NPSV-deep: a deep learning method for genotyping structural variants in short read genome sequencing data.

MOTIVATION: Structural variants (SVs) play a causal role in numerous diseases but can be difficult to detect and accurately genotype (determine zygosity) with short-read genome sequencing data (SRS). Improving SV genotyping accuracy in SRS data, particularly for the many SVs first detected with long-read sequencing, will improve our understanding of genetic variation. RESULTS: NPSV-deep is a deep learning-based approach for genotyping previously reported insertion and deletion SVs that recasts this task as an image similarity problem. NPSV-deep predicts the SV genotype based on the similarity between pileup images generated from the actual SRS data and matching SRS simulations. We show that NPSV-deep consistently matches or improves upon the state-of-the-art for SV genotyping accuracy across different SV call sets, samples and variant types, including a 25% reduction in genotyping errors for the Genome-in-a-Bottle (GIAB) high-confidence SVs. NPSV-deep is not limited to the SVs as described; it improves deletion genotyping concordance a further 1.5 percentage points for GIAB SVs (92%) by automatically correcting imprecise/incorrectly described SVs. AVAILABILITY AND IMPLEMENTATION: Python/C++ source code and pre-trained models freely available at https://github.com/mlinderm/npsv2.

Identifying novel data-driven subgroups in congenital heart disease using multi-modal measures of brain structure.

Individuals with congenital heart disease (CHD) have an increased risk of neurodevelopmental impairments. Given the hypothesized complexity linking genomics, atypical brain structure, cardiac diagnoses and their management, and neurodevelopmental outcomes, unsupervised methods may provide unique insight into neurodevelopmental variability in CHD. Using data from the Pediatric Cardiac Genomics Consortium Brain and Genes study, we identified data-driven subgroups of individuals with CHD from measures of brain structure. Using structural magnetic resonance imaging (MRI; N = 93; cortical thickness, cortical volume, and subcortical volume), we identified subgroups that differed primarily on cardiac anatomic lesion and language ability. In contrast, using diffusion MRI (N = 88; white matter connectivity strength), we identified subgroups that were characterized by differences in associations with rare genetic variants and visual-motor function. This work provides insight into the differential impacts of cardiac lesions and genomic variation on brain growth and architecture in patients with CHD, with potentially distinct effects on neurodevelopmental outcomes.

Physician and informal care use explained by the Pediatric Quality of Life Inventory (PedsQL) in children with suspected genetic disorders.

PURPOSE: To examine associations between Pediatric Quality of Life Inventory (PedsQL) 4.0 Generic Core Scales and PedsQL Infant Scales with formal health care resource utilization (HCRU) and informal caregiver burden. METHODS: We studied a pediatric cohort of 837 patients (median age: 8.4 years) with suspected genetic disorders enrolled January 2019 through July 2021 in the NYCKidSeq program for diagnostic sequencing. Using linked ~ nine-month longitudinal survey and physician claims data collected through May 2022, we modeled the association between baseline PedsQL scores and post-baseline HCRU (median follow-up: 21.1 months) and informal care. We also assessed the longitudinal change in PedsQL scores with physician services using linear mixed-effects models. RESULTS: Lower PedsQL total and physical health scores were independently associated with increases in 18-month physician services, encounters, and weekly informal care. Comparing low vs. median total scores, increases were 10.6 services (95% CI: 1.0-24.6), 3.3 encounters (95% CI: 0.5-6.8), and $668 (95% CI: $350-965), respectively. For the psychosocial domain, higher scores were associated with decreased informal care. Based on adjusted linear mixed-effects modeling, every additional ten physician services was associated with diminished improvement in longitudinal PedsQL total score trajectories by 1.1 point (95% confidence interval: 0.6-1.6) on average. Similar trends were observed in the physical and psychosocial domains. CONCLUSION: PedsQL scores were independently associated with higher utilization of physician services and informal care. Moreover, longitudinal trajectories of PedsQL scores became less favorable with increased physician services. Adding PedsQL survey instruments to conventional measures for improved risk stratification should be evaluated in further research.

The Pediatric Quality of Life Inventory (PedsQL) is widely used to measure health-related quality of life in pediatric patients; however, few studies have examined whether the PedsQL is indicative of longitudinal outcomes of morbidity and health care needs. This study captures associations between PedsQL scores with utilization of physician and informal care in children with suspected genetic disorders. We demonstrate that lower PedsQL total and physical health scores are independently associated with greater utilization of physician services and informal care. Moreover, longitudinal trajectories of PedsQL scores become less favorable with increased physician services. Results can inform future applications of PedsQL instruments.

Diagnostic yield after next-generation sequencing in pediatric cardiovascular disease.

Genetic testing with exome sequencing and genome sequencing is increasingly offered to infants and children with cardiovascular diseases. However, the rates of positive diagnoses after genetic testing within the different categories of cardiac disease and phenotypic subtypes of congenital heart disease (CHD) have been little studied. We report the diagnostic yield after next-generation sequencing in 500 patients with CHD from diverse population subgroups that were enrolled at three different sites in the Clinical Sequencing Evidence-Generating Research consortium. Patients were ascertained due to a primary cardiovascular issue comprising arrhythmia, cardiomyopathy, and/or CHD, and corresponding human phenotype ontology terms were selected to describe the cardiac and extracardiac findings. We examined the diagnostic yield for patients with arrhythmia, cardiomyopathy, and/or CHD and phenotypic subtypes of CHD comprising conotruncal defects, heterotaxy, left ventricular outflow tract obstruction, septal defects, and "other" heart defects. We found a significant increase in the frequency of positive findings for patients who underwent genome sequencing compared to exome sequencing and for syndromic cardiac defects compared to isolated cardiac defects. We also found significantly higher diagnostic rates for patients who presented with isolated cardiomyopathy compared to isolated CHD. For patients with syndromic presentations who underwent genome sequencing, there were significant differences in the numbers of positive diagnoses for phenotypic subcategories of CHD, ranging from 31.7% for septal defects to 60% for "other". Despite variation in the diagnostic yield at each site, our results support genetic testing in pediatric patients with syndromic and isolated cardiovascular issues and in all subtypes of CHD.

Feeder-free generation and characterization of endocardial and cardiac valve cells from human pluripotent stem cells.

Valvular heart disease presents a significant health burden, yet advancements in valve biology and therapeutics have been hindered by the lack of accessibility to human valve cells. In this study, we have developed a scalable and feeder-free method to differentiate human induced pluripotent stem cells (iPSCs) into endocardial cells, which are transcriptionally and phenotypically distinct from vascular endothelial cells. These endocardial cells can be challenged to undergo endothelial-to-mesenchymal transition (EndMT), after which two distinct populations emerge-one population undergoes EndMT to become valvular interstitial cells (VICs), while the other population reinforces their endothelial identity to become valvular endothelial cells (VECs). We then characterized these populations through bulk RNA-seq transcriptome analyses and compared our VIC and VEC populations to pseudobulk data generated from normal valve tissue of a 15-week-old human fetus. By increasing the accessibility to these cell populations, we aim to accelerate discoveries for cardiac valve biology and disease.

Automated Identification of Germline de novo Mutations in Family Trios: A Consensus-Based Informatic Approach.

Accurate identification of germline de novo variants (DNVs) remains a challenging problem despite rapid advances in sequencing technologies as well as methods for the analysis of the data they generate, with putative solutions often involving ad hoc filters and visual inspection of identified variants. Here, we present a purely informatic method for the identification of DNVs by analyzing short-read genome sequencing data from proband-parent trios. Our method evaluates variant calls generated by three genome sequence analysis pipelines utilizing different algorithms-GATK HaplotypeCaller, DeepTrio and Velsera GRAF-exploring the assumption that a requirement of consensus can serve as an effective filter for high-quality DNVs. We assessed the efficacy of our method by testing DNVs identified using a previously established, highly accurate classification procedure that partially relied on manual inspection and used Sanger sequencing to validate a DNV subset comprising less confident calls. The results show that our method is highly precise and that applying a force-calling procedure to putative variants further removes false-positive calls, increasing precision of the workflow to 99.6%. Our method also identified novel DNVs, 87% of which were validated, indicating it offers a higher recall rate without compromising accuracy. We have implemented this method as an automated bioinformatics workflow suitable for large-scale analyses without need for manual intervention.

Quantitative Prediction of Right Ventricular Size and Function From the ECG.

BACKGROUND: Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep learning-enabled ECG analysis for estimation of right ventricular (RV) size or function is unexplored. METHODS AND RESULTS: We trained a deep learning-ECG model to predict RV dilation (RVEDV >120 mL/m2), RV dysfunction (RVEF ≤40%), and numerical RVEDV and RVEF from a 12-lead ECG paired with reference-standard cardiac magnetic resonance imaging volumetric measurements in UK Biobank (UKBB; n=42 938). We fine-tuned in a multicenter health system (MSHoriginal [Mount Sinai Hospital]; n=3019) with prospective validation over 4 months (MSHvalidation; n=115). We evaluated performance with area under the receiver operating characteristic curve for categorical and mean absolute error for continuous measures overall and in key subgroups. We assessed the association of RVEF prediction with transplant-free survival with Cox proportional hazards models. The prevalence of RV dysfunction for UKBB/MSHoriginal/MSHvalidation cohorts was 1.0%/18.0%/15.7%, respectively. RV dysfunction model area under the receiver operating characteristic curve for UKBB/MSHoriginal/MSHvalidation cohorts was 0.86/0.81/0.77, respectively. The prevalence of RV dilation for UKBB/MSHoriginal/MSHvalidation cohorts was 1.6%/10.6%/4.3%. RV dilation model area under the receiver operating characteristic curve for UKBB/MSHoriginal/MSHvalidation cohorts was 0.91/0.81/0.92, respectively. MSHoriginal mean absolute error was RVEF=7.8% and RVEDV=17.6 mL/m2. The performance of the RVEF model was similar in key subgroups including with and without left ventricular dysfunction. Over a median follow-up of 2.3 years, predicted RVEF was associated with adjusted transplant-free survival (hazard ratio, 1.40 for each 10% decrease; P=0.031). CONCLUSIONS: Deep learning-ECG analysis can identify significant cardiac magnetic resonance imaging RV dysfunction and dilation with good performance. Predicted RVEF is associated with clinical outcome.

Cardiac genetic test yields and genotype-phenotype correlations from large cohort investigated by medical examiner's office.

BACKGROUND: Few reports describe the yield of postmortem genetic testing from medical examiners' offices or correlate genetic test results with autopsy-confirmed phenotypes from a large cohort. OBJECTIVES: To report results from cardiomyopathy- and cardiac arrhythmia-associated genetic testing in conjunction with autopsy findings of cases investigated at the United States' largest medical examiner office. METHODS: Postmortem cases tested from 2015 to 2022 with a cardiomyopathy- and cardiac arrhythmia-associated gene panel were reviewed. American College of Medical Genetics and Genomics/Association for Molecular Pathology guidelines were used to classify variant pathogenicity. Correlations of pathogenic/likely pathogenic variants (P/LPVs) with cardiac pathology were evaluated. RESULTS: The cohort included 1107 decedents of diverse ages and ethnicities. P/LPVs were detected in 87 (7.9%) cases, with 73 and 14 variants in cardiomyopathy and cardiac arrhythmia genes, respectively. Variants of uncertain significance were detected in 437 (39.5%) cases. The diagnostic yield (percentage of P/LPV) in decedents with cardiomyopathy (26.1%) was significantly higher than those without (P<.0001). The diagnostic yield was significantly lower in infants (0.7%) than older age groups (ranging from 1 to 74 years old, 5.7%-25.9%), which had no statistical difference between their yields. The diagnostic yields by cardiac autopsy findings were 54.0% for hypertrophic cardiomyopathy, 47.1% for arrhythmogenic cardiomyopathy, 20.0% for myocardial fibrosis, 19.0% for dilated cardiomyopathy, and 11.3% for myocarditis. Most P/LPVs were in MYBPC3, TTN, PKP2, SCN5A, MYH7, and FLNC. Ten P/LPVs were novel. CONCLUSIONS: Our results support the importance of performing postmortem genetic testing on decedents of all ages with cardiomyopathy, cardiac lesions insufficient to diagnosis a specific cardiomyopathy (e.g., myocardial fibrosis), and myocarditis. Combined postmortem cardiac examination and genetic analysis are advantageous in accurately determining the underlying cause of death and informing effective clinical care of family members.

Functional dissection of human cardiac enhancers and noncoding de novo variants in congenital heart disease.

Rare coding mutations cause ∼45% of congenital heart disease (CHD). Noncoding mutations that perturb cis-regulatory elements (CREs) likely contribute to the remaining cases, but their identification has been problematic. Using a lentiviral massively parallel reporter assay (lentiMPRA) in human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs), we functionally evaluated 6,590 noncoding de novo variants (ncDNVs) prioritized from the whole-genome sequencing of 750 CHD trios. A total of 403 ncDNVs substantially affected cardiac CRE activity. A majority increased enhancer activity, often at regions with undetectable reference sequence activity. Of ten DNVs tested by introduction into their native genomic context, four altered the expression of neighboring genes and iPSC-CM transcriptional state. To prioritize future DNVs for functional testing, we used the MPRA data to develop a regression model, EpiCard. Analysis of an independent CHD cohort by EpiCard found enrichment of DNVs. Together, we developed a scalable system to measure the effect of ncDNVs on CRE activity and deployed it to systematically assess the contribution of ncDNVs to CHD.

HRAS-Mutant Cardiomyocyte Model of Multifocal Atrial Tachycardia.

BACKGROUND: Germline HRAS gain-of-function pathogenic variants cause Costello syndrome (CS). During early childhood, 50% of patients develop multifocal atrial tachycardia, a treatment-resistant tachyarrhythmia of unknown pathogenesis. This study investigated how overactive HRAS activity triggers arrhythmogenesis in atrial-like cardiomyocytes (ACMs) derived from human-induced pluripotent stem cells bearing CS-associated HRAS variants. METHODS: HRAS Gly12 mutations were introduced into a human-induced pluripotent stem cells-ACM reporter line. Human-induced pluripotent stem cells were generated from patients with CS exhibiting tachyarrhythmia. Calcium transients and action potentials were assessed in induced pluripotent stem cell-derived ACMs. Automated patch clamping assessed funny currents. HCN inhibitors targeted pacemaker-like activity in mutant ACMs. Transcriptomic data were analyzed via differential gene expression and gene ontology. Immunoblotting evaluated protein expression associated with calcium handling and pacemaker-nodal expression. RESULTS: ACMs harboring HRAS variants displayed higher beating rates compared with healthy controls. The hyperpolarization activated cyclic nucleotide gated potassium channel inhibitor ivabradine and the Nav1.5 blocker flecainide significantly decreased beating rates in mutant ACMs, whereas voltage-gated calcium channel 1.2 blocker verapamil attenuated their irregularity. Electrophysiological assessment revealed an increased number of pacemaker-like cells with elevated funny current densities among mutant ACMs. Mutant ACMs demonstrated elevated gene expression (ie, ISL1, TBX3, TBX18) related to intracellular calcium homeostasis, heart rate, RAS signaling, and induction of pacemaker-nodal-like transcriptional programming. Immunoblotting confirmed increased protein levels for genes of interest and suppressed MAPK (mitogen-activated protein kinase) activity in mutant ACMs. CONCLUSIONS: CS-associated gain-of-function HRASG12 mutations in induced pluripotent stem cells-derived ACMs trigger transcriptional changes associated with enhanced automaticity and arrhythmic activity consistent with multifocal atrial tachycardia. This is the first human-induced pluripotent stem cell model establishing the mechanistic basis for multifocal atrial tachycardia in CS.

Genome Sequencing is Critical for Forecasting Outcomes following Congenital Cardiac Surgery.

While genome sequencing has transformed medicine by elucidating the genetic underpinnings of both rare and common complex disorders, its utility to predict clinical outcomes remains understudied. Here, we used artificial intelligence (AI) technologies to explore the predictive value of genome sequencing in forecasting clinical outcomes following surgery for congenital heart defects (CHD). We report results for a cohort of 2,253 CHD patients from the Pediatric Cardiac Genomics Consortium with a broad range of complex heart defects, pre- and post-operative clinical variables and exome sequencing. Damaging genotypes in chromatin-modifying and cilia-related genes were associated with an elevated risk of adverse post-operative outcomes, including mortality, cardiac arrest and prolonged mechanical ventilation. The impact of damaging genotypes was further amplified in the context of specific CHD phenotypes, surgical complexity and extra-cardiac anomalies. The absence of a damaging genotype in chromatin-modifying and cilia-related genes was also informative, reducing the risk for adverse postoperative outcomes. Thus, genome sequencing enriches the ability to forecast outcomes following congenital cardiac surgery.

Evaluating parental personal utility of pediatric genetic and genomic testing in a diverse, multilingual population.

There is increasing evidence of the clinical utility of genetic and genomic testing (GT); however, factors influencing personal utility of GT, especially in diverse, multilingual populations, remain unclear. We explored these factors in a diverse cohort of parents/guardians (participants) whose children received clinical GT through the NYCKidSeq program. A total of 847 participants completed surveys at baseline, post-results disclosure, and 6 months (6m) post-results. The largest population groups were Hispanic/Latino(a) (48%), White/European American (24%), and Black/African American (16%). Personal utility was assessed using the Personal Utility (PrU) scale, adapted for pediatric populations and included on the surveys. Three PrU subscales were identified using factor analysis: practical, educational, and parental psychological utility. Overall personal utility summary score and the three subscales significantly decreased after receiving results and over time. Hispanic/Latino(a) participants identified greater overall personal utility than European American and African American participants at all time points (p < 0.001) as did participants whose children received positive/likely positive results compared with those with negative and uncertain results (post-results: p < 0.001 and p < 0.001; 6m post-results: p = 0.002 and p < 0.001, respectively). Post-results, higher subscale scores were associated with lower education levels (practical, parental psychological: p ≤ 0.02) and higher levels of trust in the healthcare system (practical, parental psychological: p ≤ 0.04). These findings help to understand the perspectives of diverse parents/guardians, which is critical to tailoring pre- and post-test counseling across a variety of populations and clinical settings.