Genome-wide Study of Atrial Fibrillation Identifies Seven Risk Loci and Highlights Biological Pathways and Regulatory Elements Involved in Cardiac Development

Advancing drug development for atrial fibrillation by prioritising findings from human genetic association studies

BACKGROUND

Drug development for atrial fibrillation (AF) has failed to yield new approved compounds. We sought to identify and prioritise potential druggable targets with support from human genetics, by integrating the available evidence with bioinformatics sources relevant for AF drug development.

METHODS

Genetic hits for AF and related traits were identified through structured search of MEDLINE. Genes derived from each paper were cross-referenced with the OpenTargets platform for drug interactions. Confirmation/validation was demonstrated through structured searches and review of evidence on MEDLINE and ClinialTrials.gov for each drug and its association with AF.

FINDINGS

613 unique drugs were identified, with 21 already included in AF Guidelines. Cardiovascular drugs from classes not currently used for AF (e.g. ranolazine and carperitide) and anti-inflammatory drugs (e.g. dexamethasone and mehylprednisolone) had evidence of potential benefit. Further targets were considered druggable but remain open for drug development.

INTERPRETATION

Our systematic approach, combining evidence from different bioinformatics platforms, identified drug repurposing opportunities and druggable targets for AF.

FUNDING

KK is supported by Barts Charity grant G-002089 and is mentored on the AFGen 2023-24 Fellowship funded by the AFGen NIH/NHLBI grant R01HL092577. RP is supported by the UCL BHF Research Accelerator AA/18/6/34223 and NIHR grant NIHR129463. AFS is supported by the BHF grants PG/18/5033837, PG/22/10989 and UCL BHF Accelerator AA/18/6/34223 as well as the UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding guarantee EP/Z000211/1 and by the UKRI-NIHR grant MR/V033867/1 for the Multimorbidity Mechanism and Therapeutics Research Collaboration. AF is supported by UCL BHF Accelerator AA/18/6/34223. CF is supported by UCL BHF Accelerator AA/18/6/34223.

Genetics, transcriptomics, metagenomics, and metabolomics in the pathogenesis and prediction of atrial fibrillation

The primary cellular substrates of atrial fibrillation (AF) and the mechanisms underlying AF onset remain poorly characterized and therefore, its risk assessment lacks precision. While the use of omics may enable discovery of novel AF risk factors and narrow down the cellular pathways involved in AF pathogenesis, the work is far from complete. Large-scale genome-wide association studies and transcriptomic analyses that allow an unbiased, non-candidate-gene-based delineation of molecular changes associated with AF in humans have identified at least 150 genetic loci associated with AF. However, only few of these loci have been thoroughly mechanistically dissected, indicating that much remains to be discovered for targeted diagnostics and therapeutics. Metabolomics and metagenomics, on the other hand, add to the understanding of AF downstream of the primary substrate and integrate the signalling of environmental and host factors, respectively. These two rapidly developing fields have already provided several correlates of prevalent and incident AF that require additional validation in external cohorts and experimental studies. In this review, we take a look at the recent developments in genetics, transcriptomics, metagenomics, and metabolomics and how they may aid in improving the discovery of AF risk factors and shed light into the molecular mechanisms leading to AF onset.

Polygenic risk scores in atrial fibrillation: Associations and clinical utility in disease prediction

Atrial fibrillation (AF) is a common heart arrhythmia and a major cause of cardioembolic stroke. Therefore, accurate prediction is desirable to allow high-risk individuals to be identified early and their risk lowered before complications arise. Polygenic risk scores (PRSs) have become a popular method of quantifying aggregated genetic risk from common variants, but their clinical value in AF remains uncertain. This literature review summarizes the associations between PRS and AF risk and discusses the evidence for the clinical utility of PRS for AF prediction. Stroke risk in patients with AF is also considered. Despite consistent associations between PRS and AF risk, the performance of PRS as a stand-alone tool for AF prediction was poor. However, addition of PRS to the existing AF prediction models commonly improved the predictive performance above that of the clinical models alone, including in cohorts with comorbid cardiovascular disease. Associations between PRS and cardioembolic stroke risk in patients with AF have also been reported, but improvements in stroke prediction models from PRS have been minimal. PRS are likely to add value to the existing clinical AF prediction models; however, standardization of PRS across studies and populations will likely be required before they can be meaningfully adopted into routine clinical practice.

Unsupervised ensemble-based phenotyping enhances discoverability of genes related to left-ventricular morphology

Recent genome-wide association studies have successfully identified associations between genetic variants and simple cardiac morphological parameters derived from cardiac magnetic resonance images. However, the emergence of large databases, including genetic data linked to cardiac magnetic resonance facilitates the investigation of more nuanced patterns of cardiac shape variability than those studied so far. Here we propose a framework for gene discovery coined unsupervised phenotype ensembles. The unsupervised phenotype ensemble builds a redundant yet highly expressive representation by pooling a set of phenotypes learnt in an unsupervised manner, using deep learning models trained with different hyperparameters. These phenotypes are then analysed via genome-wide association studies, retaining only highly confident and stable associations across the ensemble. We applied our approach to the UK Biobank database to extract geometric features of the left ventricle from image-derived three-dimensional meshes. We demonstrate that our approach greatly improves the discoverability of genes that influence left ventricle shape, identifying 49 loci with study-wide significance and 25 with suggestive significance. We argue that our approach would enable more extensive discovery of gene associations with image-derived phenotypes for other organs or image modalities.

Genetic Susceptibility to Arrhythmia Phenotypes in a Middle Eastern Cohort of 14,259 Whole-Genome Sequenced Individuals

Background: The current study explores the genetic underpinnings of cardiac arrhythmia phenotypes within Middle Eastern populations, which are under-represented in genomic medicine research. Methods: Whole-genome sequencing data from 14,259 individuals from the Qatar Biobank were used and contained 47.8% of Arab ancestry, 18.4% of South Asian ancestry, and 4.6% of African ancestry. The frequency of rare functional variants within a set of 410 candidate genes for cardiac arrhythmias was assessed. Polygenic risk score (PRS) performance for atrial fibrillation (AF) prediction was evaluated. Results: This study identified 1196 rare functional variants, including 162 previously linked to arrhythmia phenotypes, with varying frequencies across Arab, South Asian, and African ancestries. Of these, 137 variants met the pathogenic or likely pathogenic (P/LP) criteria according to ACMG guidelines. Of these, 91 were in ACMG actionable genes and were present in 1030 individuals (~7%). Ten P/LP variants showed significant associations with atrial fibrillation p < 2.4 × 10-10. Five out of ten existing PRSs were significantly associated with AF (e.g., PGS000727, p = 0.03, OR = 1.43 [1.03, 1.97]). Conclusions: Our study is the largest to study the genetic predisposition to arrhythmia phenotypes in the Middle East using whole-genome sequence data. It underscores the importance of including diverse populations in genomic investigations to elucidate the genetic landscape of cardiac arrhythmias and mitigate health disparities in genomic medicine.

Bioinformatics analysis reveals the potential common genes and immune characteristics between atrial fibrillation and periodontitis

BACKGROUND AND OBJECTIVE

Atrial fibrillation (AF) and periodontitis, both classified under chronic inflammatory diseases, share common etiologies, including genetic factors and immune pathways. However, the exact mechanisms are still poorly understood. This study aimed to explore the potential common genes and immune characteristics between AF and periodontitis.

METHODS

Gene expression datasets for AF and periodontitis were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis was used to identify common genes in the training set. Functional analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, were conducted to elucidate the underlying mechanisms. Hub genes were further screened based on expression levels, receiver operating characteristic (ROC) curves, and least absolute shrinkage and selection operator (LASSO) regression. Then, based on the expression levels and ROC values of the hub genes in the validation set, the target genes were identified. Finally, immune cell infiltration analysis was performed on the AF and periodontitis datasets in the training set using the "CIBERSORT" R package. The relationships between target genes, infiltrating immune cells, and inflammatory factors were also investigated. In addition, AF susceptibility, atrial fibrosis, inflammatory infiltration, and RGS1 protein expression in rat models of periodontitis were assessed through in vivo electrophysiology experiments, Masson's trichrome staining, hematoxylin-eosin staining, immunohistochemistry, and western blotting, respectively.

RESULTS

A total of 21 common genes were identified between AF and periodontitis among the differentially expressed genes. After evaluating gene expression levels, ROC curves, and LASSO analysis, four significant genes between AF and periodontitis were identified, namely regulator of G-protein signaling 1 (RGS1), annexin A6 (ANXA6), solute carrier family 27 member 6 (SLC27A6), and ficolin 1 (FCN1). Further validation confirmed that RGS1 was the optimal shared target gene for AF and periodontitis. Immune cell infiltration analysis revealed that neutrophils and T cells play an important role in the pathogenesis of both diseases. RGS1 showed a significant positive correlation with activated memory CD4 T cells and gamma-delta T cells and a negative correlation with CD8 T cells and regulatory T cells in both training sets. Moreover, RGS1 was positively correlated with classical pro-inflammatory cytokines IL1β and IL6. In periodontitis rat models, AF susceptibility, atrial fibrosis, and inflammatory infiltration were significantly increased, and RGS1 expression in the atrial tissue was upregulated.

CONCLUSION

A common gene between AF and periodontitis, RGS1 appears central in linking the two conditions. Immune and inflammatory responses may underlie the interaction between AF and periodontitis.

MESuSiE enables scalable and powerful multi-ancestry fine-mapping of causal variants in genome-wide association studies

Fine-mapping in genome-wide association studies attempts to identify causal SNPs from a set of candidate SNPs in a local genomic region of interest and is commonly performed in one genetic ancestry at a time. Here, we present multi-ancestry sum of the single effects model (MESuSiE), a probabilistic multi-ancestry fine-mapping method, to improve the accuracy and resolution of fine-mapping by leveraging association information across ancestries. MESuSiE uses summary statistics as input, accounts for the diverse linkage disequilibrium pattern observed in different ancestries, explicitly models both shared and ancestry-specific causal SNPs, and relies on a variational inference algorithm for scalable computation. We evaluated the performance of MESuSiE through comprehensive simulations and multi-ancestry fine-mapping of four lipid traits with both European and African samples. In the real data, MESuSiE improves fine-mapping resolution by 19.0% to 72.0% compared to existing approaches, is an order of magnitude faster, and captures and categorizes shared and ancestry-specific causal signals with enhanced functional enrichment.

Multiplatform modeling of atrial fibrillation identifies phospholamban as a central regulator of cardiac rhythm

Atrial fibrillation (AF) is a common and genetically inheritable form of cardiac arrhythmia; however, it is currently not known how these genetic predispositions contribute to the initiation and/or maintenance of AF-associated phenotypes. One major barrier to progress is the lack of experimental systems to investigate the effects of gene function on rhythm parameters in models with human atrial and whole-organ relevance. Here, we assembled a multi-model platform enabling high-throughput characterization of the effects of gene function on action potential duration and rhythm parameters using human induced pluripotent stem cell-derived atrial-like cardiomyocytes and a Drosophila heart model, and validation of the findings using computational models of human adult atrial myocytes and tissue. As proof of concept, we screened 20 AF-associated genes and identified phospholamban loss of function as a top conserved hit that shortens action potential duration and increases the incidence of arrhythmia phenotypes upon stress. Mechanistically, our study reveals that phospholamban regulates rhythm homeostasis by functionally interacting with L-type Ca2+ channels and NCX. In summary, our study illustrates how a multi-model system approach paves the way for the discovery and molecular delineation of gene regulatory networks controlling atrial rhythm with application to AF.

Association Between rs2200733 Polymorphism of PITX2 Gene and the Risk of Atrial Fibrillation

BACKGROUND

As observed in recent genetic studies, PITX2 is one of the most popular genes with atrial fibrillation; single nucleotide polymorphism (rs2200733) at chromosome 4q25 (near PITX2) is found to be strongly associated with atrial fibrillation, but it has a difference among Chinese Han population. The basic aim of conducting this study is to find the correlation between PITX2 gene polymorphism and the risk of atrial fibrillation and to identify the possibility for early diagnosis of silent atrial fibrillation and high-risk atrial fibrillation.

METHODS

The study included 98 cases of atrial fibrillation patients and 88 non-atrial fibrillation patients in Affiliated Hospital of Yangzhou University were enrolled in a case-control study. The single nucleotide polymorphism of rs2200733 at 4q25 near PITX2 was genotyped by polymerase chain reaction-restriction fragment length polymorphism analysis.

RESULTS

A total of 98 patients with atrial fibrillation were genotyped, and the following frequencies were included in genotype percentages (44.9%, 50%, and 5.1%) while distribution of significant single nucleotide polymorphism rs2200733 consisted (29.55%, 53.41%, and 17.05%) which showed (χ2 = 9.159, P =.01). There was no significant difference in TC genotype frequency (P =.642), frequency of T allele (χ2 = 7.447, P =.006), and T allele was 1.806 times that of the control group (odds ratio = 1.806, 95% CI = 1.179-2.766, P =.006). According to logistic regression analysis, following results were concluded for TC genotype (odds ratio = 3.128, 95% CI = 1.053-9.287, P =.04), or TT genotype (odds ratio = 5.077, 95% CI = 1.653-15.595, P =.005) increased the risk of atrial fibrillation.

CONCLUSIONS

The genotype and allele frequency distribution of rs2200733 (T/C) near PITX2 is different in the atrial fibrillation group and the control group. The T allele is a risk factor for atrial fibrillation. Compared with the CC genotype, the TT genotype increased the risk of atrial fibrillation.

Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes

Artificial intelligence (AI)-based approaches can now use electrocardiograms (ECGs) to provide expert-level performance in detecting heart abnormalities and diagnosing disease. Additionally, patient age predicted from ECGs by AI models has shown great potential as a biomarker for cardiovascular age, where recent work has found its deviation from chronological age ("delta age") to be associated with mortality and co-morbidities. However, despite being crucial for understanding underlying individual risk, the genetic underpinning of delta age is unknown. In this work we performed a genome-wide association study using UK Biobank data (n=34,432) and identified eight loci associated with delta age ([Formula: see text]), including genes linked to cardiovascular disease (CVD) (e.g. SCN5A) and (heart) muscle development (e.g. TTN). Our results indicate that the genetic basis of cardiovascular ageing is predominantly determined by genes directly involved with the cardiovascular system rather than those connected to more general mechanisms of ageing. Our insights inform the epidemiology of CVD, with implications for preventative and precision medicine.

Atrial fibrillation in the presence and absence of heart failure enhances expression of genes involved in cardiomyocyte structure, conduction properties, fibrosis, inflammation, and endothelial dysfunction

BACKGROUND

Little is known about genome-wide changes in the atrial transcriptome as a cause or consequence of atrial fibrillation (AF), and the effect of its common and clinically relevant comorbidity-heart failure (HF).

OBJECTIVE

The purpose of this study was to explore candidate disease processes for AF by investigating gene expression changes in atrial tissue samples from patients with and without AF, stratified by HF.

METHODS

RNA sequencing was performed in right and left atrial appendage tissue in 195 patients undergoing open heart surgery from centers participating in the CATCH-ME consortium (no history of AF, n = 91; paroxysmal AF, n = 53; persistent/permanent AF, n = 51). Analyses were stratified into patients with/without HF (n = 75/120) and adjusted for age, sex, atrial side, and a combination of clinical characteristics.

RESULTS

We identified 35 genes associated with persistent AF compared to patients without a history of AF, both in the presence or absence of HF (false discovery rate <0.05). These were mostly novel associations, including 13 long noncoding RNAs. Genes were involved in regulation of cardiomyocyte structure, conduction properties, fibrosis, inflammation, and endothelial dysfunction. Gene set enrichment analysis identified mainly inflammatory gene sets to be enriched in AF patients without HF, and gene sets involved in cellular respiration in AF patients with HF.

CONCLUSION

Analysis of atrial gene expression profiles identified numerous novel genes associated with persistent AF, in the presence or absence of HF. Interestingly, no consistent transcriptional changes were associated with paroxysmal AF, suggesting that AF-induced changes in gene expression predominate other changes.

Long-term amelioration of an early-onset familial atrial fibrillation model with AAV-mediated in vivo gene therapy

Atrial fibrillation (AF) is a common cardiac disease with high prevalence in the general population. Despite a mild manifestation at the onset stage, it causes serious consequences, including sudden death, when the disease progresses to the late stage. Most available treatments of AF focus on symptom management or alleviation, due to a lack of fundamental knowledge and the fact that considerable variations of AF exist. With the popularisation of the next-generation sequencing technology, several causal genetic factors, including MYL4, have been discovered to contribute to AF, giving hope to developing its gene therapies. In this study, we attempted to treat a previously established rat AF model, which carried Myl4E11K/E11K loss of function mutation, via overexpression of exogenous wild-type Myl4 by AAV9 vectors. Our results showed that delivery of Myl4 expressing AAV9 to postnatal rat models rescued the symptoms of AF, indicating the therapeutic potential that early gene therapy intervention can achieve long-term effects in treating cardiac arrhythmias caused by gene mutations.

The HUNT study: A population-based cohort for genetic research

The Trøndelag Health Study (HUNT) is a population-based cohort of ∼229,000 individuals recruited in four waves beginning in 1984 in Trøndelag County, Norway. Approximately 88,000 of these individuals have available genetic data from array genotyping. HUNT participants were recruited during four community-based recruitment waves and provided information on health-related behaviors, self-reported diagnoses, family history of disease, and underwent physical examinations. Linkage via the Norwegian personal identification number integrates digitized health care information from doctor visits and national health registries including death, cancer and prescription registries. Genome-wide association studies of HUNT participants have provided insights into the mechanism of cardiovascular, metabolic, osteoporotic, and liver-related diseases, among others. Unique features of this cohort that facilitate research include nearly 40 years of longitudinal follow-up in a motivated and well-educated population, family data, comprehensive phenotyping, and broad availability of DNA, RNA, urine, fecal, plasma, and serum samples.

CXCR4 and TYROBP mediate the development of atrial fibrillation via inflammation

Atrial fibrillation (AF) is a rapid supraventricular arrhythmia. However, the pathogenesis of atrial fibrillation remains controversial. We obtained transcriptome expression profiles GSE41177, GSE115574 and GSE79768 from GEO database. WGCNA was performed, DEGs were screened, PPI network was constructed using STRING database. CTD database was used to identify the reference score of hub genes associated with cardiovascular diseases. Prediction of miRNAs of hub genes was performed by TargetScan. DIANA‐miRPath v3.0 was applied to make functional annotation of miRNA. The animal model of atrial fibrillation was constructed, RT‐PCR was used to verify the expression of hub genes. Immunofluorescence assay for THBS2 and VCAN was made to identify molecular. Design of BP neural network was made to explore the prediction relationship of CXCR4 and TYROBP on AF. The merged datasets contained 104 up‐regulated and 34 down‐regulated genes. GO and KEGG enrichment analysis results of DEGs showed they were mainly enriched in ‘regulation of release of sequestered calcium ion into cytosol’, ‘actin cytoskeleton organization’ and ‘focal adhesion’. The hub genes were CXCR4, SNAI2, S100A4, IGFBP3, CSNK2A1, CHGB, VCAN, APOE, C1QC and TYROBP, which were up‐regulated expression in the AF compared with control tissues. There was strong correlation among the CXCR4, TYROBP and AF based on the BP neural network. Through training, best training performance is 9.6474e‐05 at epoch 14, and the relativity was 0.99998. CXCR4 and TYROBP might be involved in the development of atrial fibrillation by affecting inflammation‐related signalling pathways and may serve as targets for early diagnosis and preventive treatment.

Genetics of atrial fibrillation-an update of recent findings

Atrial fibrillation (AF) is a common cardiac arrhythmia and a major risk factor for stroke, heart failure, and premature death. AF has a strong genetic predisposition. This review highlights the recent findings on the genetics of AF from genome-wide association studies (GWAS) and high-throughput sequencing studies. The consensus from GWAS implies that AF is both polygenic and pleiotropic in nature. With the advent of whole-genome sequencing and whole-exome sequencing, rare variants associated with AF pathogenesis have been identified. The recent studies have contributed towards better understanding of AF pathogenesis.

Acupuncture as an adjunctive therapy for arrhythmia: a Delphi expert consensus survey

Background

Current evidence suggests that acupuncture is an effective adjunctive therapy that can bring potential benefits to patients with cardiac arrhythmias. However, there are relevant gaps in the optimal therapeutic strategy, which may cause uncertainties on the best practice of acupuncture treatment for arrhythmia. We aim to develop consensus-based recommendations for clinical guidance on acupuncture treatment of cardiac arrhythmias.

Methods

A multidisciplinary panel of specialists was invited to participate in a two-round semi-open clinical issue investigation. Meanwhile, relevant literature reviews were searched in 3 databases to provide evidence. Subsequently, an initial consensus voting list on acupuncture as an adjunctive therapy for cardiac arrhythmias was derived from the clinical investigation and literature review. Finally, 30 authoritative experts reached a consensus on the key issues of the voting list by a three-round modified Delphi survey. Consensus was defined when >80% agreement was achieved.

Results

Following the three-round Delphi survey, there were 32 items (91.43%) finally reaching consensus, including the following 5 domains: (I) the benefits of acupuncture for the appropriate population; (II) the general therapeutic principle; (III) the acupuncture strategy; (IV) the relevant adverse events; (V) others.

Conclusions

Consensus was achieved on some key elements. Given the lack of guidelines and the substantial heterogeneity of previous studies, these recommendations are of value in providing guidance for clinical practice of acupuncturists and in assisting patients with arrhythmia to obtain standardized acupuncture treatment. It also pointed out some problems that need to be carefully explored in future studies.

Intergenerational polygenic obesity risk throughout adolescence in a cross-sectional study design: The HUNT study, Norway

OBJECTIVE

This study examined the relationship between parental obesity polygenic risk and children's BMI throughout adolescence. Additionally, from a smaller subsample, the objective was to assess whether parental polygenic risk score (PRS) may act as a proxy for offspring PRS in studies lacking offspring genetic data.

METHODS

A total of 8,561 parent-offspring (age 13-19 years) trios from the Trøndelag Health Study (the HUNT Study) were included, of which, 1,286 adolescents had available genetic data. Weighted parental PRSs from 900 single-nucleotide polymorphisms robustly associated with adult BMI were constructed and applied in linear mixed-effects models.

RESULTS

A positive association between parental PRS and offspring sex- and age-adjusted BMI (iso-BMI) throughout adolescence was identified. The estimated marginal effects per standard deviation increase in parental PRS were 0.26 (95% CI: 0.18-0.33), 0.36 (95% CI: 0.29-0.43), and 0.62 kg/m² (95% CI: 0.51-0.72) for maternal, paternal, and combined parental PRS, respectively. In subsample analyses, the magnitude of association of the parental PRS versus offspring PRS with iso-BMI in adolescents was similar.

CONCLUSIONS

Parental PRS was consistently associated with offspring iso-BMI throughout adolescence. Results from subsample analyses support the use of parental PRS of obesity as a proxy for adolescent PRS in the absence of offspring genetic data.

Genetic basis and molecular biology of cardiac arrhythmias in cardiomyopathies

Cardiac arrhythmias are common, often the first, and sometimes the life-threatening manifestations of hereditary cardiomyopathies. Pathogenic variants in several genes known to cause hereditary cardiac arrhythmias have also been identified in the sporadic cases and small families with cardiomyopathies. These findings suggest a shared genetic aetiology of a subset of hereditary cardiomyopathies and cardiac arrhythmias. The concept of a shared genetic aetiology is in accord with the complex and exquisite interplays that exist between the ion currents and cardiac mechanical function. However, neither the causal role of cardiac arrhythmias genes in cardiomyopathies is well established nor the causal role of cardiomyopathy genes in arrhythmias. On the contrary, secondary changes in ion currents, such as post-translational modifications, are common and contributors to the pathogenesis of arrhythmias in cardiomyopathies through altering biophysical and functional properties of the ion channels. Moreover, structural changes, such as cardiac hypertrophy, dilatation, and fibrosis provide a pro-arrhythmic substrate in hereditary cardiomyopathies. Genetic basis and molecular biology of cardiac arrhythmias in hereditary cardiomyopathies are discussed.

Previous Catheter Ablation Predicts In-Hospital Restoration of Sinus Rhythm in Patients Presenting with Recent-Onset Atrial Fibrillation-The Retrospective HAMBURG-AF Study

Background and Objectives: Atrial fibrillation (AF) is the most common arrythmia of the human heart. Patients mostly present highly symptomatic with dyspnea and tachycardia and have a disproportionate risk of developing heart failure or stroke events. We aimed to evaluate the determinants of early conversion into sinus rhythm during initial stay at the emergency department of a large tertiary care center. Materials and Methods: A total of 1384 subjects with recent-onset AF were recruited between October 2014 and April 2017. Patients with longstanding AF were excluded, resulting in a total of 935 patients for the present analysis. Results: In multivariate adjusted logistic regression analyses, previous catheter ablation therapy was a strong predictor of conversion in sinus rhythm during the stay in the emergency department, with an odds ratio (OR) of 3.87 (95% CI 2.40, 6.54; p < 0.001). In contrast, existing antiarrhythmic medication showed no association with facilitated conversion [OR 0.89 (95%CI 0.65, 1.20); p = 0.44]. Likewise, conventional cardiovascular risk factors (hypertension, dyslipidemia, diabetes) were also not associated with conversion during hospital stay. Conclusion: This is the first report on the relevance of previous ablation therapy for early restoration of sinus rhythm in recent-onset AF. Although catheter ablation is associated with relevant risk of late recurrence of atrial fibrillation, it seems to have a large benefit for patients with recent-onset AF.

Transcriptome and proteome mapping in the sheep atria reveal molecular featurets of atrial fibrillation progression

AIMS

Atrial fibrillation (AF) is a progressive cardiac arrhythmia that increases the risk of hospitalization and adverse cardiovascular events. There is a clear demand for more inclusive and large-scale approaches to understand the molecular drivers responsible for AF, as well as the fundamental mechanisms governing the transition from paroxysmal to persistent and permanent forms. In this study, we aimed to create a molecular map of AF and find the distinct molecular programmes underlying cell type-specific atrial remodelling and AF progression.

METHODS AND RESULTS

We used a sheep model of long-standing, tachypacing-induced AF, sampled right and left atrial tissue, and isolated cardiomyocytes (CMs) from control, intermediate (transition), and late time points during AF progression, and performed transcriptomic and proteome profiling. We have merged all these layers of information into a meaningful three-component space in which we explored the genes and proteins detected and their common patterns of expression. Our data-driven analysis points at extracellular matrix remodelling, inflammation, ion channel, myofibril structure, mitochondrial complexes, chromatin remodelling, and genes related to neural function, as well as critical regulators of cell proliferation as hallmarks of AF progression. Most important, we prove that these changes occur at early transitional stages of the disease, but not at later stages, and that the left atrium undergoes significantly more profound changes than the right atrium in its expression programme. The pattern of dynamic changes in gene and protein expression replicate the electrical and structural remodelling demonstrated previously in the sheep and in humans, and uncover novel mechanisms potentially relevant for disease treatment.

CONCLUSIONS

Transcriptomic and proteomic analysis of AF progression in a large animal model shows that significant changes occur at early stages, and that among others involve previously undescribed increase in mitochondria, changes to the chromatin of atrial CMs, and genes related to neural function and cell proliferation.

Genetic insight into sick sinus syndrome

Aims

The aim of this study was to use human genetics to investigate the pathogenesis of sick sinus syndrome (SSS) and the role of risk factors in its development.

Methods and results

We performed a genome-wide association study of 6469 SSS cases and 1 000 187 controls from deCODE genetics, the Copenhagen Hospital Biobank, UK Biobank, and the HUNT study. Variants at six loci associated with SSS, a reported missense variant in MYH6, known atrial fibrillation (AF)/electrocardiogram variants at PITX2, ZFHX3, TTN/CCDC141, and SCN10A and a low-frequency (MAF = 1.1–1.8%) missense variant, p.Gly62Cys in KRT8 encoding the intermediate filament protein keratin 8. A full genotypic model best described the p.Gly62Cys association (P = 1.6 × 10⁻²⁰), with an odds ratio (OR) of 1.44 for heterozygotes and a disproportionally large OR of 13.99 for homozygotes. All the SSS variants increased the risk of pacemaker implantation. Their association with AF varied and p.Gly62Cys was the only variant not associating with any other arrhythmia or cardiovascular disease. We tested 17 exposure phenotypes in polygenic score (PGS) and Mendelian randomization analyses. Only two associated with the risk of SSS in Mendelian randomization, AF, and lower heart rate, suggesting causality. Powerful PGS analyses provided convincing evidence against causal associations for body mass index, cholesterol, triglycerides, and type 2 diabetes (P > 0.05).

Conclusion

We report the associations of variants at six loci with SSS, including a missense variant in KRT8 that confers high risk in homozygotes and points to a mechanism specific to SSS development. Mendelian randomization supports a causal role for AF in the development of SSS.

Functional genomics and epigenomics of atrial fibrillation

Atrial fibrillation is a progressive cardiac arrhythmia that increases the risk of hospitalization and adverse cardiovascular events. Despite years of study, we still do not have a full comprehension of the molecular mechanism responsible for the disease. The recent implementation of large-scale approaches in both patient samples, population studies and animal models has helped us to broaden our knowledge on the molecular drivers responsible for AF and on the mechanisms behind disease progression. Understanding genomic and epigenomic changes that take place during chronification of AF will prove essential to design novel treatments leading to improved patient care.

Atrial fibrillation and left atrial size and function: a Mendelian randomization study

Atrial fibrillation (AF) patients have enlarged left atria (LA), but prior studies suggested enlarged atria as both cause and consequence of AF. The aim of this study is to investigate the causal association between AF and LA size and function. In the UK Biobank, all individuals with contoured cardiovascular magnetic resonance data were selected. LA maximal volume (LA max), LA minimal volume (LA min), LA stroke volume and LA ejection fraction were measured and indexed to body surface area (BSA). Two-sample Mendelian randomization analyses were performed using 84 of the known genetic variants associated with AF to assess the association with all LA size and function in individuals without prevalent AF. A total of 4274 individuals (mean age 62.0 ± 7.5 years, 53.2% women) were included. Mendelian randomization analyses estimated a causal effect between genetically determined AF and BSA-indexed LA max, LA min, and LA ejection fraction, but not between AF and LA stroke volume. Leave-one-out analyses showed that the causal associations were attenuated after exclusion of rs67249485, located near PITX2 gene. Our results suggest that AF causally increases LA size and decreases LA ejection fraction. The AF risk allele of rs67249485, located near the PITX2 gene, contributes strongly to these associations.

Role of genetics in atrial fibrillation management

Atrial fibrillation (AF) management has significantly improved during the career of professor Crijns. Research was implemented into guidelines and clinical practice. However, despite advances in AF management, large differences between individual treatment responses still exist and the mechanisms underlying initiation and perpetuation of AF are not completely understood. International collaborations have revealed the genetic contribution to AF and steps towards improving AF management are being made. In this short review, the most important paradigms shifts in the field of AF genetics are recognized and the future role of genetics in personalized management of AF is discussed.

Loss-of-Function Variants in the SYNPO2L Gene Are Associated With Atrial Fibrillation

Multiple genome-wide association studies (GWAS) have identified numerous loci associated with atrial fibrillation (AF). However, the genes driving these associations and how they contribute to the AF pathogenesis remains poorly understood. To identify genes likely to be driving the observed association, we searched the FinnGen study consisting of 12,859 AF cases and 73,341 controls for rare genetic variants predicted to cause loss-of-function. A specific splice site variant was found in the SYNPO2L gene, located in an AF associated locus on chromosome 10. This variant was associated with an increased risk of AF with a relatively high odds ratio of 3.5 (p = 9.9 × 10-8). SYNPO2L is an important gene involved in the structural development and function of the cardiac myocyte and our findings thus support the recent suggestions that AF can present as atrial cardiomyopathy.

Malignant Arrhythmogenic Role Associated with RBM20: A Comprehensive Interpretation Focused on a Personalized Approach

The RBM20 gene encodes the muscle-specific splicing factor RNA-binding motif 20, a regulator of heart-specific alternative splicing. Nearly 40 potentially deleterious variants in RBM20 have been reported in the last ten years, being found to be associated with highly arrhythmogenic events in familial dilated cardiomyopathy. Frequently, malignant arrhythmias can be a primary manifestation of disease. The early recognition of arrhythmic genotypes is crucial in avoiding lethal episodes, as it may have an impact on the adoption of personalized preventive measures. Our study performs a comprehensive update of data concerning rare variants in RBM20 that are associated with malignant arrhythmogenic phenotypes with a focus on personalized medicine.

Epidemiology of Atrial Fibrillation: Geographic/Ecological Risk Factors, Age, Sex, Genetics

Atrial fibrillation is the most common arrhythmia globally. The global prevalence of atrial fibrillation is positively correlated with the sociodemographic index of different regions. Advancing age, male sex, and Caucasian race are risk factors; female sex is correlated with higher atrial fibrillation mortality worldwide likely owing to thromboembolic risk. African American ethnicity is associated with lower atrial fibrillation risk, same as Asian and Hispanic/Latino ethnicities compared with Caucasians. Atrial fibrillation may be heritable, and more than 100 genetic loci have been identified. A polygenic risk score and clinical risk factors are feasible and effective in risk stratification of incident disease.

Loss-of-function genomic variants highlight potential therapeutic targets for cardiovascular disease

Pharmaceutical drugs targeting dyslipidemia and cardiovascular disease (CVD) may increase the risk of fatty liver disease and other metabolic disorders. To identify potential novel CVD drug targets without these adverse effects, we perform genome-wide analyses of participants in the HUNT Study in Norway (n = 69,479) to search for protein-altering variants with beneficial impact on quantitative blood traits related to cardiovascular disease, but without detrimental impact on liver function. We identify 76 (11 previously unreported) presumed causal protein-altering variants associated with one or more CVD- or liver-related blood traits. Nine of the variants are predicted to result in loss-of-function of the protein. This includes ZNF529:p.K405X, which is associated with decreased low-density-lipoprotein (LDL) cholesterol (P = 1.3 × 10-8) without being associated with liver enzymes or non-fasting blood glucose. Silencing of ZNF529 in human hepatoma cells results in upregulation of LDL receptor and increased LDL uptake in the cells. This suggests that inhibition of ZNF529 or its gene product should be prioritized as a novel candidate drug target for treating dyslipidemia and associated CVD.

Loss-of-function genomic variants highlight potential therapeutic targets for cardiovascular disease

Pharmaceutical drugs targeting dyslipidemia and cardiovascular disease (CVD) may increase the risk of fatty liver disease and other metabolic disorders. To identify potential novel CVD drug targets without these adverse effects, we perform genome-wide analyses of participants in the HUNT Study in Norway (n = 69,479) to search for protein-altering variants with beneficial impact on quantitative blood traits related to cardiovascular disease, but without detrimental impact on liver function. We identify 76 (11 previously unreported) presumed causal protein-altering variants associated with one or more CVD- or liver-related blood traits. Nine of the variants are predicted to result in loss-of-function of the protein. This includes ZNF529:p.K405X, which is associated with decreased low-density-lipoprotein (LDL) cholesterol (P = 1.3 × 10^-8) without being associated with liver enzymes or non-fasting blood glucose. Silencing of ZNF529 in human hepatoma cells results in upregulation of LDL receptor and increased LDL uptake in the cells. This suggests that inhibition of ZNF529 or its gene product should be prioritized as a novel candidate drug target for treating dyslipidemia and associated CVD.

Atrial fibrillation-a complex polygenetic disease

Atrial fibrillation (AF) is the most common type of arrhythmia. Epidemiological studies have documented a substantial genetic component. More than 160 genes have been associated with AF during the last decades. Some of these were discovered by classical linkage studies while the majority relies on functional studies or genome-wide association studies. In this review, we will evaluate the genetic basis of AF and the role of both common and rare genetic variants in AF. Rare variants in multiple ion-channel genes as well as gap junction and transcription factor genes have been associated with AF. More recently, a growing body of evidence has implicated structural genes with AF. An increased burden of atrial fibrosis in AF patients compared with non-AF patients has also been reported. These findings challenge our traditional understanding of AF being an electrical disease. We will focus on several quantitative landmark papers, which are transforming our understanding of AF by implicating atrial cardiomyopathies in the pathogenesis. This new AF research field may enable better diagnostics and treatment in the future.

Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals

Genetic studies of blood pressure (BP) to date have mainly analyzed common variants (minor allele frequency > 0.05). In a meta-analysis of up to ~1.3 million participants, we discovered 106 new BP-associated genomic regions and 87 rare (minor allele frequency ≤ 0.01) variant BP associations (P < 5 × 10-8), of which 32 were in new BP-associated loci and 55 were independent BP-associated single-nucleotide variants within known BP-associated regions. Average effects of rare variants (44% coding) were ~8 times larger than common variant effects and indicate potential candidate causal genes at new and known loci (for example, GATA5 and PLCB3). BP-associated variants (including rare and common) were enriched in regions of active chromatin in fetal tissues, potentially linking fetal development with BP regulation in later life. Multivariable Mendelian randomization suggested possible inverse effects of elevated systolic and diastolic BP on large artery stroke. Our study demonstrates the utility of rare-variant analyses for identifying candidate genes and the results highlight potential therapeutic targets.

Epigenomic and Transcriptomic Dynamics During Human Heart Organogenesis

RATIONALE

There is growing evidence that common variants and rare sequence alterations in regulatory sequences can result in birth defects or predisposition to disease. Congenital heart defects are the most common birth defect and have a clear genetic component, yet only a third of cases can be attributed to structural variation in the genome or a mutation in a gene. The remaining unknown cases could be caused by alterations in regulatory sequences.

OBJECTIVE

Identify regulatory sequences and gene expression networks that are active during organogenesis of the human heart. Determine whether these sites and networks are enriched for disease-relevant genes and associated genetic variation.

METHODS AND RESULTS

We characterized ChromHMM (chromatin state) and gene expression dynamics during human heart organogenesis. We profiled 7 histone modifications in embryonic hearts from each of 9 distinct Carnegie stages (13-14, 16-21, and 23), annotated chromatin states, and compared these maps to over 100 human tissues and cell types. We also generated RNA-sequencing data, performed differential expression, and constructed weighted gene coexpression networks. We identified 177 412 heart enhancers; 12 395 had not been previously annotated as strong enhancers. We identified 92% of all functionally validated heart-positive enhancers (n=281; 7.5× enrichment; P<2.2×10-16). Integration of these data demonstrated novel heart enhancers are enriched near genes expressed more strongly in cardiac tissue and are enriched for variants associated with ECG measures and atrial fibrillation. Our gene expression network analysis identified gene modules strongly enriched for heart-related functions, regulatory control by heart-specific enhancers, and putative disease genes.

CONCLUSIONS

Well-connected hub genes with heart-specific expression targeted by embryonic heart-specific enhancers are likely disease candidates. Our functional annotations will allow for better interpretation of whole genome sequencing data in the large number of patients affected by congenital heart defects.

Genetics of height and risk of atrial fibrillation: A Mendelian randomization study

BACKGROUND

Observational studies have identified height as a strong risk factor for atrial fibrillation, but this finding may be limited by residual confounding. We aimed to examine genetic variation in height within the Mendelian randomization (MR) framework to determine whether height has a causal effect on risk of atrial fibrillation.

METHODS AND FINDINGS

In summary-level analyses, MR was performed using summary statistics from genome-wide association studies of height (GIANT/UK Biobank; 693,529 individuals) and atrial fibrillation (AFGen; 65,446 cases and 522,744 controls), finding that each 1-SD increase in genetically predicted height increased the odds of atrial fibrillation (odds ratio [OR] 1.34; 95% CI 1.29 to 1.40; p = 5 × 10-42). This result remained consistent in sensitivity analyses with MR methods that make different assumptions about the presence of pleiotropy, and when accounting for the effects of traditional cardiovascular risk factors on atrial fibrillation. Individual-level phenome-wide association studies of height and a height genetic risk score were performed among 6,567 European-ancestry participants of the Penn Medicine Biobank (median age at enrollment 63 years, interquartile range 55-72; 38% female; recruitment 2008-2015), confirming prior observational associations between height and atrial fibrillation. Individual-level MR confirmed that each 1-SD increase in height increased the odds of atrial fibrillation, including adjustment for clinical and echocardiographic confounders (OR 1.89; 95% CI 1.50 to 2.40; p = 0.007). The main limitations of this study include potential bias from pleiotropic effects of genetic variants, and lack of generalizability of individual-level findings to non-European populations.

CONCLUSIONS

In this study, we observed evidence that height is likely a positive causal risk factor for atrial fibrillation. Further study is needed to determine whether risk prediction tools including height or anthropometric risk factors can be used to improve screening and primary prevention of atrial fibrillation, and whether biological pathways involved in height may offer new targets for treatment of atrial fibrillation.

Annotation of susceptibility SNPs associated with atrial fibrillation

OBJECTIVE

Genome-wide association studies (GWAS) and the candidate gene based association studies have identified a panel of variants associated with atrial fibrillation (AF), however, most of the identified single nucleotide polymorphisms (SNPs) were found located within intergenic or intronic genomic regions, and whether the positive SNPs have a real biological function is unknown, and the real disease causing gene need to be studied.

RESULTS

The current results of the genetic studies including common variants identified by GWAS (338 index SNPs) and candidate gene based association studies (40 SNPs) were summarized.

CONCLUSION

Our study suggests the relationship between genetic variants and possible targeted genes, and provides insight into potential genetic pathways underlying AF incidence and development. The results may provide an encyclopedia of AF susceptibility SNPs and shed light on the functional mechanisms of AF variants identified through genetic studies.

METHODS

We summarized AF susceptibility SNPs identified by GWAS and candidate gene based association studies, and give a comprehensive functional annotation of all these AF susceptibility loci. by genomic annotation, microRNA binding prediction, promoter activity analysis, enhancer activity analysis, transcription factors binding activity prediction, expression quantitative trait loci (eQTL) analysis, long-range transcriptional regulatory function analysis, gene ontology and pathway enrichment analysis.

ISL1 loss-of-function variation causes familial atrial fibrillation

Atrial fibrillation (AF) represents the most frequent form of sustained cardiac rhythm disturbance, affecting approximately 1% of the general population worldwide, and confers a substantially enhanced risk of cerebral stroke, heart failure, and death. Increasing epidemiological studies have clearly demonstrated a strong genetic basis for AF, and variants in a wide range of genes, including those coding for ion channels, gap junction channels, cardiac structural proteins and transcription factors, have been identified to underlie AF. Nevertheless, the genetic pathogenesis of AF is complex and still far from completely understood. Here, whole-exome sequencing and bioinformatics analyses of a three-generation family with AF were performed, and after filtering variants by multiple metrics, we identified a heterozygous variant in the ISL1 gene (encoding a transcription factor critical for embryonic cardiogenesis and postnatal cardiac remodeling), NM_002202.2: c.481G > T; p.(Glu161*), which was validated by Sanger sequencing and segregated with autosome-dominant AF in the family with complete penetrance. The nonsense variant was absent from 284 unrelated healthy individuals used as controls. Functional assays with a dual-luciferase reporter assay system revealed that the truncating ISL1 protein lost transcriptional activation on the verified target genes MEF2C and NKX2-5. Additionally, the variant nullified the synergistic transactivation between ISL1 and TBX5 as well as GATA4, two other transcription factors that have been implicated in AF. The findings suggest ISL1 as a novel gene contributing to AF, which adds new insight to the genetic mechanisms underpinning AF, implying potential implications for genetic testing and risk stratification of the AF family members.

Thyroid Function Affects the Risk of Stroke via Atrial Fibrillation: A Mendelian Randomization Study

CONTEXT

Observational studies suggest that variations in normal range thyroid function are associated with cardiovascular diseases. However, it remains to be determined whether these associations are causal or not.

OBJECTIVE

To test whether genetically determined variation in normal range thyroid function is causally associated with the risk of stroke and coronary artery disease (CAD) and investigate via which pathways these relations may be mediated.

DESIGN, SETTING, AND PARTICIPANTS

Mendelian randomization analyses for stroke and CAD using genetic instruments associated with normal range thyrotropin (TSH) and free thyroxine levels or Hashimoto's thyroiditis and Graves' disease. The potential mediating role of known stroke and CAD risk factors was examined. Publicly available summary statistics data were used.

MAIN OUTCOME MEASURES

Stroke or CAD risk per genetically predicted increase in TSH or FT4 levels.

RESULTS

A 1 standard deviation increase in TSH was associated with a 5% decrease in the risk of stroke (odds ratio [OR], 0.95; 95% confidence interval [CI], 0.91-0.99; P = 0.008). Multivariable MR analyses indicated that this effect is mainly mediated via atrial fibrillation. MR analyses did not show a causal association between normal range thyroid function and CAD. Secondary analyses showed a causal relationship between Hashimoto's thyroiditis and a 7% increased risk of CAD (OR, 1.07; 95% CI, 1.01-1.13; P = 0.026), which was mainly mediated via body mass index.

CONCLUSION

These results provide important new insights into the causal relationships and mediating pathways between thyroid function, stroke, and CAD. We identify variation in normal range thyroid function and Hashimoto's thyroiditis as risk factors for stroke and CAD, respectively.

The molecular genetic basis of atrial fibrillation

As the most common cardiac arrhythmia, atrial fibrillation (AF) is a major risk factor for stroke, heart failure, and premature death with considerable associated costs. However, no available treatment options have optimal benefit-harm profiles currently, reflecting an incomplete understanding of the biological mechanisms underlying this complex arrhythmia. Recently, molecular epidemiological studies, especially genome-wide association studies, have emphasized the substantial genetic component of AF etiology. A comprehensive mapping of the genetic underpinnings for AF can expand our knowledge of AF mechanism and further facilitate the process of locating novel therapeutics for AF. Here we provide a state-of-the-art review of the molecular genetics of AF incorporating evidence from linkage analysis and candidate gene, as well as genome-wide association studies of common variations and rare copy number variations; potential epigenetic modifications (e.g., DNA methylation, histone modification, and non-coding RNAs) are also involved. We also outline the challenges in mechanism investigation and potential future directions in this article.

Development and validation of a prediction model for incident hand osteoarthritis in the HUNT study

OBJECTIVE

To develop and externally validate prediction models for incident hand osteoarthritis (OA) in a large population-based cohort of middle aged and older men and women.

DESIGN

We included 17,153 men and 18,682 women from a population-based cohort, aged 35-70 years at baseline (1995-1997). Incident hand OA were obtained from diagnostic codes in the Norwegian National Patient Register (1995-2018). We studied whether a range of self-reported and clinically measured predictors could predict hand OA, using the Area Under the receiver-operating Curve (AUC) from logistic regression. External validation of an existing prediction model for male hand OA was tested on discrimination in a sample of men. Bootstrapping was used to avoid overfitting.

RESULTS

The model for men showed modest discriminatory ability (AUC = 0.67, 95% CI 0.62-0.71). Adding a genetic risk score did not improve prediction. Similar discrimination was observed in the model for women (AUC = 0.62, 95% CI 0.59-0.64). Prediction was not improved by adding a genetic risk score or hormonal and reproductive factors. Applying external validation, similar results were observed among men in HUNT (The Nord-Trøndelag Health Study) as in the developmental sample (AUC = 0.62, 95% CI 0.57-0.65).

CONCLUSION

We developed prediction models for incident hand OA in men and women. For women, the model included body mass index (BMI), heavy physical work, high physical activity and perceived poor health. The model showed moderate discrimination. For men, we have shown that a prediction model including BMI, education and information on sleep can predict incident hand OA in several populations with moderate discriminative ability.

Genetics of Atrial Fibrillation in 2020: GWAS, Genome Sequencing, Polygenic Risk, and Beyond

Atrial fibrillation is a common heart rhythm disorder that leads to an increased risk for stroke and heart failure. Atrial fibrillation is a complex disease with both environmental and genetic risk factors that contribute to the arrhythmia. Over the last decade, rapid progress has been made in identifying the genetic basis for this common condition. In this review, we provide an overview of the primary types of genetic analyses performed for atrial fibrillation, including linkage studies, genome-wide association studies, and studies of rare coding variation. With these results in mind, we aim to highlighting the existing knowledge gaps and future directions for atrial fibrillation genetics research.

A New Therapeutic Framework for Atrial Fibrillation Drug Development

Atrial fibrillation (AF) is a highly prevalent cardiac arrhythmia and cause of significant morbidity and mortality. Its increasing prevalence in aging societies constitutes a growing challenge to global healthcare systems. Despite substantial unmet needs in AF prevention and treatment, drug developments hitherto have been challenging, and the current pharmaceutical pipeline is nearly empty. In this review, we argue that current drugs for AF are inadequate because of an oversimplified system for patient classification and the development of drugs that do not interdict underlying disease mechanisms. We posit that an improved understanding of AF molecular pathophysiology related to the continuous identification of novel disease-modifying drug targets and an increased appreciation of patient heterogeneity provide a new framework to personalize AF drug development. Together with recent innovations in diagnostics, remote rhythm monitoring, and big data capabilities, we anticipate that adoption of a new framework for patient subsegmentation based on pathophysiological, genetic, and molecular subsets will improve success rates of clinical trials and advance drugs that reduce the individual patient and public health burden 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.

Mitochondrial genome-wide association study of migraine - the HUNT Study

BACKGROUND

Variation in mitochondrial DNA (mtDNA) has been indicated in migraine pathogenesis, but genetic studies to date have focused on candidate variants, with sparse findings. We aimed to perform the first mitochondrial genome-wide association study of migraine, examining both single variants and mitochondrial haplogroups.

METHODS

In total, 71,860 participants from the population-based Nord-Trøndelag Health Study were genotyped. We excluded samples not passing quality control for nuclear genotypes, in addition to samples with low call rate and closely maternally related. We analysed 775 mitochondrial DNA variants in 4021 migraine cases and 14,288 headache-free controls, using logistic regression. In addition, we analysed 3831 cases and 13,584 controls who could be reliably assigned to a mitochondrial haplogroup. Lastly, we attempted to replicate previously reported mitochondrial DNA candidate variants.

RESULTS

Neither of the mitochondrial variants or haplogroups were associated with migraine. In addition, none of the previously reported mtDNA candidate variants replicated in our data.

CONCLUSIONS

Our findings do not support a major role of mitochondrial genetic variation in migraine pathophysiology, but a larger sample is needed to detect rare variants and future studies should also examine heteroplasmic variation, epigenetic changes and copy-number variation.

Integrative Omics Approach to Identifying Genes Associated With Atrial Fibrillation

Rationale: GWAS (Genome-Wide Association Studies) have identified hundreds of genetic loci associated with atrial fibrillation (AF). However, these loci explain only a small proportion of AF heritability. Objective: To develop an approach to identify additional AF-related genes by integrating multiple omics data. Methods and Results: Three types of omics data were integrated: (1) summary statistics from the AFGen 2017 GWAS; (2) a whole blood EWAS (Epigenome-Wide Association Study) of AF; and (3) a whole blood TWAS (Transcriptome-Wide Association Study) of AF. The variant-level GWAS results were collapsed into gene-level associations using fast set-based association analysis. The CpG-level EWAS results were also collapsed into gene-level associations by an adapted SNP-set Kernel Association Test approach. Both GWAS and EWAS gene-based associations were then meta-analyzed with TWAS using a fixed-effects model weighted by the sample size of each data set. A tissue-specific network was subsequently constructed using the NetWAS (Network-Wide Association Study). The identified genes were then compared with the AFGen 2018 GWAS that contained more than triple the number of AF cases compared with AFGen 2017 GWAS. We observed that the multiomics approach identified many more relevant AF-related genes than using AFGen 2018 GWAS alone (1931 versus 206 genes). Many of these genes are involved in the development and regulation of heart- and muscle-related biological processes. Moreover, the gene set identified by multiomics approach explained much more AF variance than those identified by GWAS alone (10.4% versus 3.5%). Conclusions: We developed a strategy to integrate multiple omics data to identify AF-related genes. Our integrative approach may be useful to improve the power of traditional GWAS, which might be particularly useful for rare traits and diseases with limited sample size.

Identification of atrial fibrillation associated genes and functional non-coding variants

Disease-associated genetic variants that lie in non-coding regions found by genome-wide association studies are thought to alter the functionality of transcription regulatory elements and target gene expression. To uncover causal genetic variants, variant regulatory elements and their target genes, here we cross-reference human transcriptomic, epigenomic and chromatin conformation datasets. Of 104 genetic variant regions associated with atrial fibrillation candidate target genes are prioritized. We optimize EMERGE enhancer prediction and use accessible chromatin profiles of human atrial cardiomyocytes to more accurately predict cardiac regulatory elements and identify hundreds of sub-threshold variants that co-localize with regulatory elements. Removal of mouse homologues of atrial fibrillation-associated regions in vivo uncovers a distal regulatory region involved in Gja1 (Cx43) expression. Our analyses provide a shortlist of genes likely affected by atrial fibrillation-associated variants and provide variant regulatory elements in each region that link genetic variation and target gene regulation, helping to focus future investigations.

Allele-specific NKX2-5 binding underlies multiple genetic associations with human electrocardiographic traits

The cardiac transcription factor (TF) gene NKX2-5 has been associated with electrocardiographic (EKG) traits through genome-wide association studies (GWASs), but the extent to which differential binding of NKX2-5 at common regulatory variants contributes to these traits has not yet been studied. We analyzed transcriptomic and epigenomic data from induced pluripotent stem cell-derived cardiomyocytes from seven related individuals, and identified ~2,000 single-nucleotide variants associated with allele-specific effects (ASE-SNVs) on NKX2-5 binding. NKX2-5 ASE-SNVs were enriched for altered TF motifs, for heart-specific expression quantitative trait loci and for EKG GWAS signals. Using fine-mapping combined with epigenomic data from induced pluripotent stem cell-derived cardiomyocytes, we prioritized candidate causal variants for EKG traits, many of which were NKX2-5 ASE-SNVs. Experimentally characterizing two NKX2-5 ASE-SNVs (rs3807989 and rs590041) showed that they modulate the expression of target genes via differential protein binding in cardiac cells, indicating that they are functional variants underlying EKG GWAS signals. Our results show that differential NKX2-5 binding at numerous regulatory variants across the genome contributes to EKG phenotypes.

Atrial Fibrillation Genetics Update: Toward Clinical Implementation

Atrial fibrillation (AF) is the most common heart rhythm disorder worldwide and may have serious cardiovascular health consequences. AF is associated with increased risk of stroke, dementia, heart failure, and death. There are several known robust, clinical risk predictors for AF, such as male sex, increasing age, and hypertension; however, during the last couple of decades, a substantive genetic component has also been established. Over the last 10 years, the discovery of novel AF-related genetic variants has accelerated, increasing our understanding of mechanisms behind AF. Current studies are focusing on mapping the polygenic structure of AF, improving risk prediction, therapeutic development, and patient-specific management. Nevertheless, it is still difficult for clinicians to interpret the role of genetics in AF prediction and management. Here, we provide an overview of relevant topics within the genetics of AF and attempt to provide some guidance on how to interpret genetic advances and their implementation into clinical decision-making.

Differentially expressed genes for atrial fibrillation identified by RNA sequencing from paired human left and right atrial appendages

Atrial fibrillation is a significant worldwide contributor to cardiovascular morbidity and mortality. Few studies have investigated the differences in gene expression between the left and right atrial appendages, leaving their characterization largely unexplored. In this study, differential gene expression was investigated in atrial fibrillation and sinus rhythm using left and right atrial appendages from the same patients. RNA sequencing was performed on the left and right atrial appendages from five sinus rhythm (SR) control patients and five permanent AF case patients. Differential gene expression in both the left and right atrial appendages was analyzed using the Bioconductor package edgeR. A selection of differentially expressed genes, with relevance to atrial fibrillation, were further validated using quantitative RT-PCR. The distribution of the samples assessed through principal component analysis showed distinct grouping between left and right atrial appendages and between SR controls and AF cases. Overall 157 differentially expressed genes were identified to be downregulated and 90 genes upregulated in AF. Pathway enrichment analysis indicated a greater involvement of left atrial genes in the Wnt signaling pathway whereas right atrial genes were involved in clathrin-coated vesicle and collagen formation. The differing expression of genes in both left and right atrial appendages indicate that there are different mechanisms for development, support and remodeling of AF within the left and right atria.