The impact of COVID-19 vaccination on patients with congenital heart disease in England: a case-control study.

BACKGROUND: Studies predating widespread COVID-19 vaccination identified patients with congenital heart disease (CHD) as a group at increased risk of severe outcomes from COVID-19. Here we evaluate the impact of vaccination on COVID-19 outcomes among patients with CHD. METHODS: We conducted a case-control study using linked English electronic health records (n=3 18 135). Patients with CHD were matched with controls by age, sex, ethnicity and GP practice. The 'prevaccination' cohort comprised unvaccinated patients with CHD and matched controls with first-recorded SARS-CoV-2 infection between 1 March and 8 December 2020 (7805 cases, 27 620 controls). The 'post-vaccination' cohort comprised vaccinated patients with CHD and matched controls with first-recorded SARS-CoV-2 infection between 1 March 2021 and 1 April 2022, at least 14 days after vaccination (57 550 cases, 225 160 controls). Odds of severe COVID-19 outcomes were compared using conditional logistic regression. We also compared the rate at which vaccine efficacy diminished, and the incidence of vaccine-associated complications. RESULTS: Compared with the prevaccination cohort, postvaccination patients with CHD exhibited markedly reduced rates of COVID-19-related hospitalisation (0.5% vs 15.8%) and mortality rates (0.5% vs 4.6%). Compared with vaccinated controls, vaccinated patients with CHD remained at increased risk of hospitalisation (0.5% vs 0.2%, adjusted OR 2.24 (1.88-2.65); p<0.001) and death (0.5% vs 0.3%, adjusted OR 1.81 (1.54-2.13); p<0.001). There was no evidence that vaccine efficacy declined faster in patients with CHD, or that patients with CHD experienced a larger increase in incidence of myocarditis, pericarditis or thrombotic events. CONCLUSION: We observed a lower absolute risk of hospitalisation and death from COVID-19 in CHD patients after vaccination. However, in vaccinated CHD patients, an elevated risk of severe outcomes persists compared with vaccinated people without CHD. These results emphasise the importance of vaccination in the CHD population, and of vigilance among care providers dealing with COVID-19 infection in CHD patients, even if fully vaccinated.

Interactive molecular causal networks of hypertension using a fast machine learning algorithm MRdualPC.

BACKGROUND: Understanding the complex interactions between genes and their causal effects on diseases is crucial for developing targeted treatments and gaining insight into biological mechanisms. However, the analysis of molecular networks, especially in the context of high-dimensional data, presents significant challenges. METHODS: This study introduces MRdualPC, a computationally tractable algorithm based on the MRPC approach, to infer large-scale causal molecular networks. We apply MRdualPC to investigate the upstream causal transcriptomics influencing hypertension using a comprehensive dataset of kidney genome and transcriptome data. RESULTS: Our algorithm proves to be 100 times faster than MRPC on average in identifying transcriptomics drivers of hypertension. Through clustering, we identify 63 modules with causal driver genes, including 17 modules with extensive causal networks. Notably, we find that genes within one of the causal networks are associated with the electron transport chain and oxidative phosphorylation, previously linked to hypertension. Moreover, the identified causal ancestor genes show an over-representation of blood pressure-related genes. CONCLUSIONS: MRdualPC has the potential for broader applications beyond gene expression data, including multi-omics integration. While there are limitations, such as the need for clustering in large gene expression datasets, our study represents a significant advancement in building causal molecular networks, offering researchers a valuable tool for analyzing big data and investigating complex diseases.

Whole -genome survival analysis of 144 286 people from the UK Biobank identifies novel loci associated with blood pressure.

This study utilized UK Biobank data from 144 286 participants and employed whole-genome sequencing (WGS) data and time-to-event data over a 12-year follow-up period to identify susceptibility in genetic variants associated with hypertension. Following genotype quality control, 6 319 822 single nucleotide polymorphisms underwent analysis, revealing 31 significant variant-level associations. Among these, 29 were novel - 15 in Fibrillin-2 ( FBN2 ) and 4 in Junctophilin-2 ( JPH2 ). Mendelian randomization utilizing two identified variants (rs17677724 and rs1014754) suggested that a genetically induced decrease in heart FBN2 expression and an increase in adrenal gland JPH2 expression were causally linked to hypertension. Phenome-wide association (PheWAS) analysis using the FinnGen dataset confirmed positive associations of rs17677724 and rs1014754 with hypertension, assessed across 2727 traits in 377 277 individuals. Lastly, rs1014754 positively associated with kallistatin, whereas rs17677724 negatively associated with renin in the Fenland study, suggesting a counterregulatory response to high blood pressure. This study, employing WGS data, identified novel genetic loci and potential therapeutic targets for hypertension.

FLT4 causes developmental disorders of the cardiovascular and lymphovascular systems via pleiotropic molecular mechanisms.

AIMS: Rare, deleterious genetic variants in FLT4 are associated with Tetralogy of Fallot (TOF), the most common cyanotic congenital heart disease. The distinct genetic variants in FLT4 are also an established cause of Milroy disease, the most prevalent form of primary hereditary lymphoedema. The phenotypic features of these two conditions are non-overlapping, implying pleiotropic cellular mechanisms during development. METHODS AND RESULTS: In this study, we show that FLT4 variants identified in patients with TOF, when expressed in primary human endothelial cells, cause aggregation of FLT4 protein in the perinuclear endoplasmic reticulum, activating proteostatic and metabolic signalling, whereas lymphoedema-associated FLT4 variants and wild-type (WT) FLT4 do not. FLT4 TOF variants display characteristic gene expression profiles in key developmental signalling pathways, revealing a role for FLT4 in cardiogenesis distinct from its role in lymphatic development. Inhibition of proteostatic signalling abrogates these effects, identifying potential avenues for therapeutic intervention. Depletion of flt4 in zebrafish caused cardiac phenotypes of reduced heart size and altered heart looping. These phenotypes were rescued with coinjection of WT human FLT4 mRNA, but incompletely or not at all by mRNA harbouring FLT4 TOF variants. CONCLUSION: Taken together, we identify a pathogenic mechanism for FLT4 variants predisposing to TOF that is distinct from the known dominant negative mechanism of Milroy-causative variants. FLT4 variants give rise to conditions of the two circulatory subdivisions of the vascular system via distinct developmental pleiotropic molecular mechanisms.

Unsupervised ensemble-based phenotyping enhances discoverability of genes related to left-ventricular morphology.

Recent genome-wide association studies have successfully identified associations between genetic variants and simple cardiac morphological parameters derived from cardiac magnetic resonance images. However, the emergence of large databases, including genetic data linked to cardiac magnetic resonance facilitates the investigation of more nuanced patterns of cardiac shape variability than those studied so far. Here we propose a framework for gene discovery coined unsupervised phenotype ensembles. The unsupervised phenotype ensemble builds a redundant yet highly expressive representation by pooling a set of phenotypes learnt in an unsupervised manner, using deep learning models trained with different hyperparameters. These phenotypes are then analysed via genome-wide association studies, retaining only highly confident and stable associations across the ensemble. We applied our approach to the UK Biobank database to extract geometric features of the left ventricle from image-derived three-dimensional meshes. We demonstrate that our approach greatly improves the discoverability of genes that influence left ventricle shape, identifying 49 loci with study-wide significance and 25 with suggestive significance. We argue that our approach would enable more extensive discovery of gene associations with image-derived phenotypes for other organs or image modalities.

Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets.

Genetic mechanisms of blood pressure (BP) regulation remain poorly defined. Using kidney-specific epigenomic annotations and 3D genome information we generated and validated gene expression prediction models for the purpose of transcriptome-wide association studies in 700 human kidneys. We identified 889 kidney genes associated with BP of which 399 were prioritised as contributors to BP regulation. Imputation of kidney proteome and microRNAome uncovered 97 renal proteins and 11 miRNAs associated with BP. Integration with plasma proteomics and metabolomics illuminated circulating levels of myo-inositol, 4-guanidinobutanoate and angiotensinogen as downstream effectors of several kidney BP genes (SLC5A11, AGMAT, AGT, respectively). We showed that genetically determined reduction in renal expression may mimic the effects of rare loss-of-function variants on kidney mRNA/protein and lead to an increase in BP (e.g., ENPEP). We demonstrated a strong correlation (r = 0.81) in expression of protein-coding genes between cells harvested from urine and the kidney highlighting a diagnostic potential of urinary cell transcriptomics. We uncovered adenylyl cyclase activators as a repurposing opportunity for hypertension and illustrated examples of BP-elevating effects of anticancer drugs (e.g. tubulin polymerisation inhibitors). Collectively, our studies provide new biological insights into genetic regulation of BP with potential to drive clinical translation in hypertension.

Clinical risk associated with COVID-19 among 86000 patients with congenital heart disease.

OBJECTIVE: To determine the magnitude of any excess risk of mortality and hospitalisation due to COVID-19 infection in patients with congenital heart disease (CHD) in the UK healthcare system. METHODS: Matched case-control study within the Clinical Practice Research Datalink study of anonymised general practice records in the National Health Service in England. Patients with CHD were stratified for disease severity according to the European Society of Cardiology guidelines. Presence of a positive COVID-19 test, hospitalisation with a diagnosis of COVID-19 and COVID-19-related mortality were compared in case and control groups. RESULTS: 86 441 patients with CHD and 335 839 controls were studied. Of patients with a positive COVID-19 test, patients with CHD were more likely than controls to be hospitalised (22.4% vs 14.5%; OR=1.77 (95% CI 1.60 to 1.96); p=2.11e-28) and suffer COVID-19-related death (6.1% vs 3.8%; OR=1.60 (95% CI 1.35 to 1.89); p=7.00e-08). The excess risk of COVID-19 hospitalisation and death rose with increasing physiological severity of CHD (presence of pulmonary vascular disease and/or cyanosis), rather than anatomical complexity. CONCLUSIONS: In this study of the COVID-19 pandemic experience, using population health records in over 86000 patients with CHD in England, patients with CHD with COVID-19 were at around 50-75% higher risk of hospitalisation and mortality compared with matched controls with COVID-19. We provide the first primary care-derived estimates for COVID-19 hospitalisation and case-fatality rates in patients with CHD. Some factors predictive of worse COVID-19 outcome in general populations (such as non-white ethnic group), and other CHD-specific comorbidities (such as pulmonary hypertension), influenced outcomes among patients with CHD.

Predicting congenital renal tract malformation genes using machine learning.

Congenital renal tract malformations (RTMs) are the major cause of severe kidney failure in children. Studies to date have identified defined genetic causes for only a minority of human RTMs. While some RTMs may be caused by poorly defined environmental perturbations affecting organogenesis, it is likely that numerous causative genetic variants have yet to be identified. Unfortunately, the speed of discovering further genetic causes for RTMs is limited by challenges in prioritising candidate genes harbouring sequence variants. Here, we exploited the computer-based artificial intelligence methodology of supervised machine learning to identify genes with a high probability of being involved in renal development. These genes, when mutated, are promising candidates for causing RTMs. With this methodology, the machine learning classifier determines which attributes are common to renal development genes and identifies genes possessing these attributes. Here we report the validation of an RTM gene classifier and provide predictions of the RTM association status for all protein-coding genes in the mouse genome. Overall, our predictions, whilst not definitive, can inform the prioritisation of genes when evaluating patient sequence data for genetic diagnosis. This knowledge of renal developmental genes will accelerate the processes of reaching a genetic diagnosis for patients born with RTMs.

Activation of telomerase by TA-65 enhances immunity and reduces inflammation post myocardial infarction.

Myocardial infarction (MI) accelerates immune ageing characterised by lymphopenia, expansion of terminally differentiated CD8+ T-lymphocytes (CD8+ TEMRA) and inflammation. Pre-clinical data showed that TA-65, an oral telomerase activator, reduced immune ageing and inflammation after MI. We conducted a double blinded randomised controlled pilot trial evaluating the use of TA-65 to reduce immune cell ageing in patients following MI. Ninety MI patients aged over 65 years were randomised to either TA-65 (16 mg daily) or placebo for 12 months. Peripheral blood leucocytes were analysed by flow cytometry. The pre-defined primary endpoint was the proportion of CD8+ T-lymphocytes which were CD8+ TEMRA after 12 months. Secondary outcomes included high-sensitivity C-reactive protein (hsCRP) levels. Median age of participants was 71 years. Proportions of CD8+ TEMRA did not differ after 12 months between treatment groups. There was a significant increase in mean total lymphocyte count in the TA-65 group after 12 months (estimated treatment effect: + 285 cells/µl (95% CI: 117-452 cells/ µ l, p < 0.004), driven by significant increases from baseline in CD3+, CD4+, and CD8+ T-lymphocytes, B-lymphocytes and natural killer cells. No increase in lymphocyte populations was seen in the placebo group. At 12 months, hsCRP was 62% lower in the TA-65 group compared to placebo (1.1 vs. 2.9 mg/L). Patients in the TA-65 arm experienced significantly fewer adverse events (130 vs. 185, p = 0.002). TA-65 did not alter CD8+ TEMRA but increased all major lymphocyte subsets and reduced hsCRP in elderly patients with MI after 12 months.

Rationale, Design, and the Baseline Characteristics of the RHDGen (The Genetics of Rheumatic Heart Disease) Network Study†.

BACKGROUND: The genetics of rheumatic heart disease (RHDGen) Network was developed to assist the discovery and validation of genetic variations and biomarkers of risk for rheumatic heart disease (RHD) in continental Africans, as a part of the global fight to control and eradicate rheumatic fever/RHD. Thus, we describe the rationale and design of the RHDGen study, comprising participants from 8 African countries. METHODS: RHDGen screened potential participants using echocardiography, thereafter enrolling RHD cases and ethnically-matched controls for whom case characteristics were documented. Biological samples were collected for conducting genetic analyses, including a discovery case-control genome-wide association study (GWAS) and a replication trio family study. Additional biological samples were also collected, and processed, for the measurement of biomarker analytes and the biomarker analyses are underway. RESULTS: Participants were enrolled into RHDGen between December 2012 and March 2018. For GWAS, 2548 RHD cases and 2261 controls (3301 women [69%]; mean age [SD], 37 [16.3] years) were available. RHD cases were predominantly Black (66%), Admixed (24%), and other ethnicities (10%). Among RHD cases, 34% were asymptomatic, 26% had prior valve surgery, and 23% had atrial fibrillation. The trio family replication arm included 116 RHD trio probands and 232 parents. CONCLUSIONS: RHDGen presents a rare opportunity to identify relevant patterns of genetic factors and biomarkers in Africans that may be associated with differential RHD risk. Furthermore, the RHDGen Network provides a platform for further work on fully elucidating the causes and mechanisms associated with RHD susceptibility and development.

Data on cardiac lncRNA STX18-AS1 expression in developing human hearts and function during in vitro hESC-cardiomyocyte differentiation.

This article presents data concerning STX18-AS1, a long noncoding RNA gene identified from a Genome-wide association study of Atrial Septal Defect (ASD). The data describes its expression patterns in human tissues and functions in regulating cardiomyocyte differentiation in vitro. STX18-AS1 is a lncRNA with a higher abundance in developing tissues, including hearts. Its transcription distribution within the embryonic hearts during key heart septation stages supports STX18-AS1's association with risk SNPs for ASD. The CRISPR stem cell pool in which STX18-AS1 was knocked down, showed reduced CM differentiation efficiency and lower expression of key cardiac transcriptional factors. This indicated its regulative role in supporting the lineage specification from cardiac mesoderm into cardiac progenitors and cardiomyocytes. These data can benefit the understanding of human embryonic heart developmental biology, and the time-course changes of cardiac transcriptional factors during in vitro cardiomyocyte differentiation from human embryonic stem cells.

Significantly increased risk of chronic obstructive pulmonary disease amongst adults with predominantly mild congenital heart disease.

Adults with congenital heart disease (CHD) face increased risk of various comorbid diseases. Previous work on lung dysfunction in this population has mainly focused on restrictive lung disease, in patients with severe CHD phenotypes. We examined the association of mild CHD with chronic obstructive pulmonary disease (COPD) in the UK Biobank (UKB). Electronic health records (EHR) were used to identify 3385 CHD cases and 479,765 healthy controls in UKB, before performing a case-control analysis over a 20-year study period for a total of > 9.5 M person-years of follow-up. Our analysis showed that UKB participants with CHD are at substantially greater risk of developing COPD than healthy controls (8.7% vs 3.1% prevalence, unadjusted OR 2.98, 95% CI 2.63, 3.36, P = 1.40e-53). Slightly increased rates of smoking were observed amongst CHD cases, however the association with COPD was shown to be robust to adjustment for smoking and other factors known to modulate COPD risk within a multivariable-adjusted Cox regression framework (fully adjusted HR 2.21, 95% CI 1.97, 2.48, P = 5.5e-41). Care for adults with CHD should aim to mitigate their increased risk of COPD, possibly via increased smoking cessation support.

Investigation of Copy Number Variation in South African Patients With Congenital Heart Defects.

BACKGROUND: Congenital heart disease (CHD) is a leading non-infectious cause of pediatric morbidity and mortality worldwide. Although the etiology of CHD is poorly understood, genetic factors including copy number variants (CNVs) contribute to the risk of CHD in individuals of European ancestry. The presence of rare CNVs in African CHD populations is unknown. This study aimed to identify pathogenic and likely pathogenic CNVs in South African patients with CHD. METHODS: Genotyping was performed on 90 patients with nonsyndromic CHD using the Affymetrix CytoScan HD platform. These data were used to identify large, rare CNVs in known CHD-associated genes and candidate genes. RESULTS: We identified eight CNVs overlapping known CHD-associated genes (GATA4, CRKL, TBX1, FLT4, B3GAT3, NSD1) in six patients. The analysis also revealed CNVs encompassing five candidate genes likely to play a role in the development of CHD (DGCR8, KDM2A, JARID2, FSTL1, CYFIP1) in five patients. One patient was found to have 47, XXY karyotype. We report a total discovery yield of 6.7%, with 5.6% of the cohort carrying pathogenic or likely pathogenic CNVs expected to cause the observed phenotypes. CONCLUSIONS: In this study, we show that chromosomal microarray is an effective technique for identifying CNVs in African patients diagnosed with CHD and have demonstrated results similar to previous CHD genetic studies in Europeans. Novel potential CHD genes were also identified, indicating the value of genetic studies of CHD in ancestrally diverse populations.

A review of causal discovery methods for molecular network analysis.

BACKGROUND: With the increasing availability and size of multi-omics datasets, investigating the casual relationships between molecular phenotypes has become an important aspect of exploring underlying biology andgenetics. There are an increasing number of methodlogies that have been developed and applied to moleular networks to investigate these causal interactions. METHODS: We have introduced and reviewed the available methods for building large-scale causal molecular networks that have been developed and applied in the past decade. RESULTS: In this review we have identified and summarized the existing methods for infering causality in large-scale causal molecular networks, and discussed important factors that will need to be considered in future research in this area. CONCLUSION: Existing methods to infering causal molecular networks have their own strengths and limitations so there is no one best approach, and it is instead down to the discretion of the researcher. This review also to discusses some of the current limitations to biological interpretation of these networks, and important factors to consider for future studies on molecular networks.

Joint analysis of functionally related genes yields further candidates associated with Tetralogy of Fallot.

Although several genes involved in the development of Tetralogy of Fallot have been identified, no genetic diagnosis is available for the majority of patients. Low statistical power may have prevented the identification of further causative genes in gene-by-gene survey analyses. Thus, bigger samples and/or novel analytic approaches may be necessary. We studied if a joint analysis of groups of functionally related genes might be a useful alternative approach. Our reanalysis of whole-exome sequencing data identified 12 groups of genes that exceedingly contribute to the burden of Tetralogy of Fallot. Further analysis of those groups showed that genes with high-impact variants tend to interact with each other. Thus, our results strongly suggest that additional candidate genes may be found by studying the protein interaction network of known causative genes. Moreover, our results show that the joint analysis of functionally related genes can be a useful complementary approach to classical single-gene analyses.

Genetic analyses of circulating PUFA-derived mediators identifies heritable dihydroxyeicosatrienoic acid species.

Estimates of heritability are the first step in identifying a trait with substantial variation due to genetic factors. Large-scale genetic analyses can identify the DNA variants that influence the levels of circulating lipid species and the statistical technique Mendelian randomisation can use these DNA variants to address potential causality of these lipids in disease. We estimated the heritability of plasma eicosanoids, octadecanoids and docosanoids to identify those lipid species with substantial heritability. We analysed plasma lipid mediators in 31 White British families (196 participants) ascertained for high blood pressure and deeply clinically and biochemically phenotyped over a 25-year period. We found that the dihydroxyeicosatrienoic acid (DHET) species, 11,12-DHET and 14,15-DHET, products of arachidonic acid metabolism by cytochrome P450 (CYP) monooxygenase and soluble epoxide hydrolase (sEH), exhibited substantial heritability (h2 = 33%-37%; Padj<0.05). Identification of these two heritable bioactive lipid species allows for future large-scale, targeted, lipidomics-genomics analyses to address causality in cardiovascular and other diseases.

Data-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases.

BACKGROUND: Rheumatic heart disease (RHD) remains a major source of morbidity and mortality in developing countries. A deeper insight into the pathogenetic mechanisms underlying RHD could provide opportunities for drug repurposing, guide recommendations for secondary penicillin prophylaxis, and/or inform development of near-patient diagnostics. METHODS: We performed quantitative proteomics using Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectrometry (SWATH-MS) to screen protein expression in 215 African patients with severe RHD, and 230 controls. We applied a machine learning (ML) approach to feature selection among the 366 proteins quantifiable in at least 40% of samples, using the Boruta wrapper algorithm. The case-control differences and contribution to Area Under the Receiver Operating Curve (AUC) for each of the 56 proteins identified by the Boruta algorithm were calculated by Logistic Regression adjusted for age, sex and BMI. Biological pathways and functions enriched for proteins were identified using ClueGo pathway analyses. RESULTS: Adiponectin, complement component C7 and fibulin-1, a component of heart valve matrix, were significantly higher in cases when compared with controls. Ficolin-3, a protein with calcium-independent lectin activity that activates the complement pathway, was lower in cases than controls. The top six biomarkers from the Boruta analyses conferred an AUC of 0.90 indicating excellent discriminatory capacity between RHD cases and controls. CONCLUSIONS: These results support the presence of an ongoing inflammatory response in RHD, at a time when severe valve disease has developed, and distant from previous episodes of acute rheumatic fever. This biomarker signature could have potential utility in recognizing different degrees of ongoing inflammation in RHD patients, which may, in turn, be related to prognostic severity.

Genetic resiliency associated with dominant lethal TPM1 mutation causing atrial septal defect with high heritability.

Analysis of large-scale human genomic data has yielded unexplained mutations known to cause severe disease in healthy individuals. Here, we report the unexpected recovery of a rare dominant lethal mutation in TPM1, a sarcomeric actin-binding protein, in eight individuals with large atrial septal defect (ASD) in a five-generation pedigree. Mice with Tpm1 mutation exhibit early embryonic lethality with disrupted myofibril assembly and no heartbeat. However, patient-induced pluripotent-stem-cell-derived cardiomyocytes show normal beating with mild myofilament defect, indicating disease suppression. A variant in TLN2, another myofilament actin-binding protein, is identified as a candidate suppressor. Mouse CRISPR knock-in (KI) of both the TLN2 and TPM1 variants rescues heart beating, with near-term fetuses exhibiting large ASD. Thus, the role of TPM1 in ASD pathogenesis unfolds with suppression of its embryonic lethality by protective TLN2 variant. These findings provide evidence that genetic resiliency can arise with genetic suppression of a deleterious mutation.

Modern genomic techniques in the identification of genetic causes of cardiomyopathy.

Over the past three decades numerous disease-causing genes have been linked to the pathogenesis of heritable cardiomyopathies, but many causal genes are yet to be identified. Next-generation sequencing (NGS) platforms have revolutionised clinical testing capacity in familial cardiomyopathy. In this review, we summarise how NGS technologies have advanced our understanding of genetic non-syndromic cardiomyopathy over the last decade. First, 26 putative new disease-causing genes have been identified to date, mostly from whole-exome sequencing, and some of which (FLNC, MTO1, HCN4) have had a considerable clinical impact and are now included in routine diagnostic gene panels. Second, we consider challenges in variant interpretation and the importance of large-scale NGS population control cohorts for this purpose. Third, an emerging role of common variation in some forms of genetic cardiomyopathy is being elucidated through recent studies which have illustrated an additive effect of numerous polymorphic loci on cardiac parameters; this may explain phenotypic variability and low rates of genetic diagnosis from sequencing studies. Finally, we discuss the clinical utility of genetic testing in cardiomyopathy in Western settings, where NGS panel testing of core disease genes is currently recommended with possible implications for patient management. Given the findings of recent studies, whole-exome or whole-genome sequencing should be considered in patients of non-European ancestry with clearly familial disease, or severe paediatric disease, when no result is obtained on panel sequencing. The clinical utility of polygenic risk assessment needs to be investigated further in patients with unexplained dilated cardiomyopathy and hypertrophic cardiomyopathy in whom a pathogenic variant is not identified.