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Background A wide range of tools are available for the detection of copy number variants (CNVs) from whole-genome sequencing (WGS) data. However, none of them focus on clinically-relevant CNVs, such as those that are associated with known genetic syndromes. Such variants are often large in size, typically 1–5 Mb, but currently available CNV callers have been developed and benchmarked for the discovery of smaller variants. Thus, the ability of these programs to detect tens of real syndromic CNVs remains largely unknown. Results Here we present ConanVarvar, a tool which implements a complete workflow for the targeted analysis of large germline CNVs from WGS data. ConanVarvar comes with an intuitive R Shiny graphical user interface and annotates identified variants with information about 56 associated syndromic conditions. We benchmarked ConanVarvar and four other programs on a dataset containing real and simulated syndromic CNVs larger than 1 Mb. In comparison to other tools, ConanVarvar reports 10–30 times less false-positive variants without compromising sensitivity and is quicker to run, especially on large batches of samples. Conclusions ConanVarvar is a useful instrument for primary analysis in disease sequencing studies, where large CNVs could be the cause of disease.

A scalable, robust, and integrated differentiation platform for large‐scale production of human pluripotent stem cell‐cardiomyocyte (hPSC‐CM) in a stirred suspension bioreactor as a single‐unit operation was developed. This platform could become a valuable tool for mass production of functional hPSC‐CMs as a prerequisite for realizing their promising potential for therapeutic and industrial applications including drug discovery and toxicity assays. Recent advances in the generation of cardiomyocytes (CMs) from human pluripotent stem cells (hPSCs), in conjunction with the promising outcomes from preclinical and clinical studies, have raised new hopes for cardiac cell therapy. We report the development of a scalable, robust, and integrated differentiation platform for large‐scale production of hPSC‐CM aggregates in a stirred suspension bioreactor as a single‐unit operation. Precise modulation of the differentiation process by small molecule activation of WNT signaling, followed by inactivation of transforming growth factor‐β and WNT signaling and activation of sonic hedgehog signaling in hPSCs as size‐controlled aggregates led to the generation of approximately 100% beating CM spheroids containing virtually pure (∼90%) CMs in 10 days. Moreover, the developed differentiation strategy was universal, as demonstrated by testing multiple hPSC lines (5 human embryonic stem cell and 4 human inducible PSC lines) without cell sorting or selection. The produced hPSC‐CMs successfully expressed canonical lineage‐specific markers and showed high functionality, as demonstrated by microelectrode array and electrophysiology tests. This robust and universal platform could become a valuable tool for the mass production of functional hPSC‐CMs as a prerequisite for realizing their promising potential for therapeutic and industrial applications, including drug discovery and toxicity assays. Significance Recent advances in the generation of cardiomyocytes (CMs) from human pluripotent stem cells (hPSCs) and the development of novel cell therapy strategies using hPSC‐CMs (e.g., cardiac patches) in conjunction with promising preclinical and clinical studies, have raised new hopes for patients with end‐stage cardiovascular disease, which remains the leading cause of morbidity and mortality globally. In this study, a simplified, scalable, robust, and integrated differentiation platform was developed to generate clinical grade hPSC‐CMs as cell aggregates under chemically defined culture conditions. This approach resulted in approximately 100% beating CM spheroids with virtually pure (∼90%) functional cardiomyocytes in 10 days from multiple hPSC lines. This universal and robust bioprocessing platform can provide sufficient numbers of hPSC‐CMs for companies developing regenerative medicine technologies to rescue, replace, and help repair damaged heart tissues and for pharmaceutical companies developing advanced biologics and drugs for regeneration of lost heart tissue using high‐throughput technologies. It is believed that this technology can expedite clinical progress in these areas to achieve a meaningful impact on improving clinical outcomes, cost of care, and quality of life for those patients disabled and experiencing heart disease.

Background Congenital heart disease (CHD) is the most common congenital anomaly. Almost 90% of isolated cases have an unexplained genetic etiology after clinical testing. Non-canonical splice variants that disrupt mRNA splicing through the loss or creation of exon boundaries are not routinely captured and/or evaluated by standard clinical genetic tests. Recent computational algorithms such as SpliceAI have shown an ability to predict such variants, but are not specific to cardiac-expressed genes and transcriptional isoforms. Methods We used genome sequencing (GS) ( n  = 1101 CHD probands) and myocardial RNA-Sequencing (RNA-Seq) ( n  = 154 CHD and n  = 43 cardiomyopathy probands) to identify and validate splice disrupting variants, and to develop a heart-specific model for canonical and non-canonical splice variants that can be applied to patients with CHD and cardiomyopathy. Two thousand five hundred seventy GS samples from the Medical Genome Reference Bank were analyzed as healthy controls. Results Of 8583 rare DNA splice-disrupting variants initially identified using SpliceAI, 100 were associated with altered splice junctions in the corresponding patient myocardium affecting 95 genes. Using strength of myocardial gene expression and genome-wide DNA variant features that were confirmed to affect splicing in myocardial RNA, we trained a machine learning model for predicting cardiac-specific splice-disrupting variants (AUC 0.86 on internal validation). In a validation set of 48 CHD probands, the cardiac-specific model outperformed a SpliceAI model alone (AUC 0.94 vs 0.67 respectively). Application of this model to an additional 947 CHD probands with only GS data identified 1% patients with canonical and 11% patients with non-canonical splice-disrupting variants in CHD genes. Forty-nine percent of predicted splice-disrupting variants were intronic and > 10 bp from existing splice junctions. The burden of high-confidence splice-disrupting variants in CHD genes was 1.28-fold higher in CHD cases compared with healthy controls. Conclusions A new cardiac-specific in silico model was developed using complementary GS and RNA-Seq data that improved genetic yield by identifying a significant burden of non-canonical splice variants associated with CHD that would not be detectable through panel or exome sequencing.

BackgroundFollowing genomic advances, genetic testing options for paediatric patients with congenital heart disease (CHD) have evolved significantly. A single-site audit was conducted to assess testing outcomes and a survey created to explore family experiences and preferences.MethodAll genetic tests ordered in postcardiac surgery patients with CHD at The Children’s Hospital at Westmead between January 2017 and December 2021 were reviewed. Diagnostic yield, clinical and demographic factors, and testing trends over time were evaluated. Surveys were sent to parents of children who had met a clinical geneticist (n=112).ResultsGenetic testing was completed in 607 individuals (74 molecular testing; 533 cytogenetic testing only). The diagnostic rate was 36% and 9%, respectively. Use of molecular testing significantly increased over time (p=0.033), but yield did not (p=0.288). Molecular testing yield was high in neonates (64%), and patients with extracardiac anomalies (40%) or relevant family history (40%). Brain (p=0.022), haematological/cancer (p≤0.001), immune (p≤0.001), endocrine (p≤0.001) anomalies and intellectual disability (p=0.027) were associated with a diagnosis following cytogenetic testing. Short stature was significantly associated with diagnostic yield following molecular testing (p=0.012). Survey respondents (n=28) reported a positive experience (p=0.013) with minimal decisional regret (p=0.322).ConclusionCytogenetic testing remains an important first-tier test in CHD. Furthermore, molecular testing guided by a clinical geneticist generates a high rate of genetic diagnoses. Parents of children with CHD value genetic testing with little regret.

Purpose Congenital heart disease (CHD) affects up to 1% of live births. However, a genetic diagnosis is not made in most cases. The purpose of this study was to assess the outcomes of genome sequencing (GS) of a heterogeneous cohort of CHD patients. Methods Ninety-seven families with probands born with CHD requiring surgical correction were recruited for genome sequencing. At minimum, a proband–parents trio was sequenced per family. GS data were analyzed via a two-tiered method: application of a high-confidence gene screen (hcCHD), and comprehensive analysis. Identified variants were assessed for pathogenicity using the American College of Medical Genetics and Genomics–Association for Molecular Pathology (ACMG-AMP) guidelines. Results Clinically relevant genetic variants in known and emerging CHD genes were identified. The hcCHD screen identified a clinically actionable variant in 22% of families. Subsequent comprehensive analysis identified a clinically actionable variant in an additional 9% of families in genes with recent disease associations. Overall, this two-tiered approach provided a clinically relevant variant for 31% of families. Conclusions Interrogating GS data using our two-tiered method allowed identification of variants with high clinical utility in a third of our heterogeneous cohort. However, association of emerging genes with CHD etiology, and development of novel technologies for variant assessment and interpretation, will increase diagnostic yield during future reassessment of our GS data.

The most common cyanotic congenital heart disease (CHD) requiring management as a neonate is transposition of great arteries (TGA). Clinically, up to 50% of TGA patients develop some form of neurodevelopmental disability (NDD), thought to have a significant genetic component. A “ciliopathy” and links with laterality disorders have been proposed. This first report of whole genome sequencing in TGA, sought to identify clinically relevant variants contributing to heart, brain and laterality defects. Initial whole genome sequencing analyses on 100 TGA patients focussed on established disease genes related to CHD (n = 107), NDD (n = 659) and heterotaxy (n = 74). Single variant as well as copy number variant analyses were conducted. Variant pathogenicity was assessed using the American College of Medical Genetics and Genomics-Association for Molecular Pathology guidelines. Fifty-five putatively damaging variants were identified in established disease genes associated with CHD, NDD and heterotaxy; however, no clinically relevant variants could be attributed to disease. Notably, case-control analyses identified significantly more predicted-damaging, silent and total variants in TGA cases than healthy controls in established CHD genes (P < .001), NDD genes (P < .001) as well as across the three gene panels (P < .001). We present compelling evidence that the majority of TGA is not caused by monogenic rare variants and is most likely oligogenic and/or polygenic in nature, highlighting the complex genetic architecture and multifactorial influences on this CHD sub-type and its long-term sequelae. Assessment of variant burden in key heart, brain and/or laterality genes may be required to unravel the genetic contributions to TGA and related disabilities.

It is established that neurodevelopmental disability (NDD) is common in neonates undergoing complex surgery for congenital heart disease (CHD); however, the trajectory of disability over the lifetime of individuals with CHD is unknown. Several ‘big issues’ remain undetermined and further research is needed in order to optimise patient care and service delivery, to assess the efficacy of intervention strategies and to promote best outcomes in individuals of all ages with CHD. This review article discusses ‘gaps’ in our knowledge of NDD in CHD and proposes future directions.

Congenital heart disease (CHD) has a multifactorial aetiology, raising the possibility of an underlying genetic burden, predisposing to disease but also variable expression, including variation in disease severity, and incomplete penetrance. Using whole genome sequencing (WGS), the findings of this study, indicate that complex, critical CHD is distinct from other types of disease due to increased genetic burden in common variation, specifically among established CHD genes. Additionally, these findings highlight associations with regulatory genes and environmental “stressors” in the final presentation of disease.

Complexity stratification for CHD is an integral part of clinical research due to its heterogenous clinical presentation and outcomes. To support our ongoing research efforts into CHD requiring disease severity stratifications, a simplified CHD severity classification system was developed and verified, with potential utility for clinical researchers without specialist CHD knowledge or access to clinical/medical records. A two-tiered analysis approach was undertaken. First-tier analysis included the audit of a comprehensive system based on: i) timing of intervention, ii) cardiac morphology, and iii) cardiovascular physiology using real patient data (n = 30), across 10 common CHD lesions. Second-tier analysis allowed for a simplified version of the classification system using morphology as a stand-alone predictor. Twelve clinicians of varying specialities involved in CHD care ranked 10 common lesions from least to most severe based on typical presentation and clinical course. First-tier analysis identified that cardiac morphology was the principal driver of complexity. Second-tier analysis largely confirmed the ranking and classification of the lesions into the broad CHD severity groups, although some variation was noted, specifically among non-cardiac specialists. This simplified version of the classicisation system, with morphology as a stand-alone predictor of severity, allowed for effective stratification for the purposes of analysis. The findings presented here support this comprehensive and simple CHD severity classification system with broad utility in CHD research, particularly among clinicians and researchers with limited knowledge of CHD. The model may be applied to produce locally relevant research tools.

Up to 20% of children with congenital heart disease (CHD) undergoing cardiac surgery develop neurodevelopmental disabilities (NDD), with some studies reporting persistent impairment. Recent large-scale studies have demonstrated shared genetic mechanisms contributing to CHD and NDD. In this study, a targeted approach was applied to assess direct clinical applicability of this information. A gene panel comprising 148 known CHD and/or NDD genes was used to sequence 15 patients with CHD + NDD, 15 patients with CHD, and 15 healthy controls. The number and types of variants between the 3 groups were compared using Poisson log-linear regression, and the SNP-set (Sequence) Kernel Association Test-Optimized was used to conduct single-gene and gene-pathway burden analyses. A significant increase in rare (minor allele frequency < 0.01) and novel variants was identified between the CHD + NDD cohort and controls, P < .001 and P = .001, respectively. There was also a significant increase in rare variants in the CHD cohort compared with controls (P = .04). Rare variant burden analyses implicated pathways associated with “neurotransmitters,” “axon guidance,” and those incorporating “RASopathy” genes in the development of NDD in CHD patients. These findings suggest that an increase in novel and rare variants in known CHD and/or NDD genes is associated with the development of NDD in patients with CHD. Furthermore, burden analyses point toward rare variant burden specifically in pathways related to brain development and function as contributors to NDD. Although promising variants and pathways were identified, further research, utilizing whole-genome approaches, is required prior to demonstrating clinical utility in this patient group.