Common and Rare Coding Genetic Variation Underlying the Electrocardiographic PR Interval

Genetic architecture of cardiac dynamic flow volumes

Cardiac blood flow is a critical determinant of human health. However, the definition of its genetic architecture is limited by the technical challenge of capturing dynamic flow volumes from cardiac imaging at scale. We present DeepFlow, a deep-learning system to extract cardiac flow and volumes from phase-contrast cardiac magnetic resonance imaging. A mixed-linear model applied to 37,653 individuals from the UK Biobank reveals genome-wide significant associations across cardiac dynamic flow volumes spanning from aortic forward velocity to aortic regurgitation fraction. Mendelian randomization reveals a causal role for aortic root size in aortic valve regurgitation. Among the most significant contributing variants, localizing genes (near ELN, PRDM6 and ADAMTS7) are implicated in connective tissue and blood pressure pathways. Here we show that DeepFlow cardiac flow phenotyping at scale, combined with genotyping data, reinforces the contribution of connective tissue genes, blood pressure and root size to aortic valve function.

Oligogenic Architecture of Rare Noncoding Variants Distinguishes 4 Congenital Heart Disease Phenotypes

BACKGROUND

Congenital heart disease (CHD) is highly heritable, but the power to identify inherited risk has been limited to analyses of common variants in small cohorts.

METHODS

We performed reimputation of 4 CHD cohorts (n=55 342) to the TOPMed reference panel (freeze 5), permitting meta-analysis of 14 784 017 variants including 6 035 962 rare variants of high imputation quality as validated by whole genome sequencing.

RESULTS

Meta-analysis identified 16 novel loci, including 12 rare variants, which displayed moderate or large effect sizes (median odds ratio, 3.02) for 4 separate CHD categories. Analyses of chromatin structure link 13 of the genome-wide significant loci to key genes in cardiac development; rs373447426 (minor allele frequency, 0.003 [odds ratio, 3.37 for Conotruncal heart disease]; P=1.49×10-8) is predicted to disrupt chromatin structure for 2 nearby genes BDH1 and DLG1 involved in Conotruncal development. A lead variant rs189203952 (minor allele frequency, 0.01 [odds ratio, 2.4 for left ventricular outflow tract obstruction]; P=1.46×10-8) is predicted to disrupt the binding sites of 4 transcription factors known to participate in cardiac development in the promoter of SPAG9. A tissue-specific model of chromatin conformation suggests that common variant rs78256848 (minor allele frequency, 0.11 [odds ratio, 1.4 for Conotruncal heart disease]; P=2.6×10-8) physically interacts with NCAM1 (PFDR=1.86×10-27), a neural adhesion molecule acting in cardiac development. Importantly, while each individual malformation displayed substantial heritability (observed h2 ranging from 0.26 for complex malformations to 0.37 for left ventricular outflow tract obstructive disease) the risk for different CHD malformations appeared to be separate, without genetic correlation measured by linkage disequilibrium score regression or regional colocalization.

CONCLUSIONS

We describe a set of rare noncoding variants conferring significant risk for individual heart malformations which are linked to genes governing cardiac development. These results illustrate that the oligogenic basis of CHD and significant heritability may be linked to rare variants outside protein-coding regions conferring substantial risk for individual categories of cardiac malformation.

Association between circulating leukocytes and arrhythmias: Mendelian randomization analysis in immuno-cardiac electrophysiology

Background

Cardiac arrhythmia is a common disease associated with high mortality and morbidity. Circulating leukocyte counts, which serve as a biomarker for assessing systemic immune status, have been linked to arrhythmias in observational studies. However, observational studies are plagued by confounding factors and reverse causality, whether alterations in circulating leukocyte components are causally associated with arrhythmias remains uncertain. The present study explored this question based on genetic evidence.

Methods and findings

We performed Mendelian randomization (MR) analysis to evaluate whether alterations in leukocyte counts affect aggregated risk of all types of arrhythmia or risk of five specific types of arrhythmia. Single-nucleotide polymorphisms serving as proxies for leukocyte differential counts were retrieved from the Blood Cell Consortium, and statistical data on arrhythmias were obtained from the UK Biobank), FinnGenand a meta-analysis of genome-wide association studies for atrial fibrillation. We applied inverse variance-weighted method as the primary analysis, complemented by a series of sensitivity analyses. Bidirectional analyses were conducted to assess reverse causality. Finally, multivariable MR was performed to study the joint effects of multiple risk factors. We found that genetically predicted differential leukocyte counts were not significantly associated with aggregated occurrence of all types of arrhythmia. In contrast, each 1-standard deviation increase in lymphocyte count was associated with 46% higher risk of atrioventricular block (OR 1.46, 95% CI 1.11–1.93, p=0.0065). A similar effect size was observed across all MR sensitivity analyses, with no evidence of horizontal pleiotropy. Reverse MR analysis suggested that atrioventricular block was unlikely to cause changes in lymphocyte count. Primary MR analysis based on the inverse-variance weighted method suggested that changes in neutrophil count alter risk of right bundle branch block, and changes in basophil count alter risk of atrial fibrillation. However, these causal relationships were not robust in sensitivity analyses. We found no compelling evidence that neutrophil or lymphocyte counts cause atrial fibrillation.

Conclusion

Our data support higher lymphocyte count as a causal risk factor for atrioventricular block. These results highlight the importance of immune cells in the pathogenesis of specific cardiac conduction disorders.

Model Embraced Electromechanical Coupling Time for Estimation of Heart Failure in Patients With Hypertrophic Cardiomyopathy

Objective

This study aimed to establish a model embraced electromechanical coupling time (EMC-T) and assess the value of the model for the prediction of heart failure (HF) in patients with hypertrophic cardiomyopathy (HCM).

Materials and Methods

Data on 82 patients with HCM at Shaanxi Provincial People’s Hospital between February 2019 and November 2021 were collected and then formed the training dataset (n = 82). Data were used to screen predictors of HF using univariate and multivariate analyses. Predictors were implemented to discover the optimal cut-off value, were incorporated into a model, and shown as a nomogram. The cumulative HF curve was calculated using the Kaplan–Meier method. Additionally, patients with HCM at other hospitals collected from March 2019 to March 2021 formed the validation dataset. The model’s performance was confirmed both in training and validation sets.

Results

During a median of 22.91 months, 19 (13.38%) patients experienced HF. Cox analysis showed that EMC-T courses in the lateral wall, myoglobin, PR interval, and left atrial volume index were independent predictors of HF in patients with HCM. Five factors were incorporated into the model and shown as a nomogram. Stratification of patients into two risk subgroups by applying risk score (<230.65, ≥230.65) allowed significant distinction between Kaplan–Meier curves for cumulative incidence of HF events. In training dataset, the model had an AUC of 0.948 (95% CI: 0.885–1.000, p < 0.001) and achieved a good C-index of 0.918 (95% CI: 0.867–0.969). In validation dataset, the model had an AUC of 0.991 (95% CI: 0.848–1.000, p < 0.001) and achieved a strong C-index of 0.941 (95% CI: 0.923–1.000). Calibration plots showed high agreement between predicted and observed outcomes in both two datasets.

Conclusion

We established and validated a novel model incorporating electromechanical coupling time courses for predicting HF in patients with HCM.

Considerations for the Assessment of Substrates, Genetics and Risk Factors in Patients with Atrial Fibrillation

Successful translation of research focussing on atrial arrhythmogenic mechanisms has potential to provide a mechanism-tailored classification and to support personalised treatment approaches in patients with AF. The clinical uptake and clinical implementation of new diagnostic techniques and treatment strategies require translational research approaches on various levels. Diagnostic translation involves the development of clinical diagnostic tools. Additionally, multidisciplinary teams are required for collaborative translation to describe genetic mechanisms, molecular pathways, electrophysiological characteristics and concomitant risk factors. In this article, current approaches for AF substrate characterisation, analysis of genes potentially involved in AF and strategies for AF risk factor assessment are summarised. The authors discuss challenges and obstacles to clinical translation and implementation into clinical practice.

Genetics and genomics of arrhythmic risk: current and future strategies to prevent sudden cardiac death

A genetic risk of sudden cardiac arrest and sudden death due to an arrhythmic cause, known as sudden cardiac death (SCD), has become apparent from epidemiological studies in the general population and in patients with ischaemic heart disease. However, genetic susceptibility to sudden death is greatest in young people and is associated with uncommon, monogenic forms of heart disease. Despite comprehensive pathology and genetic evaluations, SCD remains unexplained in a proportion of young people and is termed sudden arrhythmic death syndrome, which poses challenges to the identification of relatives from affected families who might be at risk of SCD. In this Review, we assess the current understanding of the epidemiology and causes of SCD and evaluate both the monogenic and the polygenic contributions to the risk of SCD in the young and SCD associated with drug therapy. Finally, we analyse the potential clinical role of genomic testing in the prevention of SCD in the general population.

MungeSumstats: A Bioconductor package for the standardisation and quality control of many GWAS summary statistics

Motivation

Genome‐wide association studies (GWAS) summary statistics have popularized and accelerated genetic research. However, a lack of standardization of the file formats used has proven problematic when running secondary analysis tools or performing meta-analysis studies.

Results

To address this issue, we have developed MungeSumstats, a Bioconductor R package for the standardization and quality control of GWAS summary statistics. MungeSumstats can handle the most common summary statistic formats, including variant call format (VCF) producing a reformatted, standardized, tabular summary statistic file, VCF or R native data object.

Availability and implementation

MungeSumstats is available on Bioconductor (v 3.13) and can also be found on Github at: https://neurogenomics.github.io/MungeSumstats.

Supplementary information

Supplementary data are available at Bioinformatics online.

Oligogenic Inheritance Underlying Incomplete Penetrance of PROKR2 Mutations in Hypogonadotropic Hypogonadism

The role of the prokineticin 2 pathway in human reproduction, olfactory bulb morphogenesis, and gonadotropin-releasing hormone secretion is well established. Recent studies have highlighted the implication of di/oligogenic inheritance in this disorder. In the present study, we aimed to identify the genetic mechanisms that could explain incomplete penetrance in hypogonadotropic hypogonadism (HH). This study involved two unrelated Tunisian patients with HH, which was triggered by identifying a homozygous p.(Pro290Ser) mutation in the PROKR2 gene in a girl (HH1) with Kallmann syndrome (KS). The functional effect of this variant has previously been well demonstrated. Unexpectedly, her unaffected father (HH1P) and brother (HH1F) also carried this genetic variation at a homozygous state. In the second family, we identified a heterozygous p.(Lys205del) mutation in PROKR2, both in a male patient with normosmic idiopathic IHH (HH12) and his asymptomatic mother. Whole-exome sequencing in the three HH1 family members allowed the identification of additional variants in the prioritized genes. We then carried out digenic combination predictions using the oligogenic resource for variant analysis (ORVAL) software. For HH1, we found the highest number of disease-causing variant pairs. Notably, a CCDC141 variant (c.2803C > T) was involved in 18 pathogenic digenic combinations. The CCDC141 variant acts in an autosomal recessive inheritance mode, based on the digenic effect prediction data. For the second patient (HH12), prediction by ORVAL allowed the identification of an interesting pathogenic digenic combination between DUSP6 and SEMA7A genes, predicted as “dual molecular diagnosis.” The SEMA7A variant p.(Glu436Lys) is novel and predicted as a VUS by Varsome. Sanger validation revealed the absence of this variant in the healthy mother. We hypothesize that disease expression in HH12 could be induced by the digenic transmission of the SEMA7A and DUSP6 variants or a monogenic inheritance involving only the SEMA7A VUS if further functional assays allow its reclassification into pathogenic. Our findings confirm that homozygous loss-of-function genetic variations are insufficient to cause KS, and that oligogenism is most likely the main transmission mode involved in Congenital Hypogonadotropic Hypogonadism.

Genome-wide association studies of cardiac electrical phenotypes

The genetic basis of cardiac electrical phenotypes has in the last 25 years been the subject of intense investigation. While in the first years, such efforts were dominated by the study of familial arrhythmia syndromes, in recent years, large consortia of investigators have successfully pursued genome-wide association studies (GWAS) for the identification of single-nucleotide polymorphisms that govern inter-individual variability in electrocardiographic parameters in the general population. We here provide a review of GWAS conducted on cardiac electrical phenotypes in the last 14 years and discuss the implications of these discoveries for our understanding of the genetic basis of disease susceptibility and variability in disease severity. Furthermore, we review functional follow-up studies that have been conducted on GWAS loci associated with cardiac electrical phenotypes and highlight the challenges and opportunities offered by such studies.

Rare Coding Variants Associated With Electrocardiographic Intervals Identify Monogenic Arrhythmia Susceptibility Genes: A Multi-Ancestry Analysis

BACKGROUND

Alterations in electrocardiographic (ECG) intervals are well-known markers for arrhythmia and sudden cardiac death (SCD) risk. While the genetics of arrhythmia syndromes have been studied, relations between electrocardiographic intervals and rare genetic variation at a population level are poorly understood.

METHODS

Using a discovery sample of 29 000 individuals with whole-genome sequencing from Trans-Omics in Precision Medicine and replication in nearly 100 000 with whole-exome sequencing from the UK Biobank and MyCode, we examined associations between low-frequency and rare coding variants with 5 routinely measured electrocardiographic traits (RR, P-wave, PR, and QRS intervals and corrected QT interval).

RESULTS

We found that rare variants associated with population-based electrocardiographic intervals identify established monogenic SCD genes (KCNQ1, KCNH2, and SCN5A), a controversial monogenic SCD gene (KCNE1), and novel genes (PAM and MFGE8) involved in cardiac conduction. Loss-of-function and pathogenic SCN5A variants, carried by 0.1% of individuals, were associated with a nearly 6-fold increased odds of the first-degree atrioventricular block (P=8.4×10^-5). Similar variants in KCNQ1 and KCNH2 (0.2% of individuals) were associated with a 23-fold increased odds of marked corrected QT interval prolongation (P=4×10^-25), a marker of SCD risk. Incomplete penetrance of such deleterious variation was common as over 70% of carriers had normal electrocardiographic intervals.

CONCLUSIONS

Our findings indicate that large-scale high-depth sequence data and electrocardiographic analysis identifies monogenic arrhythmia susceptibility genes and rare variants with large effects. Known pathogenic variation in conventional arrhythmia and SCD genes exhibited incomplete penetrance and accounted for only a small fraction of marked electrocardiographic interval prolongation.

Genome-first approach to rare EYA4 variants and cardio-auditory phenotypes in adults

While newborns and children with hearing loss are routinely offered genetic testing, adults are rarely clinically tested for a genetic etiology. One clinically actionable result from genetic testing in children is the discovery of variants in syndromic hearing loss genes. EYA4 is a known hearing loss gene which is also involved in important pathways in cardiac tissue. The pleiotropic effects of rare EYA4 variants are poorly understood and their prevalence in a large cohort has not been previously reported. We investigated cardio-auditory phenotypes in 11,451 individuals in a large biobank using a rare variant, genome-first approach to EYA4. We filtered 256 EYA4 variants carried by 6737 participants to 26 rare and predicted deleterious variants carried by 42 heterozygotes. We aggregated predicted deleterious EYA4 gene variants into a combined variable (i.e. "gene burden") and performed association studies across phenotypes compared to wildtype controls. We validated findings with replication in three independent cohorts and human tissue expression data. EYA4 gene burden was significantly associated with audiometric-proven HL (p = [Formula: see text], Mobitz Type II AV block (p = [Formula: see text]) and the syndromic presentation of HL and primary cardiomyopathy (p = 0.0194). Analyses on audiogram, echocardiogram, and electrocardiogram data validated these associations. Prior reports have focused on identifying variants in families with severe or syndromic phenotypes. In contrast, we found, using a genotype-first approach, that gene burden in EYA4 is associated with more subtle cardio-auditory phenotypes in an adult medical biobank population, including cardiac conduction disorders which have not been previously reported. We show the value of using a focused approach to uncover human disease related to pleiotropic gene variants and suggest a role for genetic testing in adults presenting with hearing loss.

GATA6 is a regulator of sinus node development and heart rhythm

The sinus node (SAN) is the primary pacemaker of the human heart, and abnormalities in its structure or function cause sick sinus syndrome, the most common reason for electronic pacemaker implantation. Here we report that transcription factor GATA6, whose mutations in humans are linked to arrhythmia, is highly expressed in the SAN and its haploinsufficiency in mice results in hypoplastic SANs and rhythm abnormalities. Cell-specific deletion reveals a requirement for GATA6 in various SAN lineages. Mechanistically, GATA6 directly activates key regulators of the SAN genetic program in conduction and nonconduction cells, such as TBX3 and EDN1, respectively. The data identify GATA6 as an important regulator of the SAN and provide a molecular basis for understanding the conduction abnormalities associated with GATA6 mutations in humans. They also suggest that GATA6 may be a potential modifier of the cardiac pacemaker.

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.

Genome-wide association meta-analysis of 30,000 samples identifies seven novel loci for quantitative ECG traits

Genome-wide association studies (GWAS) of quantitative electrocardiographic (ECG) traits in large consortia have identified more than 130 loci associated with QT interval, QRS duration, PR interval, and heart rate (RR interval). In the current study, we meta-analyzed genome-wide association results from 30,000 mostly Dutch samples on four ECG traits: PR interval, QRS duration, QT interval, and RR interval. SNP genotype data was imputed using the Genome of the Netherlands reference panel encompassing 19 million SNPs, including millions of rare SNPs (minor allele frequency < 5%). In addition to many known loci, we identified seven novel locus-trait associations: KCND3, NR3C1, and PLN for PR interval, KCNE1, SGIP1, and NFKB1 for QT interval, and ATP2A2 for QRS duration, of which six were successfully replicated. At these seven loci, we performed conditional analyses and annotated significant SNPs (in exons and regulatory regions), demonstrating involvement of cardiac-related pathways and regulation of nearby genes.