genes and of cardiac development.88,89 In contrast, Tbx5 and Gata4 were demonstrated to interact antagonistically for regulation of atrial rhythm control (Figure 3).52 The adult-specific Tbx5 haploinsufficiency phenotype of arrhythmia susceptibility and prolonged action potential duration48 was rescued by Gata4 haploinsufficiency but not by Nkx2-5 haploinsufficiency.52 Because Gata4 haploinsufficiency normalized the reduced expression of calcium channel genes Ryr2 and Atp2a2 in the Tbx5 haploinsufficient mice, it was hypothesized that the beneficial effect was mediated by rescue of calcium homeostasis (Figure 3). In the healthy cardiomyocyte, the inhibiting effect of Gata4, coupled with the stimulating effect of Tbx5 results in balanced Atp2a2 expression. However, in Tbx5 haploinsufficient mice, Gata4 overtakes the balance causing over-repression of Atp2a2 expression. Decreased Atp2a2 expression causes Ca2+ cycling dysfunction, resulting in reduced Ca2+-influx into the SR. Consequently, there is excess cytosolic Ca2+, leading to prolonged action potential duration and arrhythmia susceptibility. Gata4 haploinsufficiency in Tbx5 haploinsufficient mice restored the transcription factor dosage balance and sinus rhythm. Pln haploinsufficiency also had a similar rescuing effect on Tbx5 haploinsufficiency, validating the involvement of SERCA. This study illustrates the potential collaboration of transcription factors in a network, contributing to AF (Figure 3).

Figure 3.

Figure 3. Transcription factor networks control the expression of effector genes defining properties relevant for atrial structure, function and rhythm. Tbx5 and Pitx2 interact antagonistically as transcriptional activator and repressor, respectively, to co-regulate a gene regulatory network that governs effector genes involved in calcium cycling, sodium currents, potassium currents, and cell-cell conduction. Other transcription factors such as Gata4 and Nkx2-5 are also known to interact with Tbx5 and Pitx2 to co-regulate genes involved in atrial structural development and electrophysiological properties. The interactions of other transcription factors (such as Prrx1) in AF physiology and their role in atrial rhythm remain to be determined.

For a number of AF-associated genes, including TBX5, expression quantitative trait locus analysis predicts increased, not decreased expression will result in increased AF susceptibility.4 In line with this, the familial gain-of-function mutation in TBX5 caused early onset AF development in humans with atypical Holt-Oram syndrome.23 Consistently, when the mouse orthologs of PR interval or AF associated regions were removed from the Tbx3 and Tbx5 loci, respectively, moderate (<2-fold) increases in expression of the respective genes were observed, resulting in target gene deregulation and conduction disorders (VMC, unpublished data). These data indicate expression levels of these transcription factors are tightly balanced in sinus rhythm, and that similar to haploinsufficiency, mildly increased transcription factor expression can have physiologically relevant consequences. To date, loss of function models have been used to explore the role of particular genes in AF; however, the above findings suggest that physiological overexpression models need to be considered to appropriately recapitulate AF risk-associated changes in gene expression and accurately model AF molecular and clinical features.

Transcription is controlled by a combination of active and repressive transcription factors in concert with epigenetics, that is, molecules and mechanisms that can perpetuate alternative gene activity states in the context of the same DNA sequence.90 This definition includes DNA methylation, histone modifications, chromatin conformation, and (nuclear/noncoding) RNAs, all of which are involved in regulation of gene transcription. For summaries of the literature on the role of noncoding RNAs, mostly microRNAs, in AF we refer to others.91–96

Causative Variant Regions: Identification of Regulatory Elements and Single Nucleotide Polymorphisms That Alter Their Activity

The vast majority of trait-associated variants are located in noncoding regions of the genome. These regions are posited to contain elements acting as regulatory chromatin. Therefore, identification of those elements harboring functional phenotype-associated genetic variation is required to understand how transcriptional regulation contributes to AF. Genetic variation may alter the function of regulatory elements by disrupting the binding of transcription factors to the associated binding motif in the regulatory element (Figure 1) even if the variant does not directly disrupt the motif itself (reviewed in study by Deplancke et al12).

Several factors complicate the search for transcription-modulating variants and their target genes. Multiple regulatory elements could be present in a single disease-associated linkage disequilibrium block, where the function could be either redundant, additive, synergistic, or modulating.97,98 These properties are of critical physiological importance but are not captured in current functional regulatory element assays (Supplemental Table 2). Furthermore, while the mouse epigenome is more thoroughly characterized than that of human, conservation of mouse-human orthologous regulatory elements (based on H3K27ac chromatin immunoprecipitation sequencing) conservation is limited,129 and species-specific acquisition of new TF-binding sites within enhancers occurs frequently.130 Therefore, mouse (or other nonhuman) data sets used to predict regulatory elements are very useful but should be interpreted with care. Currently, our ability to predict the function of regulatory elements from epigenetic data is poor. Additionally, regulatory element in vivo function (eg, repression, modulating chromatin conformation) is poorly captured by most assays. Limited anecdotal examples of in vivo characterization of the physiological role of regulatory elements in atrial biology and AF has revealed the vast complexity of the gene regulatory mechanisms which control and establish normal cardiac rhythms.49,52,53,131

Several epigenomic features can define regulatory elements: occupancy by specific transcription factors, association with activating histone posttranslational modifications (such as H3K27ac), localization within DNase-hypersensitivity or assay for transposase accessible chromatin identified region of open chromatin, or the production of noncoding RNAs. For cell type or organ-specific occupation by a transcription factor or histones with an activating posttranslational modification, various assays have been developed over the decades (Supplemental Table 2). These epigenetic signatures have provided semicomprehensive lists of candidate cell type specific regulatory elements and insight into their behavior.132 A significant body of literature indicates that active regulatory elements are transcribed, producing noncoding RNAs.87,112,113,133,134 The production of noncoding RNA from regulatory elements has been used to quantitatively define regulatory element activity,133,135 including from native regulatory elements from the mouse atria in vivo.87 The identification of regulatory elements based on noncoding RNA transcription has been applied to identify functionally active context dependent enhancers and has allowed identification of functional transcription factor-dependent regulatory elements.87 In addition to their limited conservation across species, regulatory elements are often specific to cell-type and condition (developmental stage, stress).129,136 Therefore, epigenetic datasets derived from human atrial tissues, stages of development and (disease) conditions are required to accurately identify AF-relevant regulatory elements.

While regulatory elements have been identified over one megabase (Mb) away from the target promoter(s), they are thought to act in close proximity, by the 3-dimensional conformation of the DNA.137,138 Because regulatory elements need to be in close physical proximity to their target promoters to directly regulate transcriptional activity,116,139,140 knowledge of the conformation of the DNA is necessary in the search for target genes of variant regulatory elements.

More than a million candidate regulatory elements have been identified across the human genome, many of which are cell type- or condition-specific and temporally restricted.141,142 Nevertheless, the majority of regulatory elements lack functional validation and their target genes are largely unknown. Therefore, the invention and application of new high throughput methods for functional evaluation of regulatory elements is a current focus of functional genomics research. Currently, high-throughput enhancer assays such as STARR-seq and MPRA are utilized for the large scale discovery of regulatory elements (Supplemental Table 2) and have identified disease-relevant variant regulatory elements and expression modulating variants.97,102,103,143,144 While these approaches enable the functional screening of large regions of the genome, a major drawback is that they are extra/chromosomal and are thus not subject to chromatin packaging (Supplemental Table 2). Moreover, for practical reasons, most studies thus far have identified regulatory elements with an enhancing effect on transcription, while active repression of transcription is also physiologically relevant. New methods of identification and characterization of repressive regulatory elements is therefore essential for a comprehensive understanding of the relevant regulatory networks. Recently developed approaches consider regulatory element activity, and regulatory element-promoter contacts within native genomic context in model cell lines.107,108,144 Application of such assays in AF-relevant cell types, such as atrial cardiomyocytes or fibroblasts derived from human with or without AF or from animals modeling AF, will be technically challenging, but may uncover functional variant regulatory elements at scale.

Several efforts have been made to identify human cardiac enhancers.145,146 One study used >35 epigenomic data sets from mouse and human pre- and postnatal hearts to generate a reference of >80 000 putative human heart enhancers.147 Recently, the enhancer prediction tool EMERGE148 was trained using 70 human cardiac epigenetic data sets and a set of confirmed cardiac enhancers149 to predict human cardiac enhancers, 1750 of which are located in over 100 AF loci.49 The accessible chromatin in nondiseased human left atrial cardiomyocytes was identified using ATAC-seq.49,53 These regions represent >83 000 putative regulatory elements in human atrial cardiomyocytes. An example of the output of such assays of the locus PRRX1 is shown in Figure 4. Cross-referencing both this data set and the EMERGE predicted regulatory element set with AF-associated single nucleotide polymorphisms (SNPs)4 revealed 876 putative variant regulatory elements genome-wide.49 Several candidates were validated by luciferase assay, showing the potential of such a strategy for identification of disease-specific variant regulatory elements. Future efforts should provide epigenetic data from AF-prone heart tissue including pulmonary veins and from other cell types in the heart, such as fibroblasts, to identify tissue-specific AF-relevant variant regulatory elements.

Figure 4.

Figure 4. UCSC track showing the locus containing PRRX1 associated with atrial fibrillation (AF) and different datasets used for regulatory element and target gene identification. University of California, Santa Cruz (UCSC) browser view with lead variants (single nucleotide polymorphisms [SNP]), topologically associated domains (TAD; Juicebox TADs; Durand et al, 2016210; Robinson et al, 2018211), identified enhancers (Tucker et al47), SNPs associated with AF (P<10−4; Roselli et al4), annotated genes, promoter capture Hi-C (PCHi-C) data (Montefiori et al150), assay for transposase-accessible chromatin using sequencing (ATAC-seq) representing accessible chromatin in cardiomyocytes of left atria (Hill et al76; van Ouwerkerk et al49), EMERGE enhancer prediction (van Duijvenboden et al, 2015148; van Ouwerkerk et al49) and expression of left atria whole tissue (van Ouwerkerk et al49).

Identification of the Relationship Between Variant SNPs and Biological Effect

A second challenge is to link the variant regulatory elements to target genes (Figure 1). Regulatory element-promoter targeting is selective and regulated.97,140,151 The 3 principal approaches to define specific target genes of variant regulatory elements in any complex disease are (1) identification of the variant regulatory elements as described in the previous section; (2) identification of which genes at a locus are affected by associated variants (Figure 5); and (3) the variant regulatory element as well as the directional effect on transcription should be validated in vitro or in vivo, by, for example, genome editing of cultured human cells or mice, to enable evaluation of the biological effect of different components on transcriptional networks and (electro)physiological function (Figure 5).

Figure 5.

Figure 5. Commonly employed approaches and challenges to study the mechanistic link between genetic variation and atrial fibrillation (AF) predisposition. Technical challenges and unmet needs include relevant cell type- and condition-specific (hypertension, aging, etc) conformation and transcriptome data are required to define candidate AF-associated genes. Relevant cell type- and condition-specific epigenetic data are required to identify physiologically relevant variant regulatory elements. Assays to define physiologically relevant functions of regulatory elements (activation, repression, conformation, combinatorial) are required. Sensitive models to recapitulate the effect of variants are needed.

A common and productive approach to resolve which genes within a locus are targeted by the identified GWAS variants is to link the signals to levels of gene expression. Such expression quantitative trait locus (eQTL) analysis involves the correlation of risk variants with gene expression levels in a specific tissue relevant to the disease studied.4,5 However, only a fraction of the AF-associated sentinel SNPs could be linked to one or multiple candidate target genes. Because of the limited availability of relevant genotyped tissue or cell type (eg, left atrial cardiomyocytes, pulmonary vein wall, atrial fibroblasts) for expression analysis, lack of knowledge of relevant tissue for particular genes, and the inability to detect small but biological relevant differences in expression, we currently lack sufficient power to identify target genes for all AF-associated loci.152

Regulatory elements and target genes usually share the same topologically associated domain, a self-interacting genomic region in which DNA sequences physically interact with each other more frequently than with sequences outside the topologically associated domain.153,154 Topologically associated domain structures and loops within TADs are mediated by cohesin and CTCF (CCCTC-binding factor).151,155–158 Once regulatory elements have been identified, the potential target genes can be prioritized by interrogating their 3-dimensional chromatin contacts in the relevant tissue. Recent Hi-C analyses of human atrial and ventricular tissue combined with improved statistical analyses of interactions have provided a highly relevant contact map facilitating the identification of target genes.159 While regulatory element-promoter interactions are required for transcriptional regulation, close proximity observed by 3C-derived technologies does not necessarily imply a functional interaction.103,138 Moreover, Hi-C and promoter-capture (PC)-Hi-C technologies do not identify all possible interactions, as contacts within 30 kb are technically challenging to capture. Therefore, interactions between regulatory element and promoters, or a lack thereof, should be interpreted with care and require functional validation. In an effort to map cardiovascular disease risk loci interactions, 2 studies used Promoter capture Hi-C in human induced pluripotent stem cell–derived cardiomyocytes to map the promoter interactome, identifying interactions (physical proximity) between promoters and disease-associated regions with regulatory elements.150,160 Indeed, both studies found that interactions involving cardiovascular disease and heart rhythm GWAS SNPs were enriched in promoter interactions with genes relevant for heart development and disease. Similarly, Hi-C of primary cardiac tissue combined with a new more sensitive and quantitative loop calling has been used to link the heart regulatory interactome and disease variants.159 These genome-wide interaction maps of genome organization have also been useful to identify candidate target genes of AF variant regulatory elements, as Promoter capture Hi-C datasets identify interactions between potential regulatory elements and target gene promoters (Supplemental Table 2; Figure 4).49

While the variant regulatory elements and their target genes have been predicted based on features such as epigenetic signatures, expression, contact, and eQTL,49 a few functional variant regulatory elements and their target genes have been experimentally identified (Supplemental Table 3). The strongest AF risk locus—with associations orders of magnitude more significant than the subsequent loci—is found 150 kb upstream of the homeobox transcription factor PITX2 located on chromosome 4q25.4,5 One study found a risk variant in high LD with the sentinel SNP that confers differential expression mediated by a transcription factor in an intron of PITX2a/b, upstream of the promoter of the cardiac specific isoform PITX2c.161 The region containing the variant of interest (rs2595104) showed enhancer activity in zebrafish, and the risk allele, which is known to predispose to AF, demonstrated reduced enhancer activity in vitro. Subsequent CRISPR/Cas9-mediated deletion of the rs2595104 region in human stem cell-derived cardiomyocytes showed reduced PITX2c expression compared with the unedited allele.161 Moreover, rs2595104 lies in a TFAP2a binding site, a ubiquitously expressed transcription factor that was previously shown to be important for PITX2 expression.164,165 The authors verified that TFAP2a binds preferentially to the nonrisk allele, and that TFAP2a knock down mediated the reduction of PITX2c expression.

It was hypothesized that the highest risk-associated region near 4q25 was regulated by TBX5.48 Using a combination of ATAC-seq, 4C, and ChIP-qPCR (Supplemental Table 2), TBX5 was confirmed to occupy the regulatory element that is in contact with the PITX2 promoter in human LA. Furthermore, this RE regulatory element harbors an AF risk variant rs1906595, the major allele of which abolishes regulatory element activity.

In the mouse orthologue of the AF-associated variant region 170 kb upstream of Pitx2, a nontissue-specific regulatory element specifically contacts the promoters of Pitx2c as well as Enpep, a gene adjacent to Pitx2.56 Further analyses are required to establish whether both Pitx2c and Enpep are targets of variant regulatory elements in the upstream gene desert, and what could be mechanisms underlying the change in target gene expression.

In a recent study, the mouse orthologues of 2 variant noncoding regions in the gene desert upstream of Pitx2 were deleted from the mouse genome using CRISPR/Cas9 genome editing.53 One of these regions, a 20 kb region upstream of Pitx2 with regulatory activity, is in contact with the promoter of Pitx2 as shown by 4C-seq (Supplemental Table 2). Deletion of the regulatory element reduced expression of Pitx2c specifically in male mice and led to inducible AF susceptibility. Of note, regulatory element deletion caused increased Enpep expression only in females. Moreover, the regulatory element region was essential for and specific to maintaining the active chromatin state of the Pitx2c promoter specifically. Disruption of the Pitx2 intronic CTCF binding site caused reduced Pitx2 expression, AF predisposition, and reduced active chromatin marks on Pitx2 indicating that long-range looping was mediated by CTCF. However, the separate deletion of the 2 enhancer regions did not lead to major phenotypic abnormalities without stress, and the authors speculate that a pair-wise deletion could uncover that these 2 enhancers confer phenotypic robustness.53 Further research should uncover whether these 2 identified enhancers are both needed in collaboration to reduce Pitx2 expression (or other downstream mechanisms) to a degree sufficient for an arrhythmogenic phenotype. Despite these efforts, AF relevant target genes of the highest risk-associated region near 4q25 are still uncertain. Although there is no correlation between PITX2 expression changes and AF, most models suggest that regulatory elements present in AF risk variant loci are linked to PITX2 regulation.

A region near PRRX1, a transcriptional co-activator important for development of the heart,25 associated with Agnathia-Otocephaly and Dysgnathia Complex,166 also harbors a variant regulatory element. Putative enhancers were selected in the AF-associated region on 1q24 upstream of PRRX1 using conservation and genomic markers of transcriptional enhancers.47 Two of these putative enhancers showed strong eGFP expression in cardiac and skeletal muscle tissue when tested in embryonic zebrafish (Figure 4). Twenty-one common SNPs within these regulatory elements were tested for enhancer activity of the risk versus nonrisk allele in HL1 cells, demonstrating that only SNP rs577676 led to decreased regulatory element activity. Possible target genes in the variant region were identified using Hi-C and 3C, suggesting physical contact between the identified regulatory element and the promoter of PRRX1 in cardiac fibroblasts compared with human embryonic stem cells. This supports the idea that the risk variant negatively regulates the expression of PRRX1, providing a direct mechanism linking the risk variant and target gene for this locus. Additionally, optical mapping of cardiomyocytes differentiated from PRRX1 knockout human embryonic stem cells and Prrx1a morpholino-treated zebrafish embryos revealed the action potential duration was significantly shortened in PRRX1-knockout cardiomyocytes, a phenotype often seen in patients with AF.

The mouse orthologue of another AF-associated regulatory element was found regulating expression of the AF-relevant gene Gja1 in the heart.49Gja1 encodes the major gap junctional protein with a role in impulse propagation and cell-cell coupling.167,168 Using CRISPR/Cas9 genome editing, a region containing the mouse homologue of a 28 kb AF-associated locus 0.7 Mb downstream of GJA1 was deleted. Mice homozygous for the deletion showed atrial-specific decreased expression of Gja1. The same study found that deletion of the mouse orthologue of the variant region close to the potassium-calcium activated channel KCNN3 caused the reduced expression of several nearby genes, whereas deletion of the 33 kb orthologue of the variant region in the first intron of the zinc-finger transcription factor, ZFHX3 did not alter cardiac expression of any gene within the topologically associated domain in vivo.

Multiple regulatory elements were found around the SCN5A-SCN10A locus, which has frequently been associated with cardiac rhythm and conduction as identified through GWAS and with ECG parameters being linked to AF4,5,163,169–173 and Brugada syndrome.174 One of the regulatory elements of SCN5A is positioned in an intron of SCN10A163 and contains a common variant that is associated with PR interval.85 The variant disrupts a T-box binding site in the regulatory element and is associated with decreased SCN5A expression.162,163 Two other regulatory elements are located in introns and downstream of SCN5A, the latter of which also contains variants associated with PR interval and QRS duration.85,175–177 The regulatory element cluster (super enhancer) downstream of SCN5A is evolutionary conserved and drives gene expression in the heart in a pattern resembling that of Scn5a. When deleted from the mouse genome, the cluster, which harbors conduction parameter-associated SNPs, was found to be essential for Scn5a expression and for bringing the other regulatory elements and promoters in the locus physically together to regulate Scn5a.131

Epigenetic States Link Environment and Gene Regulation

There are several risk factors for cardiovascular disease, the most important of which is age. Interestingly, epigenetic alterations have been proposed to underlie age-related transcriptional alterations.178 Indeed, changes in epigenetic state induced by aging (or other risk factors such as hypertension and metabolic disease) could provide the basis for the uncovering of genetic risk factors at AF-associated loci. Moreover, combined GWAS loci only account for a portion of heritability, suggesting that a part of the so-called missing heritability might be found in nonsequence-related epigenetics.179 An example of the importance of changes in epigenetic state is the epigenetic clock. Chronological age is highly correlated with epigenetic alterations of the methylome,180 and deviation of this methylation state could be used to predict healthspan181 and is correlated with cardiovascular disease incidence.182 Altered DNA methylation was found near or on the promoters of poised genes, which contributed to transcriptional heterogeneity of these genes.183 A methylome-wide association study of an AF cohort revealed 14 AF-associated SNPs to be associated with a CpG site in blood-derived genomic DNA.184 In a smaller study, a CpG islet proximal to PITX2 was found to be hypermethylated in patients with AF compared with controls, along with decreased PITX2 expression in patients with AF.185 Furthermore, hypermethylation in the AF susceptible loci containing PITX2 and CCDC141, as well as hypomethylation in the locus containing CACNA1C locus, were identified in all 7 patients with permanent AF.186 These data suggest that changes in DNA methylation could be involved in AF.

Not only methylation but also the alteration of other epigenetic mechanisms such as histone modification and chromatin remodeling could contribute to disease risk.187 Transcription factors regulate or maintain cell fate by activating certain and repressing other target genes in cooperativity with chromatin remodelers and histone modifying enzymes. Chromatin remodelers respond to modifications to histone posttranslational modifications including methylation, phosphorylation, acetylation, ubiquitylation, and sumoylation.188,189 Recognizing these modifications, chromatin remodelers make distant regulatory elements as well as promoters accessible (with tissue-specificity) by regulating nucleosome dynamics.190 There is extensive cross-talk between histone modifications and chromatin remodelers, the balance of which determines the recruitment of transcription factors.191 Evidence of a role for both histone modifications and chromatin remodelers in complex trait diseases such as AF is emerging. For example, the interplay between transcription factors and chromatin remodeling complex dosage of Brg1 were shown to be important during development,192 which suggests a mechanism of interdependence between transcription factors and remodeling proteins in the heart. Additionally, histone deacetylases were found to modulate pathogenic gene expression in many diseases including AF progression.193 Moreover, there is evidence that increased expression of histone modifier enhancer of zeste homolog 2 (EZH2), which catalyzes the deposition of methylation to histone 3 at lysine 27 (H3K27me3) is associated with fibrosis in patients with AF.194

Another possible mechanism linking epigenetics and AF are reactive oxygen species (ROS) levels. Increased levels of ROS are known to accelerate aging, and they are associated with AF pathogenesis.195,196 It is known that ROS levels have an impact on the mechanisms which govern the epigenome, such as DNA methylation, histone modification, and noncoding RNAs.197 Increased ROS levels can disrupt the epigenetic balance and thus the epigenetic state of a cell, causing potentially altered mechanisms (such as uncovering of the effect of risk variants) leading to increased AF predisposition. Moreover, one study found that oxidative stress promotes AF via increased intracellular Ca2+ release by oxidized RyR2.198 In mice, ROS is increased in response to injury but also during the natural transition from glycolytic to oxidative metabolism shortly after birth as well as during aging. This increase in ROS inhibits cardiomyocyte regeneration.199 Indeed, Pitx2 is known to regulate the transcriptional response to oxidative stress (cooperatively with Yap).82 In response to injury, Pitx2 expression is induced, which in turn activates ROS scavengers, protecting cells from injury. Future models should provide insight into the interplay between ROS, PITX2, TBX5, and AF, but as of yet the interaction between these factors and the effect on effectors has not been studied.

In addition to the contribution of aging via ROS, fetal and developmental origins of disease may also be mediated by epigenetic responses to environmental factors.200 In a generalized view, heritable epigenetic states of chromatin are subject to developmental changes and are sensitive to internal variation (eg, variable transcription factor activity) and external signals (eg, metabolic state) during development. This leads to interindividual variability in regulatory element deployment, gene expression, and cellular and tissue phenotype. Indeed, the same could hold for AF, as there is a high density of AF-risk variants near cardiac developmental transcription factor genes such as NKX2-5, PITX2, TBX5, and TBX3 as well as their target genes such as ion channels (HCN4, KCNN3) and gap junctions (GJA1). Two of the genes, PITX2 and NKX2-5, well-known players in AF, are responsible for the correct development of the left atrial myocardium as well as the myocardial sleeves of the pulmonary veins.73 The myocardial sleeves of the pulmonary veins are composed of cardiomyocytes resembling working atrial myocardium, and they are the sites where ectopic foci that initiate AF are often found.201,202 It has been speculated that a developmental change in expression of these genes combined with other structural or epigenetic remodeling could ultimately lead to the development of ectopic foci in the pulmonary vein myocardial sleeves.203–205 Likewise, HCN4 encodes a potassium channel responsible for the generation of pacemaker current in the sinoatrial node during early development, as well as atrial rhythm.206,207 However, the strict requirement of HCN4 function is confined to embryonic development206–208 as it seems to be dispensable in adult heart rhythm.209 Indeed, AF is characterized by irregularly irregular heart rates, suggesting that developmental changes in expression of this gene could contribute to AF in later life.


AF is a collective name for an expressed disease phenotype with many distinct pathological mechanisms. GWAS and genetically modified animal models have indicated that AF predisposition likely involves many additive small expression changes induced by variant regulatory elements. While several parameters have been clearly associated with AF, including impulse conduction, action potential duration, intracellular calcium handling, and fibrosis, the origins of AF and the cell types involved are still not fully known. An additional confounding factor is that although many genetic components have been associated with AF, some heritable components remain unexplained. This missing heritability could be caused by epigenetics not identified by sequence-based GWAS. Indeed, there are developmental and aging-related epigenetic changes that could contribute to interindividual variability. This could result in differential regulatory element deployment and gene expression in a tissue- or developmental stage-specific manner.

Here, we conclude that there is growing evidence that a transcription factor network could modulate mechanisms underlying AF. Essential cardiac developmental transcription factors that are situated close to AF-associated variants have been linked to AF or factors that predispose to AF. Here, we propose a 3-fold model that can link genetic risk variants and transcription factor networks to AF predisposition. First, AF-associated genetic variation in binding sites of transcription factors can lead to altered expression of target effector genes (eg, ion channels). Second, genetic variants could interfere with transcriptional regulation of these AF relevant transcription factors themselves (Figure 1B). Third, transcription factors are now also known to interact as cofactors of each other in the regulation of target gene expression (Figure 3). Therefore, it is likely that a slight alteration in expression levels of any of these transcription factors—as a result of variant regulatory element activity—can alter the delicate balance in the transcriptional network, affecting its output, which results in altered expression of target effector genes and AF predisposition (Figure 3).

To uncover the contribution of epigenetic state to AF risk in relation to genetic variation. there is a pressing need for availability of datasets from relevant cell types and developmental stages. Additionally, we require knowledge of the effect of disease states such as high blood pressure, stress, and aging on these epigenetic parameters. This would allow the identification of variants with a biologically relevant impact on the system in the context of complex disease. Specifically, assays are required that subsequently test variant regulatory element function and their effect on target gene expression. Moreover, we lack the means to test the impact of combinations of different variant regulatory elements on target gene expression and the transcriptional network. Most importantly, there is an unmet need for the possibility to perform such techniques in relevant cell or tissue types. With the likely diverse causes of AF in different patients, a valuable addition to AF treatment would be to make patient-specific classifications of the disease based on genetic and epigenetic parameters.

Nonstandard Abbreviations and Acronyms


atrial fibrillation


assay for transposase accessible chromatin


chromatin immunoprecipitation sequencing


CCCTC-binding factor


Genome-wide association studies


sarco/endoplasmic reticulum Ca2+-ATPase


single nucleotide polymorphism


The Tables are available as Supplement with this article at

For Sources of Funding and Disclosures, see page 45.

Correspondence to: Vincent M. Christoffels, Department of Medical Biology, Amsterdam University Medical Centers, Meibergdreef 9 1105 AZ, Amsterdam 1105, the Netherlands. Email


  • 1. Fox CS, Parise H, D’Agostino RB, Lloyd-Jones DM, Vasan RS, Wang TJ, Levy D, Wolf PA, Benjamin EJ. Parental atrial fibrillation as a risk factor for atrial fibrillation in offspring.JAMA. 2004; 291:2851–2855. doi: 10.1001/jama.291.23.2851CrossrefMedlineGoogle Scholar
  • 2. Lubitz SA, Sinner MF, Lunetta KL, Makino S, Pfeufer A, Rahman R, Veltman CE, Barnard J, Bis JC, Danik SP, et al. Independent susceptibility markers for atrial fibrillation on chromosome 4q25.Circulation. 2010; 122:976–984. doi: 10.1161/CIRCULATIONAHA.109.886440LinkGoogle Scholar
  • 3. Nattel S, Dobrev D. Electrophysiological and molecular mechanisms of paroxysmal atrial fibrillation.Nat Rev Cardiol. 2016; 13:575–590. doi: 10.1038/nrcardio.2016.118CrossrefMedlineGoogle Scholar
  • 4. Roselli C, Chaffin MD, Weng LC, Aeschbacher S, Ahlberg G, Albert CM, Almgren P, Alonso A, Anderson CD, Aragam KG, et al. Multi-ethnic genome-wide association study for atrial fibrillation.Nat Genet. 2018; 50:1225–1233. doi: 10.1038/s41588-018-0133-9CrossrefMedlineGoogle Scholar
  • 5. Nielsen JB, Thorolfsdottir RB, Fritsche LG, Zhou W, Skov MW, Graham SE, Herron TJ, McCarthy S, Schmidt EM, Sveinbjornsson G, et al. Biobank-driven genomic discovery yields new insight into atrial fibrillation biology.Nat Genet. 2018; 50:1234–1239. doi: 10.1038/s41588-018-0171-3CrossrefMedlineGoogle Scholar
  • 6. Gutierrez A, Chung MK. Genomics of atrial fibrillation.Curr. Cardiol. Rep. 2016; 18:55.Google Scholar
  • 7. Kalstø SM, Siland JE, Rienstra M, Christophersen IE. Atrial fibrillation genetics update: toward clinical implementation.Front Cardiovasc Med. 2019; 6:127. doi: 10.3389/fcvm.2019.00127CrossrefMedlineGoogle Scholar
  • 8. Fabritz L, Guasch E, Antoniades C, Bardinet I, Benninger G, Betts TR, Brand E, Breithardt G, Bucklar-Suchankova G, Camm AJ, et al. Expert consensus document: Defining the major health modifiers causing atrial fibrillation: a roadmap to underpin personalized prevention and treatment.Nat Rev Cardiol. 2016; 13:230–237. doi: 10.1038/nrcardio.2015.194CrossrefMedlineGoogle Scholar
  • 9. Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, Natarajan P, Lander ES, Lubitz SA, Ellinor PT, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations.Nat Genet. 2018; 50:1219–1224. doi: 10.1038/s41588-018-0183-zCrossrefMedlineGoogle Scholar
  • 10. Schaub MA, Boyle AP, Kundaje A, Batzoglou S, Snyder M. Linking disease associations with regulatory information in the human genome.Genome Res. 2012; 22:1748–1759. doi: 10.1101/gr.136127.111CrossrefMedlineGoogle Scholar
  • 11. Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, Reynolds AP, Sandstrom R, Qu H, Brody J, et al. Systematic localization of common disease-associated variation in regulatory DNA.Science. 2012; 337:1190–1195. doi: 10.1126/science.1222794CrossrefMedlineGoogle Scholar
  • 12. Deplancke B, Alpern D, Gardeux V. The genetics of transcription factor DNA binding variation.Cell. 2016; 166:538–554. doi: 10.1016/j.cell.2016.07.012CrossrefMedlineGoogle Scholar
  • 13. Syeda F, Kirchhof P, Fabritz L. PITX2-dependent gene regulation in atrial fibrillation and rhythm control.J Physiol. 2017; 595:4019–4026. doi: 10.1113/JP273123CrossrefMedlineGoogle Scholar
  • 14. Yashiro K, Shiratori H, Hamada H. Haemodynamics determined by a genetic programme govern asymmetric development of the aortic arch.Nature. 2007; 450:285–288. doi: 10.1038/nature06254CrossrefMedlineGoogle Scholar
  • 15. Kääb S, Darbar D, van Noord C, Dupuis J, Pfeufer A, Newton-Cheh C, Schnabel R, Makino S, Sinner MF, Kannankeril PJ, et al. Large scale replication and meta-analysis of variants on chromosome 4q25 associated with atrial fibrillation.Eur Heart J. 2009; 30:813–819. doi: 10.1093/eurheartj/ehn578CrossrefMedlineGoogle Scholar
  • 16. Gudbjartsson DF, Arnar DO, Helgadottir A, Gretarsdottir S, Holm H, Sigurdsson A, Jonasdottir A, Baker A, Thorleifsson G, Kristjansson K, et al. Variants conferring risk of atrial fibrillation on chromosome 4q25.Nature. 2007; 448:353–357. doi: 10.1038/nature06007CrossrefMedlineGoogle Scholar
  • 17. Ellinor PT, Lunetta KL, Albert CM, Glazer NL, Ritchie MD, Smith AV, Arking DE, Müller-Nurasyid M, Krijthe BP, Lubitz SA, et al. Meta-analysis identifies six new susceptibility loci for atrial fibrillation.Nat Genet. 2012; 44:670–675. doi: 10.1038/ng.2261CrossrefMedlineGoogle Scholar
  • 18. Parsons MJ, Brancaccio M, Sethi S, Maywood ES, Satija R, Edwards JK, Jagannath A, Couch Y, Finelli MJ, Smyllie NJ, et al. The regulatory factor ZFHX3 modifies circadian function in SCN via an AT motif-driven axis.Cell. 2015; 162:607–621. doi: 10.1016/j.cell.2015.06.060CrossrefMedlineGoogle Scholar
  • 19. Berry FB, Miura Y, Mihara K, Kaspar P, Sakata N, Hashimoto-Tamaoki T, Tamaoki T. Positive and negative regulation of myogenic differentiation of C2C12 cells by isoforms of the multiple homeodomain zinc finger transcription factor ATBF1.J Biol Chem. 2001; 276:25057–25065. doi: 10.1074/jbc.M010378200CrossrefMedlineGoogle Scholar
  • 20. Sun X, Fu X, Li J, Xing C, Martin DW, Zhang HH, Chen Z, Dong JT. Heterozygous deletion of Atbf1 by the Cre-loxP system in mice causes preweaning mortality.Genesis. 2012; 50:819–827. doi: 10.1002/dvg.22041CrossrefMedlineGoogle Scholar
  • 21. Wilcox AG, Vizor L, Parsons MJ, Banks G, Nolan PM. Inducible Knockout of Mouse Zfhx3 emphasizes its key role in setting the pace and amplitude of the adult circadian clock.J Biol Rhythms. 2017; 32:433–443. doi: 10.1177/0748730417722631CrossrefMedlineGoogle Scholar
  • 22. Bruneau BG, Nemer G, Schmitt JP, Charron F, Robitaille L, Caron S, Conner DA, Gessler M, Nemer M, Seidman CE, et al. A murine model of Holt-Oram syndrome defines roles of the T-box transcription factor Tbx5 in cardiogenesis and disease.Cell. 2001; 106:709–721. doi: 10.1016/s0092-8674(01)00493-7CrossrefMedlineGoogle Scholar
  • 23. Postma AV, van de Meerakker JB, Mathijssen IB, Barnett P, Christoffels VM, Ilgun A, Lam J, Wilde AA, Lekanne Deprez RH, Moorman AF. A gain-of-function TBX5 mutation is associated with atypical Holt-Oram syndrome and paroxysmal atrial fibrillation.Circ Res. 2008; 102:1433–1442. doi: 10.1161/CIRCRESAHA.107.168294LinkGoogle Scholar
  • 24. Moskowitz IP, Pizard A, Patel VV, Bruneau BG, Kim JB, Kupershmidt S, Roden D, Berul CI, Seidman CE, Seidman JG. The T-Box transcription factor Tbx5 is required for the patterning and maturation of the murine cardiac conduction system.Development. 2004; 131:4107–4116. doi: 10.1242/dev.01265CrossrefMedlineGoogle Scholar
  • 25. Martin JF, Bradley A, Olson EN. The paired-like homeo box gene MHox is required for early events of skeletogenesis in multiple lineages.Genes Dev. 1995; 9:1237–1249. doi: 10.1101/gad.9.10.1237CrossrefMedlineGoogle Scholar
  • 26. Berge DT, Brouwer A, Korving J, Martin JF, Meijlink F. Prx1 and Prx2 in skeletogenesis: roles in the craniofacial region, inner ear and limbs.Development. 1998; 125:3831–3842.CrossrefMedlineGoogle Scholar
  • 27. Bergwerff M, Gittenberger-de Groot AC, Wisse LJ, DeRuiter MC, Wessels A, Martin JF, Olson EN, Kern MJ. Loss of function of the Prx1 and Prx2 homeobox genes alters architecture of the great elastic arteries and ductus arteriosus.Virchows Arch. 2000; 436:12–19. doi: 10.1007/pl00008193CrossrefMedlineGoogle Scholar
  • 28. Ihida-Stansbury K, McKean DM, Gebb SA, Martin JF, Stevens T, Nemenoff R, Akeson A, Vaughn J, Jones PL. Paired-related homeobox gene Prx1 is required for pulmonary vascular development.Circ Res. 2004; 94:1507–1514. doi: 10.1161/01.RES.0000130656.72424.20LinkGoogle Scholar
  • 29. Yu H, Xu JH, Song HM, Zhao L, Xu WJ, Wang J, Li RG, Xu L, Jiang WF, Qiu XB, et al. Mutational spectrum of the NKX2-5 gene in patients with lone atrial fibrillation.Int J Med Sci. 2014; 11:554–563. doi: 10.7150/ijms.8407CrossrefMedlineGoogle Scholar
  • 30. Nakashima Y, Yanez DA, Touma M, Nakano H, Jaroszewicz A, Jordan MC, Pellegrini M, Roos KP, Nakano A. Nkx2-5 suppresses the proliferation of atrial myocytes and conduction system.Circ Res. 2014; 114:1103–1113. doi: 10.1161/CIRCRESAHA.114.303219LinkGoogle Scholar
  • 31. Abou Hassan OK, Fahed AC, Batrawi M, Arabi M, Refaat MM, DePalma SR, Seidman JG, Seidman CE, Bitar FF, Nemer GM. NKX2-5 mutations in an inbred consanguineous population: genetic and phenotypic diversity.Sci Rep. 2015; 5:8848. doi: 10.1038/srep08848CrossrefMedlineGoogle Scholar
  • 32. Biben C, Weber R, Kesteven S, Stanley E, McDonald L, Elliott DA, Barnett L, Köentgen F, Robb L, Feneley M, et al. Cardiac septal and valvular dysmorphogenesis in mice heterozygous for mutations in the homeobox gene Nkx2-5.Circ Res. 2000; 87:888–895. doi: 10.1161/01.res.87.10.888LinkGoogle Scholar
  • 33. Saxon JG, Baer DR, Barton JA, Hawkins T, Wu B, Trusk TC, Harris SE, Zhou B, Mishina Y, Sugi Y. BMP2 expression in the endocardial lineage is required for AV endocardial cushion maturation and remodeling.Dev Biol. 2017; 430:113–128. doi: 10.1016/j.ydbio.2017.08.008CrossrefMedlineGoogle Scholar
  • 34. Wang J, Greene SB, Bonilla-Claudio M, Tao Y, Zhang J, Bai Y, Huang Z, Black BL, Wang F, Martin JF. Bmp signaling regulates myocardial differentiation from cardiac progenitors through a MicroRNA-mediated mechanism.Dev Cell. 2010; 19:903–912. doi: 10.1016/j.devcel.2010.10.022CrossrefMedlineGoogle Scholar
  • 35. Luna-Zurita L, Prados B, Grego-Bessa J, Luxán G, del Monte G, Benguría A, Adams RH, Pérez-Pomares JM, de la Pompa JL. Integration of a Notch-dependent mesenchymal gene program and Bmp2-driven cell invasiveness regulates murine cardiac valve formation.J Clin Invest. 2010; 120:3493–3507. doi: 10.1172/JCI42666CrossrefMedlineGoogle Scholar
  • 36. Goldman DC, Donley N, Christian JL. Genetic interaction between Bmp2 and Bmp4 reveals shared functions during multiple aspects of mouse organogenesis.Mech Dev. 2009; 126:117–127. doi: 10.1016/j.mod.2008.11.008CrossrefMedlineGoogle Scholar
  • 37. Ma L, Lu MF, Schwartz RJ, Martin JF. Bmp2 is essential for cardiac cushion epithelial-mesenchymal transition and myocardial patterning.Development. 2005; 132:5601–5611. doi: 10.1242/dev.02156CrossrefMedlineGoogle Scholar
  • 38. Maekawa T, Jin W, Ishii S. The role of ATF-2 family transcription factors in adipocyte differentiation: antiobesity effects of p38 inhibitors.Mol Cell Biol. 2010; 30:613–625. doi: 10.1128/MCB.00685-09CrossrefMedlineGoogle Scholar
  • 39. Qi L, Ding Y. Involvement of the CREB5 regulatory network in colorectal cancer metastasis.Yi Chuan. 2014; 36:679–684. doi: 10.3724/SP.J.1005.2014.0679CrossrefMedlineGoogle Scholar
  • 40. Srivastava D, Thomas T, Lin Q, Kirby ML, Brown D, Olson EN. Regulation of cardiac mesodermal and neural crest development by the bHLH transcription factor, dHAND.Nat Genet. 1997; 16:154–160. doi: 10.1038/ng0697-154CrossrefMedlineGoogle Scholar
  • 41. Liu N, Barbosa AC, Chapman SL, Bezprozvannaya S, Qi X, Richardson JA, Yanagisawa H, Olson EN. DNA binding-dependent and -independent functions of the Hand2 transcription factor during mouse embryogenesis.Development. 2009; 136:933–942. doi: 10.1242/dev.034025CrossrefMedlineGoogle Scholar
  • 42. Xia M, Luo W, Jin H, Yang Z. HAND2-mediated epithelial maintenance and integrity in cardiac outflow tract morphogenesis.Development. 2019; 146:dev177477.CrossrefMedlineGoogle Scholar
  • 43. Yamagishi H, Olson EN, Srivastava D. The basic helix-loop-helix transcription factor, dHAND, is required for vascular development.J Clin Invest. 2000; 105:261–270. doi: 10.1172/JCI8856CrossrefMedlineGoogle Scholar
  • 44. Bakker ML, Boukens BJ, Mommersteeg MT, Brons JF, Wakker V, Moorman AF, Christoffels VM. Transcription factor Tbx3 is required for the specification of the atrioventricular conduction system.Circ Res. 2008; 102:1340–1349. doi: 10.1161/CIRCRESAHA.107.169565LinkGoogle Scholar
  • 45. Mesbah K, Harrelson Z, Théveniau-Ruissy M, Papaioannou VE, Kelly RG. Tbx3 is required for outflow tract development.Circ Res. 2008; 103:743–750. doi: 10.1161/CIRCRESAHA.108.172858LinkGoogle Scholar
  • 46. Hoogaars WM, Engel A, Brons JF, Verkerk AO, de Lange FJ, Wong LY, Bakker ML, Clout DE, Wakker V, Barnett P, et al. Tbx3 controls the sinoatrial node gene program and imposes pacemaker function on the atria.Genes Dev. 2007; 21:1098–1112. doi: 10.1101/gad.416007CrossrefMedlineGoogle Scholar
  • 47. Tucker NR, Dolmatova EV, Lin H, Cooper RR, Ye J, Hucker WJ, Jameson HS, Parsons VA, Weng LC, Mills RW, et al. Diminished PRRX1 expression is associated with increased risk of atrial fibrillation and shortening of the cardiac action potential.Circ Cardiovasc Genet. 2017; 10:e001902.LinkGoogle Scholar
  • 48. Nadadur RD, Broman MT, Boukens B, Mazurek SR, Yang X, van den Boogaard M, Bekeny J, Gadek M, Ward T, Zhang M, et al. Pitx2 modulates a Tbx5-dependent gene regulatory network to maintain atrial rhythm.Sci Transl Med. 2016; 8:354ra115. doi: 10.1126/scitranslmed.aaf4891CrossrefMedlineGoogle Scholar
  • 49. van Ouwerkerk AF, Bosada FM, van Duijvenboden K, Hill MC, Montefiori LE, Scholman KT, Liu J, de Vries AAF, Boukens BJ, Ellinor PT, et al. Identification of atrial fibrillation associated genes and functional non-coding variants.Nat Commun. 2019; 10:4755. doi: 10.1038/s41467-019-12721-5CrossrefMedlineGoogle Scholar
  • 50. Dai W, Laforest B, Tyan L, Shen KM, Nadadur RD, Alvarado FJ, Mazurek SR, Lazarevic S, Gadek M, Wang Y, et al. A calcium transport mechanism for atrial fibrillation in Tbx5-mutant mice.Elife. 2019; 8:e41814.CrossrefMedlineGoogle Scholar
  • 51. Tao Y, Zhang M, Li L, Bai Y, Zhou Y, Moon AM, Kaminski HJ, Martin JF. Pitx2, an atrial fibrillation predisposition gene, directly regulates ion transport and intercalated disc genes.Circ Cardiovasc Genet. 2014; 7:23–32. doi: 10.1161/CIRCGENETICS.113.000259LinkGoogle Scholar
  • 52. Laforest B, Dai W, Tyan L, Lazarevic S, Shen KM, Gadek M, Broman MT, Weber CR, Moskowitz IP. Atrial fibrillation risk loci interact to modulate Ca2+-dependent atrial rhythm homeostasis.J Clin Invest. 2019; 129:4937–4950. doi: 10.1172/JCI124231CrossrefMedlineGoogle Scholar
  • 53. Zhang M, Hill MC, Kadow ZA, Suh JH, Tucker NR, Hall AW, Tran TT, Swinton PS, Leach JP, Margulies KB, et al. Long-range Pitx2c enhancer–promoter interactions prevent predisposition to atrial fibrillation.Proc Natl Acad Sci USA. 2019; 116:22692–22698.CrossrefMedlineGoogle Scholar
  • 54. Collins MM, Ahlberg G, Hansen CV, Guenther S, Marín-Juez R, Sokol AM, El-Sammak H, Piesker J, Hellsten Y, Olesen MS, et al. Early sarcomere and metabolic defects in a zebrafish pitx2c cardiac arrhythmia model.Proc Natl Acad Sci USA. 2019; 116:24115–24121. doi: 10.1073/pnas.1913905116CrossrefMedlineGoogle Scholar
  • 55. Syeda F, Holmes AP, Yu TY, Tull S, Kuhlmann SM, Pavlovic D, Betney D, Riley G, Kucera JP, Jousset F, et al. PITX2 modulates atrial membrane potential and the antiarrhythmic effects of sodium-channel blockers.J Am Coll Cardiol. 2016; 68:1881–1894. doi: 10.1016/j.jacc.2016.07.766CrossrefMedlineGoogle Scholar
  • 56. Aguirre LA, Alonso ME, Badía-Careaga C, Rollán I, Arias C, Fernández-Miñán A, López-Jiménez E, Aránega A, Gómez-Skarmeta JL, Franco D, et al. Long-range regulatory interactions at the 4q25 atrial fibrillation risk locus involve PITX2c and ENPEP.BMC Biol. 2015; 13:26. doi: 10.1186/s12915-015-0138-0CrossrefMedlineGoogle Scholar
  • 57. Franco D, Christoffels VM, Campione M. Homeobox transcription factor Pitx2: The rise of an asymmetry gene in cardiogenesis and arrhythmogenesis.Trends Cardiovasc Med. 2014; 24:23–31. doi: 10.1016/j.tcm.2013.06.001CrossrefMedlineGoogle Scholar
  • 58. Furtado MB, Wilmanns JC, Chandran A, Tonta M, Biben C, Eichenlaub M, Coleman HA, Berger S, Bouveret R, Singh R, et al. A novel conditional mouse model for Nkx2-5 reveals transcriptional regulation of cardiac ion channels.Differentiation. 2016; 91:29–41. doi: 10.1016/j.diff.2015.12.003CrossrefMedlineGoogle Scholar
  • 59. Posch MG, Boldt LH, Polotzki M, Richter S, Rolf S, Perrot A, Dietz R, Ozcelik C, Haverkamp W. Mutations in the cardiac transcription factor GATA4 in patients with lone atrial fibrillation.Eur J Med Genet. 2010; 53:201–203. doi: 10.1016/j.ejmg.2010.03.008CrossrefMedlineGoogle Scholar
  • 60. Bapat A, Anderson CD, Ellinor PT, Lubitz SA. Genomic basis of atrial fibrillation.Heart. 2018; 104:201–206. doi: 10.1136/heartjnl-2016-311027CrossrefMedlineGoogle Scholar
  • 61. Fatkin D, Santiago CF, Huttner IG, Lubitz SA, Ellinor PT. Genetics of atrial fibrillation: state of the art in 2017.Heart Lung Circ. 2017; 26:894–901. doi: 10.1016/j.hlc.2017.04.008CrossrefMedlineGoogle Scholar
  • 62. Christophersen IE, Ellinor PT. Genetics of atrial fibrillation: from families to genomes.J Hum Genet. 2016; 61:61–70. doi: 10.1038/jhg.2015.44CrossrefMedlineGoogle Scholar
  • 63. Tucker NR, Ellinor PT. Emerging directions in the genetics of atrial fibrillation.Circ Res. 2014; 114:1469–1482. doi: 10.1161/CIRCRESAHA.114.302225LinkGoogle Scholar
  • 64. Tucker NR, Clauss S, Ellinor PT. Common variation in atrial fibrillation: navigating the path from genetic association to mechanism.Cardiovasc Res. 2016; 109:493–501. doi: 10.1093/cvr/cvv283CrossrefMedlineGoogle Scholar
  • 65. Lambert SA, Jolma A, Campitelli LF, Das PK, Yin Y, Albu M, Chen X, Taipale J, Hughes TR, Weirauch MT. The human transcription factors.Cell. 2018; 172:650–665. doi: 10.1016/j.cell.2018.01.029CrossrefMedlineGoogle Scholar
  • 66. Bruneau BG. The developmental genetics of congenital heart disease.Nature. 2008; 451:943–948. doi: 10.1038/nature06801CrossrefMedlineGoogle Scholar
  • 67. Seifi M, Walter MA. Axenfeld-Rieger syndrome.Clin Genet. 2018; 93:1123–1130. doi: 10.1111/cge.13148CrossrefMedlineGoogle Scholar
  • 68. Dasouki M, Andrews B, Parimi P, Kamnasaran D. Recurrent agnathia-otocephaly caused by DNA replication slippage in PRRX1.Am J Med Genet A. 2013; 161A:803–808. doi: 10.1002/ajmg.a.35879CrossrefMedlineGoogle Scholar
  • 69. Wang J, Klysik E, Sood S, Johnson RL, Wehrens XH, Martin JF. Pitx2 prevents susceptibility to atrial arrhythmias by inhibiting left-sided pacemaker specification.Proc Natl Acad Sci USA. 2010; 107:9753–9758. doi: 10.1073/pnas.0912585107CrossrefMedlineGoogle Scholar
  • 70. Kirchhof P, Kahr PC, Kaese S, Piccini I, Vokshi I, Scheld HH, Rotering H, Fortmueller L, Laakmann S, Verheule 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; 4:123–133. doi: 10.1161/CIRCGENETICS.110.958058LinkGoogle Scholar
  • 71. Chinchilla A, Daimi H, Lozano-Velasco E, Dominguez JN, Caballero R, Delpón E, Tamargo J, Cinca J, Hove-Madsen L, Aranega AE, et al. PITX2 insufficiency leads to atrial electrical and structural remodeling linked to arrhythmogenesis.Circ Cardiovasc Genet. 2011; 4:269–279. doi: 10.1161/CIRCGENETICS.110.958116LinkGoogle Scholar
  • 72. Liu C, Liu W, Lu MF, Brown NA, Martin JF. Regulation of left-right asymmetry by thresholds of Pitx2c activity.Development. 2001; 128:2039–2048.CrossrefMedlineGoogle Scholar
  • 73. Mommersteeg MT, Brown NA, Prall OW, de Gier-de Vries C, Harvey RP, Moorman AF, Christoffels VM. Pitx2c and Nkx2-5 are required for the formation and identity of the pulmonary myocardium.Circ Res. 2007; 101:902–909. doi: 10.1161/CIRCRESAHA.107.161182LinkGoogle Scholar
  • 74. Campione M, Steinbeisser H, Schweickert A, Deissler K, van Bebber F, Lowe LA, Nowotschin S, Viebahn C, Haffter P, Kuehn MR, et al. The homeobox gene Pitx2: mediator of asymmetric left-right signaling in vertebrate heart and gut looping.Development. 1999; 126:1225–1234.CrossrefMedlineGoogle Scholar
  • 75. Liu C, Liu W, Palie J, Lu MF, Brown NA, Martin JF. Pitx2c patterns anterior myocardium and aortic arch vessels and is required for local cell movement into atrioventricular cushions.Development. 2002; 129:5081–5091.CrossrefMedlineGoogle Scholar
  • 76. Hill MC, Kadow ZA, Li L, Tran TT, Wythe JD, Martin JF. A cellular atlas of Pitx2-dependent cardiac development.Development. 2019; 146:dev180398.CrossrefMedlineGoogle Scholar
  • 77. Lu MF, Pressman C, Dyer R, Johnson RL, Martin JF. Function of Rieger syndrome gene in left-right asymmetry and craniofacial development.Nature. 1999; 401:276–278. doi: 10.1038/45797CrossrefMedlineGoogle Scholar
  • 78. Kahr PC, Piccini I, Fabritz L, Greber B, Schöler H, Scheld HH, Hoffmeier A, Brown NA, Kirchhof P. Systematic analysis of gene expression differences between left and right atria in different mouse strains and in human atrial tissue.PLoS One. 2011; 6:e26389. doi: 10.1371/journal.pone.0026389CrossrefMedlineGoogle Scholar
  • 79. Wang J, Bai Y, Li N, Ye W, Zhang M, Greene SB, Tao Y, Chen Y, Wehrens XH, Martin JF. Pitx2-microRNA pathway that delimits sinoatrial node development and inhibits predisposition to atrial fibrillation.Proc Natl Acad Sci U S A. 2014; 111:9181–9186. doi: 10.1073/pnas.1405411111CrossrefMedlineGoogle Scholar
  • 80. Lozano-Velasco E, Hernández-Torres F, Daimi H, Serra SA, Herraiz A, Hove-Madsen L, Aránega A, Franco D. Pitx2 impairs calcium handling in a dose-dependent manner by modulating Wnt signalling.Cardiovasc Res. 2016; 109:55–66. doi: 10.1093/cvr/cvv207CrossrefMedlineGoogle Scholar
  • 81. Li L, Tao G, Hill MC, Zhang M, Morikawa Y, Martin JF. Pitx2 maintains mitochondrial function during regeneration to prevent myocardial fat deposition.Development. 2018; 145:dev168609.CrossrefMedlineGoogle Scholar
  • 82. Tao G, Kahr PC, Morikawa Y, Zhang M, Rahmani M, Heallen TR, Li L, Sun Z, Olson EN, Amendt BA, et al. Pitx2 promotes heart repair by activating the antioxidant response after cardiac injury.Nature. 2016; 534:119–123. doi: 10.1038/nature17959CrossrefMedlineGoogle Scholar
  • 83. Li QY, Newbury-Ecob RA, Terrett JA, Wilson DI, Curtis AR, Yi CH, Gebuhr T, Bullen PJ, Robson SC, Strachan T, et al. Holt-Oram syndrome is caused by mutations in TBX5, a member of the Brachyury (T) gene family.Nat Genet. 1997; 15:21–29. doi: 10.1038/ng0197-21CrossrefMedlineGoogle Scholar
  • 84. Basson CT, Bachinsky DR, Lin RC, Levi T, Elkins JA, Soults J, Grayzel D, Kroumpouzou E, Traill TA, Leblanc-Straceski J, et al. Mutations in human TBX5 [corrected] cause limb and cardiac malformation in Holt-Oram syndrome.Nat Genet. 1997; 15:30–35. doi: 10.1038/ng0197-30CrossrefMedlineGoogle Scholar
  • 85. van Setten J, Brody JA, Jamshidi Y, Swenson BR, Butler AM, Campbell H, Del Greco FM, Evans DS, Gibson Q, Gudbjartsson DF, et al. PR interval genome-wide association meta-analysis identifies 50 loci associated with atrial and atrioventricular electrical activity.Nat Commun. 2018; 9:2904. doi: 10.1038/s41467-018-04766-9CrossrefMedlineGoogle Scholar
  • 86. Riley G, Syeda F, Kirchhof P, Fabritz L. An introduction to murine models of atrial fibrillation.Front Physiol. 2012; 3:296. doi: 10.3389/fphys.2012.00296CrossrefMedlineGoogle Scholar
  • 87. Yang XH, Nadadur RD, Hilvering CR, Bianchi V, Werner M, Mazurek SR, Gadek M, Shen KM, Goldman JA, Tyan L, et al. Transcription-factor-dependent enhancer transcription defines a gene regulatory network for cardiac rhythm.Elife. 2017; 6:e31683.CrossrefMedlineGoogle Scholar
  • 88. Luna-Zurita L, Stirnimann CU, Glatt S, Kaynak BL, Thomas S, Baudin F, Samee MA, He D, Small EM, Mileikovsky M, et al. Complex interdependence regulates heterotypic transcription factor distribution and coordinates cardiogenesis.Cell. 2016; 164:999–1014. doi: 10.1016/j.cell.2016.01.004CrossrefMedlineGoogle Scholar
  • 89. Ang YS, Rivas RN, Ribeiro AJS, Srivas R, Rivera J, Stone NR, Pratt K, Mohamed TMA, Fu JD, Spencer CI, et al. Disease model of GATA4 mutation reveals transcription factor cooperativity in human cardiogenesis.Cell. 2016; 167:1734–1749.e22. doi: 10.1016/j.cell.2016.11.033CrossrefMedlineGoogle Scholar
  • 90. Cavalli G, Heard E. Advances in epigenetics link genetics to the environment and disease.Nature. 2019; 571:489–499. doi: 10.1038/s41586-019-1411-0CrossrefMedlineGoogle Scholar
  • 91. Duygu B, Poels EM, da Costa Martins PA. Genetics and epigenetics of arrhythmia and heart failure.Front Genet. 2013; 4:219. doi: 10.3389/fgene.2013.00219CrossrefMedlineGoogle Scholar
  • 92. Tao H, Shi KH, Yang JJ, Li J. Epigenetic mechanisms in atrial fibrillation: New insights and future directions.Trends Cardiovasc Med. 2016; 26:306–318. doi: 10.1016/j.tcm.2015.08.006CrossrefMedlineGoogle Scholar
  • 93. Komal S, Yin JJ, Wang SH, Huang CZ, Tao HL, Dong JZ, Han SN, Zhang LR. MicroRNAs: Emerging biomarkers for atrial fibrillation.J Cardiol. 2019; 74:475–482. doi: 10.1016/j.jjcc.2019.05.018CrossrefMedlineGoogle Scholar
  • 94. Zhang H, Liu L, Hu J, Song L. MicroRNA Regulatory Network Revealing the Mechanism of Inflammation in Atrial Fibrillation.Med Sci Monit. 2015; 21:3505–3513. doi: 10.12659/msm.895982CrossrefMedlineGoogle Scholar
  • 95. Jiang S, Guo C, Zhang W, Che W, Zhang J, Zhuang S, Wang Y, Zhang Y, Liu B. The integrative regulatory network of circRNA, microRNA, and mRNA in atrial fibrillation.Front Genet. 2019; 10:526.CrossrefMedlineGoogle Scholar
  • 96. Briasoulis A, Inampudi C, Akintoye E, Alvarez P, Panaich S, Vaughan-Sarrazin M. Safety and efficacy of novel oral anticoagulants versus warfarin in medicare beneficiaries with atrial fibrillation and valvular heart disease.J Am Heart Assoc. 2018; 7:e008773.LinkGoogle Scholar
  • 97. Long HK, Prescott SL, Wysocka J. Ever-changing landscapes: transcriptional enhancers in development and evolution.Cell. 2016; 167:1170–1187. doi: 10.1016/j.cell.2016.09.018CrossrefMedlineGoogle Scholar
  • 98. Osterwalder M, Barozzi I, Tissières V, Fukuda-Yuzawa Y, Mannion BJ, Afzal SY, Lee EA, Zhu Y, Plajzer-Frick I, Pickle CS, et al. Enhancer redundancy provides phenotypic robustness in mammalian development.Nature. 2018; 554:239–243. doi: 10.1038/nature25461CrossrefMedlineGoogle Scholar
  • 99. Arnold CD, Gerlach D, Stelzer C, Boryń ŁM, Rath M, Stark A. Genome-wide quantitative enhancer activity maps identified by STARR-seq.Science. 2013; 339:1074–1077. doi: 10.1126/science.1232542CrossrefMedlineGoogle Scholar
  • 100. Vanhille L, Griffon A, Maqbool MA, Zacarias-Cabeza J, Dao LT, Fernandez N, Ballester B, Andrau JC, Spicuglia S. High-throughput and quantitative assessment of enhancer activity in mammals by CapStarr-seq.Nat Commun. 2015; 6:6905. doi: 10.1038/ncomms7905CrossrefMedlineGoogle Scholar
  • 101. Vockley CM, D’Ippolito AM, McDowell IC, Majoros WH, Safi A, Song L, Crawford GE, Reddy TE. Direct GR binding sites potentiate clusters of TF binding across the human genome.Cell. 2016; 166:1269–1281.e19. doi: 10.1016/j.cell.2016.07.049CrossrefMedlineGoogle Scholar
  • 102. van Arensbergen J, Pagie L, FitzPatrick VD, de Haas M, Baltissen MP, Comoglio F, van der Weide RH, Teunissen H, Võsa U, Franke L, et al. High-throughput identification of human SNPs affecting regulatory element activity.Nat Genet. 2019; 51:1160–1169. doi: 10.1038/s41588-019-0455-2CrossrefMedlineGoogle Scholar
  • 103. Tewhey R, Kotliar D, Park DS, Liu B, Winnicki S, Reilly SK, Andersen KG, Mikkelsen TS, Lander ES, Schaffner SF, et al. Direct identification of hundreds of expression-modulating variants using a multiplexed reporter assay.Cell. 2016; 165:1519–1529. doi: 10.1016/j.cell.2016.04.027CrossrefMedlineGoogle Scholar
  • 104. Kheradpour P, Ernst J, Melnikov A, Rogov P, Wang L, Zhang X, Alston J, Mikkelsen TS, Kellis M. Systematic dissection of regulatory motifs in 2000 predicted human enhancers using a massively parallel reporter assay.Genome Res. 2013; 23:800–811. doi: 10.1101/gr.144899.112CrossrefMedlineGoogle Scholar
  • 105. Kinney JB, Murugan A, Callan CG, Cox EC. Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence.Proc Natl Acad Sci U S A. 2010; 107:9158–9163. doi: 10.1073/pnas.1004290107CrossrefMedlineGoogle Scholar
  • 106. Melnikov A, Murugan A, Zhang X, Tesileanu T, Wang L, Rogov P, Feizi S, Gnirke A, Callan CG, Kinney JB, et al. Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay.Nat Biotechnol. 2012; 30:271–277. doi: 10.1038/nbt.2137CrossrefMedlineGoogle Scholar
  • 107. Gasperini M, Hill AJ, McFaline-Figueroa JL, Martin B, Kim S, Zhang MD, Jackson D, Leith A, Schreiber J, Noble WS, et al. A genome-wide framework for mapping gene regulation via cellular genetic screens.Cell. 2019; 176:1516. doi: 10.1016/j.cell.2019.02.027CrossrefMedlineGoogle Scholar
  • 108. Fei T, Li W, Peng J, Xiao T, Chen CH, Wu A, Huang J, Zang C, Liu XS, Brown M. Deciphering essential cistromes using genome-wide CRISPR screens.Proc Natl Acad Sci U S A. 2019; 116:25186–25195. doi: 10.1073/pnas.1908155116CrossrefMedlineGoogle Scholar
  • 109. Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position.Nat Methods. 2013; 10:1213–1218. doi: 10.1038/nmeth.2688CrossrefMedlineGoogle Scholar
  • 110. Robertson G, Hirst M, Bainbridge M, Bilenky M, Zhao Y, Zeng T, Euskirchen G, Bernier B, Varhol R, Delaney A, et al. Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing.Nat Methods. 2007; 4:651–657. doi: 10.1038/nmeth1068CrossrefMedlineGoogle Scholar
  • 111. Johnson DS, Mortazavi A, Myers RM, Wold B. Genome-wide mapping of in vivo protein-DNA interactions.Science. 2007; 316:1497–1502. doi: 10.1126/science.1141319CrossrefMedlineGoogle Scholar
  • 112. Kim TK, Hemberg M, Gray JM, Costa AM, Bear DM, Wu J, Harmin DA, Laptewicz M, Barbara-Haley K, Kuersten S, et al. Widespread transcription at neuronal activity-regulated enhancers.Nature. 2010; 465:182–187. doi: 10.1038/nature09033CrossrefMedlineGoogle Scholar
  • 113. Wu H, Nord AS, Akiyama JA, Shoukry M, Afzal V, Rubin EM, Pennacchio LA, Visel A. Tissue-specific RNA expression marks distant-acting developmental enhancers.PLoS Genet. 2014; 10:e1004610. doi: 10.1371/journal.pgen.1004610CrossrefMedlineGoogle Scholar
  • 114. Han J, Zhang J, Chen L, Shen B, Zhou J, Hu B, Du Y, Tate PH, Huang X, Zhang W. Efficient in vivo deletion of a large imprinted lncRNA by CRISPR/Cas9.RNA Biol. 2014; 11:829–835. doi: 10.4161/rna.29624CrossrefMedlineGoogle Scholar
  • 115. Skene PJ, Henikoff S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites.Elife. 2017; 6:e21856.CrossrefMedlineGoogle Scholar
  • 116. Tolhuis B, Palstra RJ, Splinter E, Grosveld F, de Laat W. Looping and interaction between hypersensitive sites in the active beta-globin locus.Mol Cell. 2002; 10:1453–1465. doi: 10.1016/s1097-2765(02)00781-5CrossrefMedlineGoogle Scholar
  • 117. Dekker J, Rippe K, Dekker M, Kleckner N. Capturing chromosome conformation.Science. 2002; 295:1306–1311. doi: 10.1126/science.1067799CrossrefMedlineGoogle Scholar
  • 118. Simonis M, Klous P, Splinter E, Moshkin Y, Willemsen R, de Wit E, van Steensel B, de Laat W. Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture-on-chip (4C).Nat Genet. 2006; 38:1348–1354. doi: 10.1038/ng1896CrossrefMedlineGoogle Scholar
  • 119. Van De Werken HJG, Landan G, Holwerda SJB, Hoichman M, Klous P, Chachik R, Splinter E, Valdes-Quezada C, Öz Y, Bouwman BAM, et al. Robust 4C-seq data analysis to screen for regulatory DNA interactions.Nat Methods. 2012; 9:969–972.CrossrefMedlineGoogle Scholar
  • 120. Schwartzman O, Mukamel Z, Oded-Elkayam N, Olivares-Chauvet P, Lubling Y, Landan G, Izraeli S, Tanay A. UMI-4C for quantitative and targeted chromosomal contact profiling.Nat Methods. 2016; 13:685–691. doi: 10.1038/nmeth.3922CrossrefMedlineGoogle Scholar
  • 121. Dostie J, Dekker J. Mapping networks of physical interactions between genomic elements using 5C technology.Nat Protoc. 2007; 2:988–1002. doi: 10.1038/nprot.2007.116CrossrefMedlineGoogle Scholar
  • 122. Belton JM, McCord RP, Gibcus JH, Naumova N, Zhan Y, Dekker J. Hi-C: a comprehensive technique to capture the conformation of genomes.Methods. 2012; 58:268–276. doi: 10.1016/j.ymeth.2012.05.001CrossrefMedlineGoogle Scholar
  • 123. Schoenfelder S, Javierre BM, Furlan-Magaril M, Wingett SW, Fraser P. Promoter capture Hi-C: High-resolution, genome-wide profiling of promoter interactions.J Vis Exp. 2018; 2018.Google Scholar
  • 124. Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A, Amit I, Lajoie BR, Sabo PJ, Dorschner MO, et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome.Science. 2009; 326:289–293. doi: 10.1126/science.1181369CrossrefMedlineGoogle Scholar
  • 125. Quinodoz SA, Ollikainen N, Tabak B, Palla A, Schmidt JM, Detmar E, Lai MM, Shishkin AA, Bhat P, Takei Y, et al. Higher-order inter-chromosomal hubs shape 3d genome organization in the nucleus.Cell. 2018; 174:744–757.e24. doi: 10.1016/j.cell.2018.05.024CrossrefMedlineGoogle Scholar
  • 126. Davies JO, Telenius JM, McGowan SJ, Roberts NA, Taylor S, Higgs DR, Hughes JR. Multiplexed analysis of chromosome conformation at vastly improved sensitivity.Nat Methods. 2016; 13:74–80. doi: 10.1038/nmeth.3664CrossrefMedlineGoogle Scholar
  • 127. Hughes JR, Roberts N, McGowan S, Hay D, Giannoulatou E, Lynch M, De Gobbi M, Taylor S, Gibbons R, Higgs DR. Analysis of hundreds of cis-regulatory landscapes at high resolution in a single, high-throughput experiment.Nat Genet. 2014; 46:205–212. doi: 10.1038/ng.2871CrossrefMedlineGoogle Scholar
  • 128. Schoenfelder S, Sugar R, Dimond A, Javierre BM, Armstrong H, Mifsud B, Dimitrova E, Matheson L, Tavares-Cadete F, Furlan-Magaril M, et al. Polycomb repressive complex PRC1 spatially constrains the mouse embryonic stem cell genome.Nat Genet. 2015; 47:1179–1186. doi: 10.1038/ng.3393CrossrefMedlineGoogle Scholar
  • 129. Villar D, Berthelot C, Aldridge S, Rayner TF, Lukk M, Pignatelli M, Park TJ, Deaville R, Erichsen JT, Jasinska AJ, et al. Enhancer evolution across 20 mammalian species.Cell. 2015; 160:554–566. doi: 10.1016/j.cell.2015.01.006CrossrefMedlineGoogle Scholar
  • 130. Flores MA, Ovcharenko I. Enhancer reprogramming in mammalian genomes.BMC Bioinformatics. 2018; 19:316. doi: 10.1186/s12859-018-2343-7CrossrefMedlineGoogle Scholar
  • 131. Man JCK, Mohan RA, Boogaard MVD, Hilvering CRE, Jenkins C, Wakker V, Bianchi V, Laat W, Barnett P, Boukens BJ, et al. An enhancer cluster controls gene activity and topology of the SCN5A-SCN10A locus in vivo.Nat Commun. 2019; 10:4943. doi: 10.1038/s41467-019-12856-5CrossrefMedlineGoogle Scholar
  • 132. Yen A, Kheradpour P, Zhang Z, Heravi-moussavi A, Liu Y, Amin V, Ziller MJ, Whitaker JW, Schultz MD, Sandstrom RS, et al. Integrative analysis of 111 reference human epigenomes.Nature. 2015; 518:317–330.CrossrefMedlineGoogle Scholar
  • 133. Wang D, Garcia-Bassets I, Benner C, Li W, Su X, Zhou Y, Qiu J, Liu W, Kaikkonen MU, Ohgi KA, et al. Reprogramming transcription by distinct classes of enhancers functionally defined by eRNA.Nature. 2011; 474:390–394. doi: 10.1038/nature10006CrossrefMedlineGoogle Scholar
  • 134. Kaikkonen MU, Adelman K. Emerging roles of non-coding RNA transcription.Trends Biochem Sci. 2018; 43:654–667. doi: 10.1016/j.tibs.2018.06.002CrossrefMedlineGoogle Scholar
  • 135. Henriques T, Scruggs BS, Inouye MO, Muse GW, Williams LH, Burkholder AB, Lavender CA, Fargo DC, Adelman K. Widespread transcriptional pausing and elongation control at enhancers.Genes Dev. 2018; 32:26–41. doi: 10.1101/gad.309351.117CrossrefMedlineGoogle Scholar
  • 136. Berthelot C, Villar D, Horvath JE, Odom DT, Flicek P. Complexity and conservation of regulatory landscapes underlie evolutionary resilience of mammalian gene expression.Nat Ecol Evol. 2018; 2:152–163. doi: 10.1038/s41559-017-0377-2CrossrefMedlineGoogle Scholar
  • 137. Mifsud B, Tavares-Cadete F, Young AN, Sugar R, Schoenfelder S, Ferreira L, Wingett SW, Andrews S, Grey W, Ewels PA, et al. Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C.Nat Genet. 2015; 47:598–606. doi: 10.1038/ng.3286CrossrefMedlineGoogle Scholar
  • 138. de Laat W, Duboule D. Topology of mammalian developmental enhancers and their regulatory landscapes.Nature. 2013; 502:499–506. doi: 10.1038/nature12753CrossrefMedlineGoogle Scholar
  • 139. Deng W, Lee J, Wang H, Miller J, Reik A, Gregory PD, Dean A, Blobel GA. Controlling long-range genomic interactions at a native locus by targeted tethering of a looping factor.Cell. 2012; 149:1233–1244. doi: 10.1016/j.cell.2012.03.051CrossrefMedlineGoogle Scholar
  • 140. Zabidi MA, Stark A. Regulatory enhancer-core-promoter communication via transcription factors and cofactors.Trends Genet. 2016; 32:801–814. doi: 10.1016/j.tig.2016.10.003CrossrefMedlineGoogle Scholar
  • 141. Heintzman ND, Hon GC, Hawkins RD, Kheradpour P, Stark A, Harp LF, Ye Z, Lee LK, Stuart RK, Ching CW, et al. Histone modifications at human enhancers reflect global cell-type-specific gene expression.Nature. 2009; 459:108–112. doi: 10.1038/nature07829CrossrefMedlineGoogle Scholar
  • 142. Nord AS, Blow MJ, Attanasio C, Akiyama JA, Holt A, Hosseini R, Phouanenavong S, Plajzer-Frick I, Shoukry M, Afzal V, et al. Rapid and pervasive changes in genome-wide enhancer usage during mammalian development.Cell. 2013; 155:1521–1531. doi: 10.1016/j.cell.2013.11.033CrossrefMedlineGoogle Scholar
  • 143. Engel KL, Mackiewicz M, Hardigan AA, Myers RM, Savic D. Decoding transcriptional enhancers: evolving from annotation to functional interpretation.Semin Cell Dev Biol. 2016; 57:40–50. doi: 10.1016/j.semcdb.2016.05.014CrossrefMedlineGoogle Scholar
  • 144. Fulco CP, Nasser J, Jones TR, Munson G, Bergman DT, Subramanian V, Grossman SR, Anyoha R, Patwardhan TA, Nguyen TH, et al. Activity-by-Contact model of enhancer specificity from thousands of CRISPR perturbations (CRISPRi FlowFISH).bioRxiv [Internet]. 2019; 51:529990. Available at: Scholar
  • 145. May D, Blow MJ, Kaplan T, McCulley DJ, Jensen BC, Akiyama JA, Holt A, Plajzer-Frick I, Shoukry M, Wright C, et al. Large-scale discovery of enhancers from human heart tissue.Nat Genet. 2011; 44:89–93. doi: 10.1038/ng.1006CrossrefMedlineGoogle Scholar
  • 146. Narlikar L, Sakabe NJ, Blanski AA, Arimura FE, Westlund JM, Nobrega MA, Ovcharenko I. Genome-wide discovery of human heart enhancers.Genome Res. 2010; 20:381–392. doi: 10.1101/gr.098657.109CrossrefMedlineGoogle Scholar
  • 147. Dickel DE, Barozzi I, Zhu Y, Fukuda-Yuzawa Y, Osterwalder M, Mannion BJ, May D, Spurrell CH, Plajzer-Frick I, Pickle CS, et al. Genome-wide compendium and functional assessment of in vivo heart enhancers.Nat Commun. 2016; 7:12923. doi: 10.1038/ncomms12923CrossrefMedlineGoogle Scholar
  • 148. van Duijvenboden K, de Boer BA, Capon N, Ruijter JM, Christoffels VM. EMERGE: a flexible modelling framework to predict genomic regulatory elements from genomic signatures.Nucleic Acids Res. 2016; 44:e42. doi: 10.1093/nar/gkv1144CrossrefMedlineGoogle Scholar
  • 149. Visel A, Minovitsky S, Dubchak I, Pennacchio LA. VISTA Enhancer Browser–a database of tissue-specific human enhancers.Nucleic Acids Res. 2007; 35:D88–D92. doi: 10.1093/nar/gkl822CrossrefMedlineGoogle Scholar
  • 150. Montefiori LE, Sobreira DR, Sakabe NJ, Aneas I, Joslin AC, Hansen GT, Bozek G, Moskowitz IP, McNally EM, Nóbrega MA. A promoter interaction map for cardiovascular disease genetics.Elife. 2018; 7:e35788.CrossrefMedlineGoogle Scholar
  • 151. Krijger PH, de Laat W. Regulation of disease-associated gene expression in the 3D genome.Nat Rev Mol Cell Biol. 2016; 17:771–782. doi: 10.1038/nrm.2016.138CrossrefMedlineGoogle Scholar
  • 152. Albert FW, Kruglyak L. The role of regulatory variation in complex traits and disease.Nat Rev Genet. 2015; 16:197–212. doi: 10.1038/nrg3891CrossrefMedlineGoogle Scholar
  • 153. Dixon JR, Selvaraj S, Yue F, Kim A, Li Y, Shen Y, Hu M, Liu JS, Ren B. Topological domains in mammalian genomes identified by analysis of chromatin interactions.Nature. 2012; 485:376–380. doi: 10.1038/nature11082CrossrefMedlineGoogle Scholar
  • 154. Nora EP, Lajoie BR, Schulz EG, Giorgetti L, Okamoto I, Servant N, Piolot T, van Berkum NL, Meisig J, Sedat J, et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre.Nature. 2012; 485:381–385. doi: 10.1038/nature11049CrossrefMedlineGoogle Scholar
  • 155. Nora EP, Goloborodko A, Valton AL, Gibcus JH, Uebersohn A, Abdennur N, Dekker J, Mirny LA, Bruneau BG. Targeted degradation of CTCF decouples local insulation of chromosome domains from genomic compartmentalization.Cell. 2017; 169:930–944.e22. doi: 10.1016/j.cell.2017.05.004CrossrefMedlineGoogle Scholar
  • 156. Rao SSP, Huang SC, Glenn St Hilaire B, Engreitz JM, Perez EM, Kieffer-Kwon KR, Sanborn AL, Johnstone SE, Bascom GD, Bochkov ID, et al. Cohesin loss eliminates all loop domains.Cell. 2017; 171:305–320.e24. doi: 10.1016/j.cell.2017.09.026CrossrefMedlineGoogle Scholar
  • 157. Zuin J, Dixon JR, van der Reijden MI, Ye Z, Kolovos P, Brouwer RW, van de Corput MP, van de Werken HJ, Knoch TA, van IJcken WF, et al. Cohesin and CTCF differentially affect chromatin architecture and gene expression in human cells.Proc Natl Acad Sci USA. 2014; 111:996–1001. doi: 10.1073/pnas.1317788111CrossrefMedlineGoogle Scholar
  • 158. Lupiáñez DG, Kraft K, Heinrich V, Krawitz P, Brancati F, Klopocki E, Horn D, Kayserili H, Opitz JM, Laxova R, et al. Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions.Cell. 2015; 161:1012–1025. doi: 10.1016/j.cell.2015.04.004CrossrefMedlineGoogle Scholar
  • 159. Bianchi V, Geeven G, Tucker N, Hilvering CRE, Hall AW, Roselli C, Hill M, Martin JF, Margulies KB, Ellinor PT, et al. Detailed regulatory interaction map of the human heart facilitates gene discovery for cardiovascular disease.SSRN Electron J. 2019.CrossrefGoogle Scholar
  • 160. Choy MK, Javierre BM, Williams SG, Baross SL, Liu Y, Wingett SW, Akbarov A, Wallace C, Freire-Pritchett P, Rugg-Gunn PJ, et al. Promoter interactome of human embryonic stem cell-derived cardiomyocytes connects GWAS regions to cardiac gene networks.Nat Commun. 2018; 9:4792. doi: 10.1038/s41467-018-07399-0CrossrefMedlineGoogle Scholar
  • 161. Ye J, Tucker NR, Weng LC, Clauss S, Lubitz SA, Ellinor PT. A functional variant associated with atrial fibrillation regulates PITX2c expression through TFAP2a.Am J Hum Genet. 2016; 99:1281–1291. doi: 10.1016/j.ajhg.2016.10.001CrossrefMedlineGoogle Scholar
  • 162. van den Boogaard M, Wong LY, Tessadori F, Bakker ML, Dreizehnter LK, Wakker V, Bezzina CR, ‘t Hoen PA, Bakkers J, Barnett P, et al. Genetic variation in T-box binding element functionally affects SCN5A/SCN10A enhancer.J Clin Invest. 2012; 122:2519–2530. doi: 10.1172/JCI62613CrossrefMedlineGoogle Scholar
  • 163. van den Boogaard M, Smemo S, Burnicka-Turek O, Arnolds DE, van de Werken HJ, Klous P, McKean D, Muehlschlegel JD, Moosmann J, Toka O, et al. A common genetic variant within SCN10A modulates cardiac SCN5A expression.J Clin Invest. [Internet]. 2014; 124:1844–1852. Available from: Scholar
  • 164. Shiratori H, Sakuma R, Watanabe M, Hashiguchi H, Mochida K, Sakai Y, Nishino J, Saijoh Y, Whitman M, Hamada H. Two-step regulation of left-right asymmetric expression of Pitx2: initiation by nodal signaling and maintenance by Nkx2.Mol Cell. 2001; 7:137–149. doi: 10.1016/s1097-2765(01)00162-9CrossrefMedlineGoogle Scholar
  • 165. Bamforth SD, Bragança J, Farthing CR, Schneider JE, Broadbent C, Michell AC, Clarke K, Neubauer S, Norris D, Brown NA, et al. Cited2 controls left-right patterning and heart development through a Nodal-Pitx2c pathway.Nat Genet. 2004; 36:1189–1196. doi: 10.1038/ng1446CrossrefMedlineGoogle Scholar
  • 166. Leussink B, Brouwer A, el Khattabi M, Poelmann RE, Gittenberger-de Groot AC, Meijlink F. Expression patterns of the paired-related homeobox genes MHox/Prx1 and S8/Prx2 suggest roles in development of the heart and the forebrain.Mech Dev. 1995; 52:51–64. doi: 10.1016/0925-4773(95)00389-iCrossrefMedlineGoogle Scholar
  • 167. Camelliti P, Green CR, LeGrice I, Kohl P. Fibroblast network in rabbit sinoatrial node: structural and functional identification of homogeneous and heterogeneous cell coupling.Circ Res. 2004; 94:828–835. doi: 10.1161/01.RES.0000122382.19400.14LinkGoogle Scholar
  • 168. Rohr S. Role of gap junctions in the propagation of the cardiac action potential.Cardiovasc Res. 2004; 62:309–322. doi: 10.1016/j.cardiores.2003.11.035CrossrefMedlineGoogle Scholar
  • 169. Sotoodehnia N, Isaacs A, de Bakker PI, Dörr M, Newton-Cheh C, Nolte IM, van der Harst P, Müller M, Eijgelsheim M, Alonso A, et al. Common variants in 22 loci are associated with QRS duration and cardiac ventricular conduction.Nat Genet. 2010; 42:1068–1076. doi: 10.1038/ng.716CrossrefMedlineGoogle Scholar
  • 170. Smith JG, Magnani JW, Palmer C, Meng YA, Soliman EZ, Musani SK, Kerr KF, Schnabel RB, Lubitz SA, Sotoodehnia N, et al; Candidate-gene Association Resource (CARe) Consortium. Genome-wide association studies of the PR interval in African Americans.PLoS Genet. 2011; 7:e1001304. doi: 10.1371/journal.pgen.1001304CrossrefMedlineGoogle Scholar
  • 171. Chambers JC, Zhao J, Terracciano CM, Bezzina CR, Zhang W, Kaba R, Navaratnarajah M, Lotlikar A, Sehmi JS, Kooner MK, et al. Genetic variation in SCN10A influences cardiac conduction.Nat Genet. 2010; 42:149–152. doi: 10.1038/ng.516CrossrefMedlineGoogle Scholar
  • 172. Holm H, Gudbjartsson DF, Arnar DO, Thorleifsson G, Thorgeirsson G, Stefansdottir H, Gudjonsson SA, Jonasdottir A, Mathiesen EB, Njølstad I, et al. Several common variants modulate heart rate, PR interval and QRS duration.Nat Genet. 2010; 42:117–122. doi: 10.1038/ng.511CrossrefMedlineGoogle Scholar
  • 173. Pfeufer A, van Noord C, Marciante KD, Arking DE, Larson MG, Smith AV, Tarasov KV, Müller M, Sotoodehnia N, Sinner MF, et al. Genome-wide association study of PR interval.Nat Genet. 2010; 42:153–159. doi: 10.1038/ng.517CrossrefMedlineGoogle Scholar
  • 174. Bezzina CR, Barc J, Mizusawa Y, Remme CA, Gourraud JB, Simonet F, Verkerk AO, Schwartz PJ, Crotti L, Dagradi F, et al. Common variants at SCN5A-SCN10A and HEY2 are associated with Brugada syndrome, a rare disease with high risk of sudden cardiac death.Nat Genet. 2013; 45:1044–1049. doi: 10.1038/ng.2712CrossrefMedlineGoogle Scholar
  • 175. Verweij N, Mateo Leach I, van den Boogaard M, van Veldhuisen DJ, Christoffels VM, Hillege HL, van Gilst WH, Barnett P, de Boer RA, van der Harst P.; LifeLines Cohort Study. Genetic determinants of P wave duration and PR segment.Circ Cardiovasc Genet. 2014; 7:475–481. doi: 10.1161/CIRCGENETICS.113.000373LinkGoogle Scholar
  • 176. Hemerich D, Pei J, Harakalova M, van Setten J, Boymans S, Boukens BJ, Efimov IR, Michels M, van der Velden J, Vink A, et al. Integrative functional annotation of 52 genetic loci influencing myocardial mass identifies candidate regulatory variants and target genes.Circ Genom Precis Med. 2019; 12:e002328. doi: 10.1161/CIRCGEN.118.002328LinkGoogle Scholar
  • 177. van der Harst P, van Setten J, Verweij N, Vogler G, Franke L, Maurano MT, Wang X, Mateo Leach I, Eijgelsheim M, Sotoodehnia N, et al. 52 Genetic loci influencing myocardial mass.J Am Coll Cardiol. 2016; 68:1435–1448. doi: 10.1016/j.jacc.2016.07.729CrossrefMedlineGoogle Scholar
  • 178. Benayoun BA, Pollina EA, Brunet A. Epigenetic regulation of ageing: linking environmental inputs to genomic stability.Nat Rev Mol Cell Biol. 2015; 16:593–610. doi: 10.1038/nrm4048CrossrefMedlineGoogle Scholar
  • 179. Koch L. Epigenetics: an epigenetic twist on the missing heritability of complex traits.Nat. Rev. Genet. 2014; 15:218.CrossrefGoogle Scholar
  • 180. Horvath S. DNA methylation age of human tissues and cell types.Genome Biol. 2013; 14:R115. doi: 10.1186/gb-2013-14-10-r115CrossrefMedlineGoogle Scholar
  • 181. Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, Hou L, Baccarelli AA, Stewart JD, Li Y, et al. An epigenetic biomarker of aging for lifespan and healthspan.Aging (Albany NY). 2018; 10:573–591. doi: 10.18632/aging.101414CrossrefMedlineGoogle Scholar
  • 182. Lind L, Ingelsson E, Sundström J, Siegbahn A, Lampa E. Methylation-based estimated biological age and cardiovascular disease.Eur J Clin Invest. 2018; 48.CrossrefMedlineGoogle Scholar
  • 183. Hernando-Herraez I, Evano B, Stubbs T, Commere PH, Jan Bonder M, Clark S, Andrews S, Tajbakhsh S, Reik W. Ageing affects DNA methylation drift and transcriptional cell-to-cell variability in mouse muscle stem cells.Nat Commun. 2019; 10:4361. doi: 10.1038/s41467-019-12293-4CrossrefMedlineGoogle Scholar
  • 184. Lin H, Yin X, Xie Z, Lunetta KL, Lubitz SA, Larson MG, Ko D, Magnani JW, Mendelson MM, Liu C, et al. Methylome-wide association study of atrial fibrillation in framingham heart study.Sci Rep. 2017; 7:40377.CrossrefMedlineGoogle Scholar
  • 185. Doñate Puertas R, Meugnier E, Romestaing C, Rey C, Morel E, Lachuer J, Gadot N, Scridon A, Julien C, Tronc F, et al. Atrial fibrillation is associated with hypermethylation in human left atrium, and treatment with decitabine reduces atrial tachyarrhythmias in spontaneously hypertensive rats.Transl Res. 2017; 184:57–67. e5. doi: 10.1016/j.trsl.2017.03.004CrossrefMedlineGoogle Scholar
  • 186. Zhao G, Zhou J, Gao J, Liu Y, Gu S, Zhang X, Su P. Genome-wide DNA methylation analysis in permanent atrial fibrillation.Mol Med Rep. 2017; 16:5505–5514. doi: 10.3892/mmr.2017.7221CrossrefMedlineGoogle Scholar
  • 187. Zhang W, Song M, Qu J, Liu GH. Epigenetic modifications in cardiovascular aging and diseases.Circ Res. 2018; 123:773–786. doi: 10.1161/CIRCRESAHA.118.312497LinkGoogle Scholar
  • 188. Berger SL. Histone modifications in transcriptional regulation.Curr Opin Genet Dev. 2002; 12:142–148. doi: 10.1016/s0959-437x(02)00279-4CrossrefMedlineGoogle Scholar
  • 189. Lawrence M, Daujat S, Schneider R. Lateral thinking: how histone modifications regulate gene expression.Trends Genet. 2016; 32:42–56. doi: 10.1016/j.tig.2015.10.007CrossrefMedlineGoogle Scholar
  • 190. Zaret KS, Mango SE. Pioneer transcription factors, chromatin dynamics, and cell fate control.Curr Opin Genet Dev. 2016; 37:76–81. doi: 10.1016/j.gde.2015.12.003CrossrefMedlineGoogle Scholar
  • 191. Li B, Carey M, Workman JL. The role of chromatin during transcription.Cell. 2007; 128:707–719. doi: 10.1016/j.cell.2007.01.015CrossrefMedlineGoogle Scholar
  • 192. Takeuchi JK, Lou X, Alexander JM, Sugizaki H, Delgado-Olguín P, Holloway AK, Mori AD, Wylie JN, Munson C, Zhu Y, et al. Chromatin remodelling complex dosage modulates transcription factor function in heart development.Nat Commun. 2011; 2:187. doi: 10.1038/ncomms1187CrossrefMedlineGoogle Scholar
  • 193. Zhang D, Hu X, Henning RH, Brundel BJ. Keeping up the balance: role of HDACs in cardiac proteostasis and therapeutic implications for atrial fibrillation.Cardiovasc Res. 2016; 109:519–526. doi: 10.1093/cvr/cvv265CrossrefMedlineGoogle Scholar
  • 194. Song S, Zhang R, Mo B, Chen L, Liu L, Yu Y, Cao W, Fang G, Wan Y, Gu Y, et al. EZH2 as a novel therapeutic target for atrial fibrosis and atrial fibrillation.J Mol Cell Cardiol. 2019; 135:119–133. doi: 10.1016/j.yjmcc.2019.08.003CrossrefMedlineGoogle Scholar
  • 195. Korantzopoulos P, Kolettis TM, Galaris D, Goudevenos JA. The role of oxidative stress in the pathogenesis and perpetuation of atrial fibrillation.Int J Cardiol. 2007; 115:135–143. doi: 10.1016/j.ijcard.2006.04.026CrossrefMedlineGoogle Scholar
  • 196. Dai DF, Rabinovitch PS. Cardiac aging in mice and humans: the role of mitochondrial oxidative stress.Trends Cardiovasc Med. 2009; 19:213–220. doi: 10.1016/j.tcm.2009.12.004CrossrefMedlineGoogle Scholar
  • 197. Davalli P, Mitic T, Caporali A, Lauriola A, D’Arca D. ROS, cell senescence, and novel molecular mechanisms in aging and age-related diseases.Oxid Med Cell Longev. 2016; 2016:3565127. doi: 10.1155/2016/3565127CrossrefMedlineGoogle Scholar
  • 198. Xie W, Santulli G, Reiken SR, Yuan Q, Osborne BW, Chen BX, Marks AR. Mitochondrial oxidative stress promotes atrial fibrillation.Sci Rep. 2015; 5:11427. doi: 10.1038/srep11427CrossrefMedlineGoogle Scholar
  • 199. Puente BN, Kimura W, Muralidhar SA, Moon J, Amatruda JF, Phelps KL, Grinsfelder D, Rothermel BA, Chen R, Garcia JA, et al. The oxygen-rich postnatal environment induces cardiomyocyte cell-cycle arrest through DNA damage response.Cell. 2014; 157:565–579. doi: 10.1016/j.cell.2014.03.032CrossrefMedlineGoogle Scholar
  • 200. Calkins K, Devaskar SU. Fetal origins of adult disease.Curr Probl Pediatr Adolesc Health Care. 2011; 41:158–176. doi: 10.1016/j.cppeds.2011.01.001CrossrefMedlineGoogle Scholar
  • 201. Haïssaguerre M, Jaïs P, Shah DC, Takahashi A, Hocini M, Quiniou G, Garrigue S, Le Mouroux A, Le Métayer P, Clémenty J. Spontaneous initiation of atrial fibrillation by ectopic beats originating in the pulmonary veins.N Engl J Med. 1998; 339:659–666. doi: 10.1056/NEJM199809033391003CrossrefMedlineGoogle Scholar
  • 202. Ho SY, Cabrera JA, Tran VH, Farré J, Anderson RH, Sánchez-Quintana D. Architecture of the pulmonary veins: relevance to radiofrequency ablation.Heart. 2001; 86:265–270. doi: 10.1136/heart.86.3.265CrossrefMedlineGoogle Scholar
  • 203. Postma AV, Dekker LR, Soufan AT, Moorman AF. Developmental and genetic aspects of atrial fibrillation.Trends Cardiovasc Med. 2009; 19:123–130. doi: 10.1016/j.tcm.2009.07.003CrossrefMedlineGoogle Scholar
  • 204. Mommersteeg MT, Christoffels VM, Anderson RH, Moorman AF. Atrial fibrillation: a developmental point of view.Heart Rhythm. 2009; 6:1818–1824. doi: 10.1016/j.hrthm.2009.07.011CrossrefMedlineGoogle Scholar
  • 205. Kapur S, Macrae CA. The developmental basis of adult arrhythmia: atrial fibrillation as a paradigm.Front Physiol. 2013; 4:221. doi: 10.3389/fphys.2013.00221CrossrefMedlineGoogle Scholar
  • 206. Garcia-Frigola C, Shi Y, Evans SM. Expression of the hyperpolarization-activated cyclic nucleotide-gated cation channel HCN4 during mouse heart development.Gene Expr Patterns. 2003; 3:777–783. doi: 10.1016/s1567-133x(03)00125-xCrossrefMedlineGoogle Scholar
  • 207. Stieber J, Herrmann S, Feil S, Löster J, Feil R, Biel M, Hofmann F, Ludwig A. The hyperpolarization-activated channel HCN4 is required for the generation of pacemaker action potentials in the embryonic heart.Proc Natl Acad Sci U S A. 2003; 100:15235–15240. doi: 10.1073/pnas.2434235100CrossrefMedlineGoogle Scholar
  • 208. Harzheim D, Pfeiffer KH, Fabritz L, Kremmer E, Buch T, Waisman A, Kirchhof P, Kaupp UB, Seifert R. Cardiac pacemaker function of HCN4 channels in mice is confined to embryonic development and requires cyclic AMP.EMBO J. 2008; 27:692–703. doi: 10.1038/emboj.2008.3CrossrefMedlineGoogle Scholar
  • 209. Hoesl E, Stieber J, Herrmann S, Feil S, Tybl E, Hofmann F, Feil R, Ludwig A. Tamoxifen-inducible gene deletion in the cardiac conduction system.J Mol Cell Cardiol. 2008; 45:62–69. doi: 10.1016/j.yjmcc.2008.04.008CrossrefMedlineGoogle Scholar
  • 210. Durand NC, Robinson JT, Shamim MS, Machol I, Mesirov JP, Lander ES, Lieberman Aiden E. Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom.Cell Systems. 2016; 3:99–101. doi: 10.1016/j.cels.2015.07.012CrossrefMedlineGoogle Scholar
  • 211. Robinson JT, Turner D, Durand NC, Thorvaldsdóttir H, Mesirov JP, Lieberman Aiden E. Juicebox.js provides a cloud-based visualization system for Hi-C data.Cell Systems. 2018;6.256–258.e1. doi: 10.1016/j.cels.2018.01.001CrossrefGoogle Scholar


eLetters should relate to an article recently published in the journal and are not a forum for providing unpublished data. Comments are reviewed for appropriate use of tone and language. Comments are not peer-reviewed. Acceptable comments are posted to the journal website only. Comments are not published in an issue and are not indexed in PubMed. Comments should be no longer than 500 words and will only be posted online. References are limited to 10. Authors of the article cited in the comment will be invited to reply, as appropriate.

Comments and feedback on AHA/ASA Scientific Statements and Guidelines should be directed to the AHA/ASA Manuscript Oversight Committee via its Correspondence page.