Clinical utility gene card for: long-QT syndrome (types 1-13)

Omics technologies in personalized combination therapy for cardiovascular diseases: challenges and opportunities

The primary purpose of 'omics' technologies is to understand the intricacy of genomics, proteomics, metabolomics and other molecular mechanisms to reveal the complex traits of human diseases. The significant use of omics technologies and their applications in medicine gear up the study of the pathogenesis of several disorders. The detection of biomarkers in the early onset of diseases is challenging; still, omics can discover novel molecular mechanisms and biomarkers. In this review, the different types of omics and their technologies are explicated and aimed to provide their emerging applications in cardiovascular precision medicine. These technologies significantly impact optimizing medical treatment for individuals to reach a higher level in precision medicine.

NGS-Based genetic testing for heritable cardiovascular diseases. Specific requirements for obtaining informed consent

Clinical genetic testing in cardiovascular genetic medicine has undergone rapid changes. Next generation sequencing allows simultaneous testing of all genes associated with any cardiovascular phenotype, and molecular genetic testing for multiple genes has become the standard of practice for cardiovascular medicine. While technical and clinical advantages of multigenic approaches are evident, informed consent procedures have become more complex and challenging to the physician ordering such a test, particularly due to the increased potential for unsolicited findings. Based on the EuroGentest "Guidelines for diagnostic next-generation sequencing" we here propose a set of disease-specific requirements for obtaining informed consent for NGS-based genetic testing in a cardiogenetic clinic. We can show that it is often not feasible to obtain informed consent for every detail and suggest, in such cases, to reach general consent beforehand and discuss specific implications of unsolicited findings after the test results are available.

Next generation sequencing applications for cardiovascular disease

The Human Genome Project (HGP), as the primary sequencing of the human genome, lasted more than one decade to be completed using the traditional Sanger's method. At present, next-generation sequencing (NGS) technology could provide the genome sequence data in hours. NGS has also decreased the expense of sequencing; therefore, nowadays it is possible to carry out both whole-genome (WGS) and whole-exome sequencing (WES) for the variations detection in patients with rare genetic diseases as well as complex disorders such as common cardiovascular diseases (CVDs). Finding new variants may contribute to establishing a risk profile for the pathology process of diseases. Here, recent applications of NGS in cardiovascular medicine are discussed; both Mendelian disorders of the cardiovascular system and complex genetic CVDs including inherited cardiomyopathy, channelopathies, stroke, coronary artery disease (CAD) and are considered. We also state some future use of NGS in clinical practice for increasing our information about the CVDs genetics and the limitations of this new technology. Key messages Traditional Sanger's method was the mainstay for Human Genome Project (HGP); Sanger sequencing has high fidelity but is slow and costly as compared to next generation methods. Within cardiovascular medicine, NGS has been shown to be successful in identifying novel causative mutations and in the diagnosis of Mendelian diseases which are caused by a single variant in a single gene. NGS has provided the opportunity to perform parallel analysis of a great number of genes in an unbiased approach (i.e. without knowing the underlying biological mechanism) which probably contribute to advance our knowledge regarding the pathology of complex diseases such as CVD.

Re-evaluating pathogenicity of variants associated with the long QT syndrome

INTRODUCTION

Genetic testing for congenital long QT syndrome (LQTS) has become common. Recent studies have shown that some variants labelled as pathogenic might be misclassified due to sparse case reports and relatively common allele frequencies (AF) in the general population. This study aims to evaluate the presence of LQTS-associated variants in the Genome Aggregation Database (gnomAD) population, and assess the functional impact of these variants.

METHODS AND RESULTS

Variants associated with LQTS from the Human Gene Mutation Database were extracted and matched to the gnomAD to evaluate population-based AF. We used MetaSVM to predict the function of LQTS variants. Allele distribution by protein topology in KCNQ1, KCNH2, and SCN5A was compared between gnomAD (n = 123,136) and a cohort of LQTS patients aggregated from eight published studies (n = 2,683). Among the 1,415 LQTS-associated single nucleotide variants in 30 genes, 347 (25%) are present in gnomAD; 24% of the 347 variants were predicted as functionally tolerated compared with 4% of variants not present in gnomAD (P < 0.001). Of the 347 pathogenic variants in gnomAD, seven (2%) had an AF of ≥ 0.001 and 65 (19%) variants had an AF of ≥ 0.0001. In KCNQ1, KCNH2, and SCN5A, allele distribution by protein functional region was significantly different with gnomAD alleles appearing less frequently in highly pathogenic domains than case alleles.

CONCLUSION

A significant number of LQTS variants have insufficient evidence for pathogenicity and relatively common AF in the general population. Caution should be used when ascribing pathogenicity to these variants.

Bayesian models for syndrome- and gene-specific probabilities of novel variant pathogenicity

Background

With the advent of affordable and comprehensive sequencing technologies, access to molecular genetics for clinical diagnostics and research applications is increasing. However, variant interpretation remains challenging, and tools that close the gap between data generation and data interpretation are urgently required. Here we present a transferable approach to help address the limitations in variant annotation.

Methods

We develop a network of Bayesian logistic regression models that integrate multiple lines of evidence to evaluate the probability that a rare variant is the cause of an individual’s disease. We present models for genes causing inherited cardiac conditions, though the framework is transferable to other genes and syndromes.

Results

Our models report a probability of pathogenicity, rather than a categorisation into pathogenic or benign, which captures the inherent uncertainty of the prediction. We find that gene- and syndrome-specific models outperform genome-wide approaches, and that the integration of multiple lines of evidence performs better than individual predictors. The models are adaptable to incorporate new lines of evidence, and results can be combined with familial segregation data in a transparent and quantitative manner to further enhance predictions.

Though the probability scale is continuous, and innately interpretable, performance summaries based on thresholds are useful for comparisons. Using a threshold probability of pathogenicity of 0.9, we obtain a positive predictive value of 0.999 and sensitivity of 0.76 for the classification of variants known to cause long QT syndrome over the three most important genes, which represents sufficient accuracy to inform clinical decision-making. A web tool APPRAISE [http://www.cardiodb.org/APPRAISE] provides access to these models and predictions.

Conclusions

Our Bayesian framework provides a transparent, flexible and robust framework for the analysis and interpretation of rare genetic variants. Models tailored to specific genes outperform genome-wide approaches, and can be sufficiently accurate to inform clinical decision-making.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-014-0120-4) contains supplementary material, which is available to authorized users.

Induced pluripotent stem cells as cardiac arrhythmic in vitro models and the impact for drug discovery

INTRODUCTION

The development of new antiarrhythmic agents is challenging and is hampered by high attrition rate of novel drug candidates. One of the reasons for this is limited predictability of existing preclinical models for drug assessment. Cardiomyocytes (CMs) derived from disease-specific induced pluripotent stem cells (iPSC) represent a novel in vitro cellular model of cardiac arrhythmias with an unprecedented potential for generating new mechanistic insight into disease pathophysiology and improving the process of drug development.

AREAS COVERED

This review outlines recent studies demonstrating the suitability and limitations of iPSC-derived CMs (iPS-CMs) for in vitro modeling inherited arrhythmias and drug testing. The authors focus on channelopathies and outline the properties of iPS-CMs, highlighting their utility and limitations for investigating the mechanism of cardiac arrhythmias and drug discovery.

EXPERT OPINION

The iPS-CMs represent a valuable addition to the already existing armamentarium of cardiac arrhythmic models. However, the superiority of iPS-CMs over other arrhythmia models has not yet been rigorously established and the limitations of the model must be overcome before its full potential for antiarrhythmic drug discovery can be realized. Nevertheless, iPS cell-based platforms hold a great potential for increasing our knowledge about cellular arrhythmia mechanisms and improving the drug discovery process.