Copy number variants in Ebstein anomaly

Human Genetics of Ebstein Anomaly

Ebstein anomaly (EA) is a rare, congenital cardiac defect of the tricuspid valve with a birth prevalence between 0.5 and 1 in 20,000 [1]. It is characterized by displacement of the tricuspid valve toward the apex of the right ventricle (RV) and "atrialization" of the RV (Fig. 57.1) [2]. EA accounts for about 0.5% of all congenital heart diseases (CHD) [2]. Depending on severity of the defect and due to heterogeneity of the disease, patient's presentation varies from severe heart failure symptoms and arrhythmia in neonatal life to asymptomatic adults.

Identification of vital modules and genes associated with heart failure based on weighted gene coexpression network analysis

Aims

Heart failure (HF) is a chronic heart disease with a high incidence and mortality. Due to the regulatory complexity of gene coexpression networks, the underlying hub genes regulation in HF remain incompletely appreciated. We aimed to explore potential key modules and genes for HF using weighted gene coexpression network analysis (WGCNA).

Methods and results

The expression profiles by high throughput sequencing of heart tissues samples from HF and non‐HF samples were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between HF and non‐HF samples were firstly identified. Then, a coexpression network was constructed to identify key modules and potential hub genes. The biological functions of potential hub genes were analysed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. Finally, a protein–protein interaction (PPI) network was constructed using the STRING online tool. A total of 135 DEGs (133 up‐regulated and 2 down‐regulated DEGs) between HF and non‐HF samples were identified in the GSE135055 and GSE123976 datasets. Moreover, a total of 38 modules were screened based on WGCNA in the GSE135055 dataset, and six potential hub genes (UCK2, ASB1, CCNI, CUX1, IRX6, and STX16) were screened from the key module by setting the gene significance over 0.2 and the module membership over 0.8. Furthermore, 78 potential hub genes were obtained by taking the intersection of the 135 DEGs and all genes in the key module, and enrichment analysis revealed that they were mainly involved in the MAPK and PI3K‐AKT signalling pathways. Finally, in a PPI network constructed with the 78 potential hub genes, CUX1 and ASB1 were identified as hub genes in HF because they were also identified as potential hub genes in the WGCNA.

Conclusions

To the best of our knowledge, our study is the first to employ WGCNA to identify the key module and hub genes for HF. Our study identified a module and two genes that might play important roles in HF, which may provide potential biomarkers for the diagnosis of HF and improve our knowledge of the molecular mechanisms underlying HF.

Little to Give, Much to Gain-What Can You Do With a Dried Blood Spot?

PURPOSE OF REVIEW

Technological advances have allowed dried blood spots (DBS) to be utilized for various measurements, helpful in population-based studies. The following is a review of the literature highlighting the advantages and disadvantages of DBS and describing their use in multiple areas of research.

RECENT FINDINGS

DBS can track pollutant exposure to understand their impact on health. DBS can also be used for (epi-)genetic studies, to measure clinical biomarkers, and to monitor drug adherence. Advantages of DBS include being minimally invasive, requiring low blood volume, and being cost-effective to collect, transport, and store. Disadvantages of DBS include the hematocrit effect, which is related to the viscosity of the blood affecting its spread on to the filter paper, causing a major source of error when assessing concentrations, and the possibility of low DNA volume. Numerous uses for DBS make them an important source of biomaterial but they require additional validation for accuracy and reproducibility.

Ebstein Anomaly in the Adult Patient

Ebstein anomaly is a congenital malformation involving primarily the tricuspid valve, with failure of delamination from the underlying myocardium and right ventricular myopathy. Echocardiography is diagnostic in most patients and demonstrates apical displacement of the septal leaflet and variable tethering of leaflet tissue to the right ventricular myocardium. Operative intervention is considered for exertional symptoms, progressive right ventricular enlargement, or right ventricular dysfunction. Tricuspid valve cone repair is the preferred surgical approach. Tricuspid valve replacement and bidirectional cavopulmonary shunt also are considered in patients with advanced disease. Pregnancy generally is well tolerated. Patients with Ebstein anomaly require lifelong follow-up.

Distinct epigenomic patterns are associated with haploinsufficiency and predict risk genes of developmental disorders

Haploinsufficiency is a major mechanism of genetic risk in developmental disorders. Accurate prediction of haploinsufficient genes is essential for prioritizing and interpreting deleterious variants in genetic studies. Current methods based on mutation intolerance in population data suffer from inadequate power for genes with short transcripts. Here we show haploinsufficiency is strongly associated with epigenomic patterns, and develop a computational method (Episcore) to predict haploinsufficiency leveraging epigenomic data from a broad range of tissue and cell types by machine learning methods. Based on data from recent exome sequencing studies on developmental disorders, Episcore achieves better performance in prioritizing likely-gene-disrupting (LGD) de novo variants than current methods. We further show that Episcore is less-biased by gene size, and complementary to mutation intolerance metrics for prioritizing LGD variants. Our approach enables new applications of epigenomic data and facilitates discovery and interpretation of novel risk variants implicated in developmental disorders.

Predicting haploinsufficient genes helps to understand the genetic risk underlying developmental disorders. Here, the authors develop a Random Forest-based method that uses epigenomic data to predict haploinsufficiency, Episcore, which is complementary to methods based on mutation intolerance scores.