Meta-Analysis of Genome-Wide Association Studies Reveals Genetic Mechanisms of Supraventricular Arrhythmias.

BACKGROUND: Substantial data support a heritable basis for supraventricular tachycardias, but the genetic determinants and molecular mechanisms of these arrhythmias are poorly understood. We sought to identify genetic loci associated with atrioventricular nodal reentrant tachycardia (AVNRT) and atrioventricular accessory pathways or atrioventricular reciprocating tachycardia (AVAPs/AVRT). METHODS: We performed multiancestry meta-analyses of genome-wide association studies to identify genetic loci for AVNRT (4 studies) and AVAP/AVRT (7 studies). We assessed evidence supporting the potential causal effects of candidate genes by analyzing relations between associated variants and cardiac gene expression, performing transcriptome-wide analyses, and examining prior genome-wide association studies. RESULTS: Analyses comprised 2384 AVNRT cases and 106 489 referents, and 2811 AVAP/AVRT cases and 1,483 093 referents. We identified 2 significant loci for AVNRT, which implicate NKX2-5 and TTN as disease susceptibility genes. A transcriptome-wide association analysis supported an association between reduced predicted cardiac expression of NKX2-5 and AVNRT. We identified 3 significant loci for AVAP/AVRT, which implicate SCN5A, SCN10A, and TTN/CCDC141. Variant associations at several loci have been previously reported for cardiac phenotypes, including atrial fibrillation, stroke, Brugada syndrome, and electrocardiographic intervals. CONCLUSIONS: Our findings highlight gene regions associated with ion channel function (AVAP/AVRT), as well as cardiac development and the sarcomere (AVAP/AVRT and AVNRT) as important potential effectors of supraventricular tachycardia susceptibility.

Loss of the Atrial Fibrillation-Related Gene, Zfhx3, Results in Atrial Dilation and Arrhythmias.

BACKGROUND: ZFHX3 (zinc finger homeobox 3), a gene that encodes a large transcription factor, is at the second-most significantly associated locus with atrial fibrillation (AF), but its function in the heart is unknown. This study aims to identify causative genetic variation related to AF at the ZFHX3 locus and examine the impact of Zfhx3 loss on cardiac function in mice. METHODS: CRISPR-Cas9 genome editing, chromatin immunoprecipitation, and luciferase assays in pluripotent stem cell-derived cardiomyocytes were used to identify causative genetic variation related to AF at the ZFHX3 locus. Cardiac function was assessed by echocardiography, magnetic resonance imaging, electrophysiology studies, calcium imaging, and RNA sequencing in mice with heterozygous and homozygous cardiomyocyte-restricted Zfhx3 loss (Zfhx3 Het and knockout, respectively). Human cardiac single-nucleus ATAC (assay for transposase-accessible chromatin)-sequencing data was analyzed to determine which genes in atrial cardiomyocytes are directly regulated by ZFHX3. RESULTS: We found single-nucleotide polymorphism (SNP) rs12931021 modulates an enhancer regulating ZFHX3 expression, and the AF risk allele is associated with decreased ZFHX3 transcription. We observed a gene-dose response in AF susceptibility with Zfhx3 knockout mice having higher incidence, frequency, and burden of AF than Zfhx3 Het and wild-type mice, with alterations in conduction velocity, atrial action potential duration, calcium handling and the development of atrial enlargement and thrombus, and dilated cardiomyopathy. Zfhx3 loss results in atrial-specific differential effects on genes and signaling pathways involved in cardiac pathophysiology and AF. CONCLUSIONS: Our findings implicate ZFHX3 as the causative gene at the 16q22 locus for AF, and cardiac abnormalities caused by loss of cardiac Zfhx3 are due to atrial-specific dysregulation of pathways involved in AF susceptibility. Together, these data reveal a novel and important role for Zfhx3 in the control of cardiac genes and signaling pathways essential for normal atrial function.

Epigenetic Analyses of Human Left Atrial Tissue Identifies Gene Networks Underlying Atrial Fibrillation.

BACKGROUND: Atrial fibrillation (AF) often arises from structural abnormalities in the left atria (LA). Annotation of the noncoding genome in human LA is limited, as are effects on gene expression and chromatin architecture. Many AF-associated genetic variants reside in noncoding regions; this knowledge gap impairs efforts to understand the molecular mechanisms of AF and cardiac conduction phenotypes. METHODS: We generated a model of the LA noncoding genome by profiling 7 histone post-translational modifications (active: H3K4me3, H3K4me2, H3K4me1, H3K27ac, H3K36me3; repressive: H3K27me3, H3K9me3), CTCF binding, and gene expression in samples from 5 individuals without structural heart disease or AF. We used MACS2 to identify peak regions (P<0.01), applied a Markov model to classify regulatory elements, and annotated this model with matched gene expression data. We intersected chromatin states with expression quantitative trait locus, DNA methylation, and HiC chromatin interaction data from LA and left ventricle. Finally, we integrated genome-wide association data for AF and electrocardiographic traits to link disease-related variants to genes. RESULTS: Our model identified 21 epigenetic states, encompassing regulatory motifs, such as promoters, enhancers, and repressed regions. Genes were regulated by proximal chromatin states; repressive states were associated with a significant reduction in gene expression (P<2×10-16). Chromatin states were differentially methylated, promoters were less methylated than repressed regions (P<2×10-16). We identified over 15 000 LA-specific enhancers, defined by homeobox family motifs, and annotated several cardiovascular disease susceptibility loci. Intersecting AF and PR genome-wide association studies loci with long-range chromatin conformation data identified a gene interaction network dominated by NKX2-5, TBX3, ZFHX3, and SYNPO2L. CONCLUSIONS: Profiling the noncoding genome provides new insights into the gene expression and chromatin regulation in human LA tissue. These findings enabled identification of a gene network underlying AF; our experimental and analytic approach can be extended to identify molecular mechanisms for other cardiac diseases and traits.

Genetic Determinants of Electrocardiographic P-Wave Duration and Relation to Atrial Fibrillation.

BACKGROUND: The P-wave duration (PWD) is an electrocardiographic measurement that represents cardiac conduction in the atria. Shortened or prolonged PWD is associated with atrial fibrillation (AF). We used exome-chip data to examine the associations between common and rare variants with PWD. METHODS: Fifteen studies comprising 64 440 individuals (56 943 European, 5681 African, 1186 Hispanic, 630 Asian) and ≈230 000 variants were used to examine associations with maximum PWD across the 12-lead ECG. Meta-analyses summarized association results for common variants; gene-based burden and sequence kernel association tests examined low-frequency variant-PWD associations. Additionally, we examined the associations between PWD loci and AF using previous AF genome-wide association studies. RESULTS: We identified 21 common and low-frequency genetic loci (14 novel) associated with maximum PWD, including several AF loci (TTN, CAND2, SCN10A, PITX2, CAV1, SYNPO2L, SOX5, TBX5, MYH6, RPL3L). The top variants at known sarcomere genes (TTN, MYH6) were associated with longer PWD and increased AF risk. However, top variants at other loci (eg, PITX2 and SCN10A) were associated with longer PWD but lower AF risk. CONCLUSIONS: Our results highlight multiple novel genetic loci associated with PWD, and underscore the shared mechanisms of atrial conduction and AF. Prolonged PWD may be an endophenotype for several different genetic mechanisms of AF.

Multi-ancestry GWAS of the electrocardiographic PR interval identifies 202 loci underlying cardiac conduction.

The electrocardiographic PR interval reflects atrioventricular conduction, and is associated with conduction abnormalities, pacemaker implantation, atrial fibrillation (AF), and cardiovascular mortality. Here we report a multi-ancestry (N = 293,051) genome-wide association meta-analysis for the PR interval, discovering 202 loci of which 141 have not previously been reported. Variants at identified loci increase the percentage of heritability explained, from 33.5% to 62.6%. We observe enrichment for cardiac muscle developmental/contractile and cytoskeletal genes, highlighting key regulation processes for atrioventricular conduction. Additionally, 8 loci not previously reported harbor genes underlying inherited arrhythmic syndromes and/or cardiomyopathies suggesting a role for these genes in cardiovascular pathology in the general population. We show that polygenic predisposition to PR interval duration is an endophenotype for cardiovascular disease, including distal conduction disease, AF, and atrioventricular pre-excitation. These findings advance our understanding of the polygenic basis of cardiac conduction, and the genetic relationship between PR interval duration and cardiovascular disease.

Bivalent Chromatin Domains in Glioblastoma Reveal a Subtype-Specific Signature of Glioma Stem Cells.

Glioblastoma multiforme (GBM) can be clustered by gene expression into four main subtypes associated with prognosis and survival, but enhancers and other gene-regulatory elements have not yet been identified in primary tumors. Here, we profiled six histone modifications and CTCF binding as well as gene expression in primary gliomas and identified chromatin states that define distinct regulatory elements across the tumor genome. Enhancers in mesenchymal and classical tumor subtypes drove gene expression associated with cell migration and invasion, whereas enhancers in proneural tumors controlled genes associated with a less aggressive phenotype in GBM. We identified bivalent domains marked by activating and repressive chromatin modifications. Interestingly, the gene interaction network from common (subtype-independent) bivalent domains was highly enriched for homeobox genes and transcription factors and dominated by SHH and Wnt signaling pathways. This subtype-independent signature of early neural development may be indicative of poised dedifferentiation capacity in glioblastoma and could provide potential targets for therapy.Significance: Enhancers and bivalent domains in glioblastoma are regulated in a subtype-specific manner that resembles gene regulation in glioma stem cells. Cancer Res; 78(10); 2463-74. ©2018 AACR.

An eQTL analysis of the human glioblastoma multiforme genome.

In this paper we use eQTL mapping to identify associations between gene dysregulation and single nucleotide polymorphism (SNP) genotypes in glioblastoma multiforme (GBM). A set of 532,954 SNPs was evaluated as predictors of the expression levels of 22,279 expression probes. We identified SNPs associated with fold change in expression level rather than raw expression levels in the tumor. Following adjustment for false discovery rate, the complete set of probes yielded 9257 significant associations (p<0.05). We found 18 eQTLs that were missense mutations. Many of the eQTLs in the non-coding regions of a gene, or linked to nearby genes, had large numbers of significant associations (e.g. 321 for RNASE3, 101 for BNC2). Functional enrichment analysis revealed that the expression probes in significant associations were involved in signal transduction, transcription regulation, membrane function, and cell cycle regulation. These results suggest several loci that may serve as hubs in gene regulatory pathways associated with GBM.

Simultaneous SNP identification and assessment of allele-specific bias from ChIP-seq data.

BACKGROUND: Single nucleotide polymorphisms (SNPs) have been associated with many aspects of human development and disease, and many non-coding SNPs associated with disease risk are presumed to affect gene regulation. We have previously shown that SNPs within transcription factor binding sites can affect transcription factor binding in an allele-specific and heritable manner. However, such analysis has relied on prior whole-genome genotypes provided by large external projects such as HapMap and the 1000 Genomes Project. This requirement limits the study of allele-specific effects of SNPs in primary patient samples from diseases of interest, where complete genotypes are not readily available. RESULTS: In this study, we show that we are able to identify SNPs de novo and accurately from ChIP-seq data generated in the ENCODE Project. Our de novo identified SNPs from ChIP-seq data are highly concordant with published genotypes. Independent experimental verification of more than 100 sites estimates our false discovery rate at less than 5%. Analysis of transcription factor binding at de novo identified SNPs revealed widespread heritable allele-specific binding, confirming previous observations. SNPs identified from ChIP-seq datasets were significantly enriched for disease-associated variants, and we identified dozens of allele-specific binding events in non-coding regions that could distinguish between disease and normal haplotypes. CONCLUSIONS: Our approach combines SNP discovery, genotyping and allele-specific analysis, but is selectively focused on functional regulatory elements occupied by transcription factors or epigenetic marks, and will therefore be valuable for identifying the functional regulatory consequences of non-coding SNPs in primary disease samples.