Integration of genetics into a systems model of electrocardiographic traits using HumanCVD BeadChip

A common variant alters SCN5A-miR-24 interaction and associates with heart failure mortality

SCN5A encodes the voltage-gated Na+ channel NaV1.5 that is responsible for depolarization of the cardiac action potential and rapid intercellular conduction. Mutations disrupting the SCN5A coding sequence cause inherited arrhythmias and cardiomyopathy, and single-nucleotide polymorphisms (SNPs) linked to SCN5A splicing, localization, and function associate with heart failure-related sudden cardiac death. However, the clinical relevance of SNPs that modulate SCN5A expression levels remains understudied. We recently generated a transcriptome-wide map of microRNA (miR) binding sites in human heart, evaluated their overlap with common SNPs, and identified a synonymous SNP (rs1805126) adjacent to a miR-24 site within the SCN5A coding sequence. This SNP was previously shown to reproducibly associate with cardiac electrophysiological parameters, but was not considered to be causal. Here, we show that miR-24 potently suppresses SCN5A expression and that rs1805126 modulates this regulation. We found that the rs1805126 minor allele associates with decreased cardiac SCN5A expression and that heart failure subjects homozygous for the minor allele have decreased ejection fraction and increased mortality, but not increased ventricular tachyarrhythmias. In mice, we identified a potential basis for this in discovering that decreased Scn5a expression leads to accumulation of myocardial reactive oxygen species. Together, these data reiterate the importance of considering the mechanistic significance of synonymous SNPs as they relate to miRs and disease, and highlight a surprising link between SCN5A expression and nonarrhythmic death in heart failure.

HAPRAP: a haplotype-based iterative method for statistical fine mapping using GWAS summary statistics

MOTIVATION

Fine mapping is a widely used approach for identifying the causal variant(s) at disease-associated loci. Standard methods (e.g. multiple regression) require individual level genotypes. Recent fine mapping methods using summary-level data require the pairwise correlation coefficients ([Formula: see text]) of the variants. However, haplotypes rather than pairwise [Formula: see text], are the true biological representation of linkage disequilibrium (LD) among multiple loci. In this article, we present an empirical iterative method, HAPlotype Regional Association analysis Program (HAPRAP), that enables fine mapping using summary statistics and haplotype information from an individual-level reference panel.

RESULTS

Simulations with individual-level genotypes show that the results of HAPRAP and multiple regression are highly consistent. In simulation with summary-level data, we demonstrate that HAPRAP is less sensitive to poor LD estimates. In a parametric simulation using Genetic Investigation of ANthropometric Traits height data, HAPRAP performs well with a small training sample size (N < 2000) while other methods become suboptimal. Moreover, HAPRAP's performance is not affected substantially by single nucleotide polymorphisms (SNPs) with low minor allele frequencies. We applied the method to existing quantitative trait and binary outcome meta-analyses (human height, QTc interval and gallbladder disease); all previous reported association signals were replicated and two additional variants were independently associated with human height. Due to the growing availability of summary level data, the value of HAPRAP is likely to increase markedly for future analyses (e.g. functional prediction and identification of instruments for Mendelian randomization).

AVAILABILITY AND IMPLEMENTATION

The HAPRAP package and documentation are available at http://apps.biocompute.org.uk/haprap/ CONTACT: : jie.zheng@bristol.ac.uk or tom.gaunt@bristol.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Sex-dichotomous effects of NOS1AP promoter DNA methylation on intracranial aneurysm and brain arteriovenous malformation

The goal of this study was to investigate the contribution of NOS1AP-promoter DNA methylation to the risk of intracranial aneurysm (IA) and brain arteriovenous malformation (BAVM) in a Han Chinese population. A total of 48 patients with IAs, 22 patients with BAVMs, and 26 control individuals were enrolled in the study. DNA methylation was tested using bisulfite pyrosequencing technology. We detected significantly higher DNA methylation levels in BAVM patients than in IA patients based on the multiple testing correction (CpG4-5 methylation: 5.86±1.04% vs. 4.37±2.64%, P=0.006). In women, CpG4-5 methylation levels were much lower in IA patients (3.64±1.97%) than in BAVM patients (6.11±1.20%, P<0.0001). However, in men, CpG1-3 methylation levels were much higher in the controls (6.92±0.78%) than in BAVM patients (5.99±0.70%, P=0.008). Additionally, there was a gender-based difference in CpG1 methylation within the controls (men vs. women: 5.75±0.50% vs. 4.99±0.53%, P=0.003) and BAVM patients (men vs. women: 4.70±0.74% vs. 5.50±0.87%, P=0.026). A subgroup analysis revealed significantly higher CpG3 methylation in patients who smoked than in those who did not (P=0.041). Our results suggested that gender modulated the interaction between NOS1AP promoter DNA methylation in IA and BAVM patients. Our results also confirmed that regular tobacco smoking was associated with increased NOS1AP methylation in humans. Additional studies with larger sample sizes are required to replicate and extend these findings.

The cardiac sodium channel gene SCN5A and its gene product NaV1.5: Role in physiology and pathophysiology

The gene SCN5A encodes the main cardiac sodium channel NaV1.5. This channel predominates the cardiac sodium current, INa, which underlies the fast upstroke of the cardiac action potential. As such, it plays a crucial role in cardiac electrophysiology. Over the last 60years a tremendous amount of knowledge regarding its function at the electrophysiological and molecular level has been acquired. Furthermore, genetic studies have shown that mutations in SCN5A are associated with multiple cardiac diseases (e.g. Brugada syndrome, Long QT syndrome, conduction disease and cardiomyopathy), while genetic variation in the general population has been associated with differences in cardiac conduction and risk of arrhythmia through genome wide association studies. In this review we aim to give an overview of the current knowledge (and the gaps therein) on SCN5A and NaV1.5.

Functional annotation of HOT regions in the human genome: implications for human disease and cancer

Advances in genome-wide association studies (GWAS) and large-scale sequencing studies have resulted in an impressive and growing list of disease- and trait-associated genetic variants. Most studies have emphasised the discovery of genetic variation in coding sequences, however, the noncoding regulatory effects responsible for human disease and cancer biology have been substantially understudied. To better characterise the cis-regulatory effects of noncoding variation, we performed a comprehensive analysis of the genetic variants in HOT (high-occupancy target) regions, which are considered to be one of the most intriguing findings of recent large-scale sequencing studies. We observed that GWAS variants that map to HOT regions undergo a substantial net decrease and illustrate development-specific localisation during haematopoiesis. Additionally, genetic risk variants are disproportionally enriched in HOT regions compared with LOT (low-occupancy target) regions in both disease-relevant and cancer cells. Importantly, this enrichment is biased toward disease- or cancer-specific cell types. Furthermore, we observed that cancer cells generally acquire cancer-specific HOT regions at oncogenes through diverse mechanisms of cancer pathogenesis. Collectively, our findings demonstrate the key roles of HOT regions in human disease and cancer and represent a critical step toward further understanding disease biology, diagnosis, and therapy.

Genetic markers of repolarization and arrhythmic events after acute coronary syndromes

BACKGROUND

There is a genetic contribution to the risk of ventricular arrhythmias in survivors of acute coronary syndromes (ACS). We wished to explore the role of 33 candidate single nucleotide polymorphisms (SNPs) in prolonged repolarization and sudden death in patients surviving ACS.

METHODS

A total of 2,139 patients (1680 white ethnicity) surviving an admission for ACS were enrolled in the prospective Coronary Disease Cohort Study. Extensive clinical, echocardiographic, and neurohormonal data were collected for 12 months, and clinical events were recorded for a median of 5 years. Each SNP was assessed for association with sudden cardiac death (SCD)/cardiac arrest (CA) and prolonged repolarization at 3 time-points: index admission, 1 month, and 12 months postdischarge.

RESULTS

One hundred six SCD/CA events occurred during follow-up (6.3%). Three SNPs from 3 genes (rs17779747 [KCNJ2], rs876188 [C14orf64], rs3864180 [GPC5]) were significantly associated with SCD/CA in multivariable models (after correction for multiple testing); the minor allele of rs17779747 with a decreased risk (hazard ratio [HR] 0.68 per copy of the minor allele, 95% CI 0.50-0.92, P = .012), and rs876188 and rs386418 with an increased risk (HR 1.52 [95% CI 1.10-2.09, P = .011] and HR 1.34 [95% CI 1.04-1.82, P = .023], respectively). At 12 months postdischarge, rs10494366 and rs12143842 (NOS1AP) were significant predictors of prolonged repolarization (HR 1.32 [95% CI 1.04-1.67, P = .022] and HR 1.30 [95% CI 1.01-1.66, P = .038], respectively), but not at earlier time-points.

CONCLUSION

Three SNPs were associated with SCD/CA. Repolarization time was associated with variation in the NOS1AP gene. This study demonstrates a possible role for SNPs in risk stratification for arrhythmic events after ACS.

Canonical correlation analysis for gene-based pleiotropy discovery

Genome-wide association studies have identified a wealth of genetic variants involved in complex traits and multifactorial diseases. There is now considerable interest in testing variants for association with multiple phenotypes (pleiotropy) and for testing multiple variants for association with a single phenotype (gene-based association tests). Such approaches can increase statistical power by combining evidence for association over multiple phenotypes or genetic variants respectively. Canonical Correlation Analysis (CCA) measures the correlation between two sets of multidimensional variables, and thus offers the potential to combine these two approaches. To apply CCA, we must restrict the number of attributes relative to the number of samples. Hence we consider modules of genetic variation that can comprise a gene, a pathway or another biologically relevant grouping, and/or a set of phenotypes. In order to do this, we use an attribute selection strategy based on a binary genetic algorithm. Applied to a UK-based prospective cohort study of 4286 women (the British Women's Heart and Health Study), we find improved statistical power in the detection of previously reported genetic associations, and identify a number of novel pleiotropic associations between genetic variants and phenotypes. New discoveries include gene-based association of NSF with triglyceride levels and several genes (ACSM3, ERI2, IL18RAP, IL23RAP and NRG1) with left ventricular hypertrophy phenotypes. In multiple-phenotype analyses we find association of NRG1 with left ventricular hypertrophy phenotypes, fibrinogen and urea and pleiotropic relationships of F7 and F10 with Factor VII, Factor IX and cholesterol levels.

Pleiotropy appears when a variation in one gene affects to several non-related phenotypes. The study of this phenomenon can be useful in gene function discovery, but also in the study of the evolution of a gene. In this paper, we present a methodology, based on Canonical Correlation Analysis, which studies gene-centered multiple association of the variation of SNPs in one or a set of genes with one or a set of phenotypes. The resulting methodology can be applied in gene-centered association analysis, multiple association analysis or pleiotropic pattern discovery. We apply this methodology with a genotype dataset and a set of cardiovascular related phenotypes, and discover new gene association between gene NRG1 and phenotypes related with left ventricular hypertrophy, and pleiotropic effects of this gene with other phenotypes as coagulation factors and urea or pleiotropic effects between coagulation related genes F7 and F10 with coagulation factors and cholesterol levels. This methodology could be also used to find multiple associations in other omics datasets.

Genome-wide identification of expression quantitative trait loci (eQTLs) in human heart

In recent years genome-wide association studies (GWAS) have uncovered numerous chromosomal loci associated with various electrocardiographic traits and cardiac arrhythmia predisposition. A considerable fraction of these loci lie within inter-genic regions. The underlying trait-associated variants likely reside in regulatory regions and exert their effect by modulating gene expression. Hence, the key to unraveling the molecular mechanisms underlying these cardiac traits is to interrogate variants for association with differential transcript abundance by expression quantitative trait locus (eQTL) analysis. In this study we conducted an eQTL analysis of human heart. For a total of 129 left ventricular samples that were collected from non-diseased human donor hearts, genome-wide transcript abundance and genotyping was determined using microarrays. Each of the 18,402 transcripts and 897,683 SNP genotypes that remained after pre-processing and stringent quality control were tested for eQTL effects. We identified 771 eQTLs, regulating 429 unique transcripts. Overlaying these eQTLs with cardiac GWAS loci identified novel candidates for studies aimed at elucidating the functional and transcriptional impact of these loci. Thus, this work provides for the first time a comprehensive eQTL map of human heart: a powerful and unique resource that enables systems genetics approaches for the study of cardiac traits.

Genomics of cardiac electrical function

Proper generation and conduction of the cardiac electrical impulse is essential for the continuous coordinated contraction of the heart. Dysregulation of cardiac electrical function may lead to cardiac arrhythmias, which constitute a huge medical and social burden. Identifying the genetic factors underlying cardiac electrical activity serves the double purpose of allowing the early identification of individuals at risk for arrhythmia and discovering new potential therapeutic targets for prevention. The aim of this review is to provide an overview of the genes and genetic loci linked thus far to cardiac electrical function and arrhythmia. These genes and loci have been primarily uncovered through studies on the familial rhythm disorders and through genome-wide association studies on electrocardiographic parameters in large sets of the general population. An overview of all genes and loci with their respective effect is given.

Sequential sentinel SNP Regional Association Plots (SSS-RAP): an approach for testing independence of SNP association signals using meta-analysis data

Genome-Wide Association Studies (GWAS) frequently incorporate meta-analysis within their framework. However, conditional analysis of individual-level data, which is an established approach for fine mapping of causal sites, is often precluded where only group-level summary data are available for analysis. Here, we present a numerical and graphical approach, "sequential sentinel SNP regional association plot" (SSS-RAP), which estimates regression coefficients (beta) with their standard errors using the meta-analysis summary results directly. Under an additive model, typical for genes with small effect, the effect for a sentinel SNP can be transformed to the predicted effect for a possibly dependent SNP through a 2×2 2-SNP haplotypes table. The approach assumes Hardy-Weinberg equilibrium for test SNPs. SSS-RAP is available as a Web-tool (http://apps.biocompute.org.uk/sssrap/sssrap.cgi). To develop and illustrate SSS-RAP we analyzed lipid and ECG traits data from the British Women's Heart and Health Study (BWHHS), evaluated a meta-analysis for ECG trait and presented several simulations. We compared results with existing approaches such as model selection methods and conditional analysis. Generally findings were consistent. SSS-RAP represents a tool for testing independence of SNP association signals using meta-analysis data, and is also a convenient approach based on biological principles for fine mapping in group level summary data.