Application of high-throughput sequencing for studying genomic variations in congenital heart disease

Massive Parallel DNA Sequencing of Patients with Inherited Cardiomyopathies in Cyprus and Suggestion of Digenic or Oligogenic Inheritance

Inherited cardiomyopathies represent a highly heterogeneous group of cardiac diseases. DNA variants in genes expressed in cardiomyocytes cause a diverse spectrum of cardiomyopathies, ultimately leading to heart failure, arrythmias, and sudden cardiac death. We applied massive parallel DNA sequencing using a 72-gene panel for studying inherited cardiomyopathies. We report on variants in 25 families, where pathogenicity was predicted by different computational approaches, databases, and an in-house filtering analysis. All variants were validated using Sanger sequencing. Familial segregation was tested when possible. We identified 41 different variants in 26 genes. Analytically, we identified fifteen variants previously reported in the Human Gene Mutation Database: twelve mentioned as disease-causing mutations (DM) and three as probable disease-causing mutations (DM?). Additionally, we identified 26 novel variants. We classified the forty-one variants as follows: twenty-eight (68.3%) as variants of uncertain significance, eight (19.5%) as likely pathogenic, and five (12.2%) as pathogenic. We genetically characterized families with a cardiac phenotype. The genetic heterogeneity and the multiplicity of candidate variants are making a definite molecular diagnosis challenging, especially when there is a suspicion of incomplete penetrance or digenic-oligogenic inheritance. This is the first systematic study of inherited cardiac conditions in Cyprus, enabling us to develop a genetic baseline and precision cardiology.

Technologies to Study Genetics and Molecular Pathways

Over the last few decades, the study of congenital heart disease (CHD) has benefited from various model systems and the development of molecular biological techniques enabling the analysis of single gene as well as global effects. In this chapter, we first describe different models including CHD patients and their families, animal models ranging from invertebrates to mammals, and various cell culture systems. Moreover, techniques to experimentally manipulate these models are discussed. Second, we introduce cardiac phenotyping technologies comprising the analysis of mouse and cell culture models, live imaging of cardiogenesis, and histological methods for fixed hearts. Finally, the most important and latest molecular biotechniques are described. These include genotyping technologies, different applications of next-generation sequencing, and the analysis of transcriptome, epigenome, proteome, and metabolome. In summary, the models and technologies presented in this chapter are essential to study the function and development of the heart and to understand the molecular pathways underlying CHD.

Estimating the frequency of causal genetic variants in foetuses with congenital heart defects: a Chinese cohort study

BACKGROUND

The belief that genetics plays a major role in the pathogenesis of congenital heart defects (CHD) has grown popular among clinicians. Although some studies have focused on the genetic testing of foetuses with CHD in China, the genotype-phenotype relationship has not yet been fully established, and hotspot copy number variations (CNVs) related to CHD in the Chinese population are still unclear. This cohort study aimed to assess the prevalence of chromosomal abnormalities in Chinese foetuses with different types of CHD.

RESULTS

In a cohort of 200 foetuses, chromosomal abnormalities were detected in 49 (24.5%) after a prenatal chromosome microarray analysis (CMA), including 23 foetuses (11.5%) with aneuploidies and 26 (13.0%) with clinically significant CNVs. The additional diagnostic yield following whole exome sequencing (WES) was 11.5% (6/52). The incidence of total chromosomal abnormality in the non-isolated CHD group (31.8%) was higher than that in the isolated CHD group (20.9%), mainly because the incidence of aneuploidy was significantly increased when CHD was combined with extracardiac structural abnormalities or soft markers. The chromosomal abnormality rate of the complex CHD group was higher than that of the simple CHD group; however, the difference was not statistically significant (31.8% vs. 23.6%, P = 0.398). The most common CNV detected in CHD foetuses was the 22q11.2 deletion, followed by deletions of 5p15.33p15.31, deletions of 15q13.2q13.3, deletions of 11q24.2q25, deletions of 17p13.3p13.2, and duplications of 17q12.

CONCLUSIONS

CMA is the recommended initial examination for cases of CHD in prenatal settings, for both simple heart defects and isolated heart defects. For cases with negative CMA results, the follow-up application of WES will offer a considerable proportion of additional detection of clinical significance.

The Interaction Analysis of SNP Variants and DNA Methylation Identifies Novel Methylated Pathogenesis Genes in Congenital Heart Diseases

Congenital heart defect (CHD) is a rare and complicated disease with a high mortality rate. Its etiology remains unclear and includes many aspects. DNA methylation has been indicated to be involved in heart development in the early stage of life, and aberrant methylation level was related to CHDs. This study provides the first evidence of the cross talk of SNP variants and DNA methylation in clarifying CHD underlying genomic cause. We gathered whole exome sequencing (WES) data for Group 1 consisting of patients with PA (n = 78), TOF (n = 20), TAPVC (n = 78), and PDA (n = 40), and 100 healthy children as control group. Rare non-synonymous mutations and novel genes were found and highlighted. Meanwhile, we carried out the second analysis of DNA methylation data from patients with PA (n = 3), TAPVC (n = 3), TOF (n = 3), and PDA (n = 2), and five healthy controls using 850 K array in Group 2. DNA methylation was linked to WES data, and we explored an obvious overlap of hyper/hypomethylated genes. Next, we identified some candidate genes by Fisher’s exact test and Burden analysis; then, those methylated genes were figured out by the criteria of the mutation located in the CpG islands of the genome, differential methylation sites (DMS), and DNA methylation quantitative trait loci (meQTLs) in the database, respectively. Also, the interaction of differentially methylated candidate genes with known CHD pathogenetic genes was depicted in a molecular network. Taken together, our findings show that nine novel genes (ANGPTL4, VEGFA, PAX3, MUC4, HLA-DRB1, TJP2, BCR, PKD1, and HK2) in methylation level are critical to CHD and reveal a new insight into the molecular pathogenesis of CHD.

The Biological Significance of Multi-copy Regions and Their Impact on Variant Discovery

Identification of genetic variants via high-throughput sequencing (HTS) technologies has been essential for both fundamental and clinical studies. However, to what extent the genome sequence composition affects variant calling remains unclear. In this study, we identified 63,897 multi-copy sequences (MCSs) with a minimum length of 300 bp, each of which occurs at least twice in the human genome. The 151,749 genomic loci (multi-copy regions, or MCRs) harboring these MCSs account for 1.98% of the genome and are distributed unevenly across chromosomes. MCRs containing the same MCS tend to be located on the same chromosome. Gene Ontology (GO) analyses revealed that 3800 genes whose UTRs or exons overlap with MCRs are enriched for Golgi-related cellular component terms and various enzymatic activities in the GO biological function category. MCRs are also enriched for loci that are sensitive to neocarzinostatin-induced double-strand breaks. Moreover, genetic variants discovered by genome-wide association studies and recorded in dbSNP are significantly underrepresented in MCRs. Using simulated HTS datasets, we show that false variant discovery rates are significantly higher in MCRs than in other genomic regions. These results suggest that extra caution must be taken when identifying genetic variants in the MCRs via HTS technologies.

Navigating the non-coding genome in heart development and Congenital Heart Disease

Congenital Heart Disease (CHD) is characterised by a wide range of cardiac defects, from mild to life-threatening, which occur in babies worldwide. To date, there is no cure to CHD, however, progress in surgery has reduced its mortality allowing children affected by CHD to reach adulthood. In an effort to understand its genetic basis, several studies involving whole-genome sequencing (WGS) of patients with CHD have been undertaken and generated a great wealth of information. The majority of putative causative mutations identified in WGS studies fall into the non-coding part of the genome. Unfortunately, due to the lack of understanding of the function of these non-coding mutations, it is challenging to establish a causal link between the non-coding mutation and the disease. Thus, here we review the state-of-the-art approaches to interpret non-coding mutations in the context of CHD and address the following questions: What are the non-coding sequences important for cardiac function? Which technologies are used to identify them? Which resources are available to analyse them? What mutations are expected in these non-coding sequences? Learning from developmental process, what is their expected role in CHD?

Targeted Next-Generation Sequencing in Patients with Non-syndromic Congenital Heart Disease

Congenital heart disease (CHD) is a genetically heterogeneous disease. Targeted next-generation sequencing (NGS) offers a unique opportunity to sequence multiple genes at lower cost and effort compared to Sanger sequencing. We tested a targeted NGS of a specific gene panel in a relatively large population of non-syndromic CHD patients. The patient cohort comprised 68 CHD patients (45 males; 8.3 ± 1.7 years). Amplicon libraries for 16 CHD-strictly related genes were generated using a TruSeq^® Custom Amplicon kit (Illumina, CA) and sequenced using the Illumina MiSeq platform. Sequence data were processed through the MiSeq Reporter and wANNOVAR softwares. After applying stringent filtering criteria, 20 missense variants in 9 genes were predicted to be damaging and were validated by Sanger sequencing with 100% concordance. Fourteen variants were present in public databases with very rare allele frequency, of which four variants (p.Arg25Cys in NKX2-5, p.Val763Ile in ZFPM2, p.Arg1398Gln and Gly1826Asp in MYH6) have been previously linked to CHD or cardiomyopathy. The remaining six variants in four genes (GATA4, NKX2-5, NOTCH1, TBX1) were novel mutations, currently not found in public databases, and absent in 200 control alleles of healthy subjects. Four patients (5.8%) carried two missense variants (1 compound heterozygote in the same gene and 3 double heterozygotes in different genes), with possibly synergistic deleterious effects. Targeted NGS is a powerful and efficient tool to detect DNA sequence variants in multiple genes, providing the opportunity for discovery of the co-occurrence of two or more missense rare variants.

Genetic and flow anomalies in congenital heart disease

Congenital heart defects are the most common malformations in humans, affecting approximately 1% of newborn babies. While genetic causes of congenital heart disease have been studied, only less than 20% of human cases are clearly linked to genetic anomalies. The cause for the majority of the cases remains unknown. Heart formation is a finely orchestrated developmental process and slight disruptions of it can lead to severe malformations. Dysregulation of developmental processes leading to heart malformations are caused by genetic anomalies but also environmental factors including blood flow. Intra-cardiac blood flow dynamics plays a significant role regulating heart development and perturbations of blood flow lead to congenital heart defects in animal models. Defects that result from hemodynamic alterations, however, recapitulate those observed in human babies, even those due to genetic anomalies and toxic teratogen exposure. Because important cardiac developmental events, such as valve formation and septation, occur under blood flow conditions while the heart is pumping, blood flow regulation of cardiac formation might be a critical factor determining cardiac phenotype. The contribution of flow to cardiac phenotype, however, is frequently ignored. More research is needed to determine how blood flow influences cardiac development and the extent to which flow may determine cardiac phenotype.

Genetics and genetic testing in congenital heart disease

Congenital heart defects (CHDs) are structural abnormalities of the heart and great vessels that are present from birth. The presence or absence of extracardiac anomalies has historically been used to identify patients with possible monogenic, chromosomal, or teratogenic CHD causes. These distinctions remain clinically relevant, but it is increasingly clear that nonsyndromic CHDs can also be genetic. This article discusses key morphologic, molecular, and signaling mechanisms relevant to heart development, summarizes overall progress in molecular genetic analyses of CHDs, and provides current recommendations for clinical application of genetic testing.

Application of Next Generation Sequencing for personalized medicine for sudden cardiac death

Sudden cardiac death (SCD) is a serious public health problem. In the United States, more than 300,000 people are affected by SCD every year. Significantly, sudden deaths represent 20% of the total mortality and 50% of cardiovascular mortality in Western countries. In addition, SCD constitutes one of the most important unsolved challenges in the practice of forensic pathology because of the failure to determine the exact cause of sudden death. In young individuals, SCD is frequently caused by cardiomyopathies and channelopathies, that have generally an autosomal dominant pattern of inheritance. The impact of genetics and genetic testing on the clinical management of these diseases is unquestioned. In particular, genetic tests are an important tool for identifying pre-symptomatic individuals carrying genetic variant that predisposes them to SCD. High-throughput sequencing technologies offer novel opportunities to deeper investigate the genetic background underlying these fatal diseases and to early identify individuals at risk for SCD. In this review, we provide an overview of the development of Next-Generation Sequencing (NGS) technologies and of guidelines useful to design an efficient sequencing protocol and to perform an accurate data analysis. We suggest a flow chart to follow for the set up of a genetic screening protocol for the prevention of cardiac pathologies, in particular SCD events, in young athletes.

High throughput exome coverage of clinically relevant cardiac genes

Background

Given the growing use of whole-exome sequencing (WES) for clinical diagnostics of complex human disorders, we evaluated coverage of clinically relevant cardiac genes on WES and factors influencing uniformity and depth of coverage of exonic regions.

Methods

Two hundred and thirteen human DNA samples were exome sequenced via Illumina HiSeq using different versions of the Agilent SureSelect capture kit. 50 cardiac genes were further analyzed including 31 genes from the American College of Medical Genetics (ACMG) list for reporting of incidental findings and 19 genes associated with congenital heart disease for which clinical testing is available. Gene coordinates were obtained from two databases, CCDS and Known Gene and compared. Read depth for each region was extracted from the exomes and used to assess capture variability between kits for individual genes, and for overall coverage. GC content, gene size, and inter-sample variability were also tested as potential contributors to variability in gene coverage.

Results

All versions of capture kits (designed based on Consensus coding sequence) included only 55% of known genomic regions for the cardiac genes. Although newer versions of each Agilent kit showed improvement in capture of CCDS regions to 99%, only 64% of Known Gene regions were captured even with newer capture kits. There was considerable variability in coverage of the cardiac genes. 10 of the 50 genes including 6 on the ACMG list had less than the optimal coverage of 30X. Within each gene, only 32 of the 50 genes had the majority of their bases covered at an interquartile range ≥30X. Heterogeneity in gene coverage was modestly associated with gene size and significantly associated with GC content.

Conclusions

Despite improvement in overall coverage across the exome with newer capture kit versions and higher sequencing depths, only 50% of known genomic regions of clinical cardiac genes are targeted and individual gene coverage is non-uniform. This may contribute to a bias with greater attribution of disease causation to mutations in well-represented and well-covered genes. Improvements in WES technology are needed before widespread clinical application.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-014-0067-8) contains supplementary material, which is available to authorized users.