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BackgroundSudden unexpected death in children is a tragic event. Understanding the genetics of sudden death in the young (SDY) enables family counseling and cascade screening. The objective of this study was to characterize genetic variation in an SDY cohort using whole genome sequencing.MethodsThe SDY Case Registry is a National Institutes of Health/Centers for Disease Control surveillance effort to discern the prevalence, causes, and risk factors for SDY. The SDY Case Registry prospectively collected clinical data and DNA biospecimens from SDY cases ResultsThe SDY cohort was 42% European, 30% African, 17% Hispanic, and 11% with mixed ancestries, and 39% female. Six percent of the cohort was found to harbor a pathogenic or likely pathogenic genetic variant in an epilepsy, cardiomyopathy or arrhythmia gene. The genomes of SDY cases, but not controls, were enriched for rare, damaging variants in epilepsy, cardiomyopathy and arrhythmia-related genes. A greater number of rare epilepsy genetic variants correlated with younger age at death.ConclusionsWhile damaging cardiomyopathy and arrhythmia genes are recognized contributors to SDY, we also observed an enrichment in epilepsy-related genes in the SDY cohort, and a correlation between rare epilepsy variation and younger age at death. These findings emphasize the importance of considering epilepsy genes when evaluating SDY.

Atrial fibrillation (AF) leads to significant morbidity and mortality, which is primarily related to stroke despite effective stroke prevention therapies. There remains a critical need for personalized, socially aware, equitable stroke risk prediction among patients with AF to enable optimal implementation of contemporary stroke-prevention therapies. In this brief report, we leverage innovative computational tools and high-quality, extensive data (1.8 m patients, augmented with social determinants of health information) to demonstrate the ability of a unique, explainable AI approach to improve the accuracy and equity of stroke risk prediction. Current risk stratification approaches are blind to social determinants of health and fail to adjust for unique contributions and interactions of variables upon stroke risk. In contrast, our results indicate that social determinants of health can be important modifiers of clinical variables and ultimately stroke risk. We hope that this analysis can provide evidence to drive better, more personalized, and equitable stroke risk stratification and prevention for patients with AF in the future.

Recent large-scale sequencing efforts have shed light on the genetic contribution to the etiology of congenital heart defects (CHD); however, the relative impact of genetics on clinical outcomes remains less understood. Outcomes analyses using genetic data are complicated by the intrinsic severity of the CHD lesion and by interactions with conditionally dependent clinical variables. Here we apply Bayesian Networks, an explainable Artificial Intelligence solution, to describe the intertwined relationships between clinical variables, demography, and genetics in a cohort of children with single ventricle CHD. As isolated variables, a damaging genetic variant in a gene related to abnormal heart morphology and prolonged ventilator support following stage I palliative surgery increased the probability of having a low Mental Developmental Index (MDI) score at 14 months of age by 1.9- and 5.8-fold, respectively. However, in combination, these variables acted synergistically to further increase the probability of a low MDI score by 10-fold. Likewise, genetic information was predictive of a favorable neurodevelopmental outcome. For example, the absence of a damaging variant in a known syndromic CHD gene and a shorter post-operative ventilator support increased the probability of a normal MDI score 1.7- and 2.4-fold, respectively, but in combination increased the probability of a good outcome by 59-fold. Our analyses suggest a modest genetic contribution to neurodevelopmental and growth outcomes as isolated variables, similar to known clinical predictors. By contrast, genetic, demographic, and clinical variables interact synergistically to markedly impact clinical outcomes. These findings underscore the importance of capturing and quantifying the impact of damaging genomic variants in the context of multiple, conditionally dependent variables, such as pre- and post-operative factors, and demography.

ObjectiveA multi-center, prospective, cross-sectional natural history study to define the clinical phenotype of Andersen-Tawil syndrome, validate its current diagnostic criteria, explore genotype-phenotype correlations, and establish clinically relevant endpoints for use in therapeutic trials.MethodsParticipants were followed at yearly intervals for two years. Outcome measures included attack frequency and duration, neurophysiological exercise testing and interictal muscle strength. Cardiac endpoints were QTc interval, presence of U-waves, frequency of ventricular ectopy and arrhythmias. Participants completed the SF-36 and underwent KCNJ2 gene analysis.Results28 participants were enrolled. The age range was 17 to 82 years. 23 participants harbored mutations in KCNJ2, including a new mutation, Y68D. All exhibited at least one skeletal feature with 26/28 exhibiting two or more. Common physical abnormalities were a small mandible (89%), low set ears (82%) and micromelia of hands or feet (71%). 26 participants reported periodic paralysis. The frequency of attacks varied from 12/week to 1/year, and duration from 12 minutes to 21 days. Common triggers for attacks were prolonged rest (85%) and exercise/exertion (73%). 20/25 had an abnormal long exercise test. A prolonged QTc interval was identified in 36% participants, U waves in 39% and ambulatory ECGs demonstrated runs of ventricular tachycardia in 32% and more than 10 000 ventricular couples in 14% of participants.InterpretationThere is extensive heterogeneity in both the spectrum and severity of Andersen-Tawil syndrome. Overall, the symptoms result in a significant impairment in quality of life. The cardiac and neurophysiological data could serve as outcome measures in future treatment trials.

Discovery of comorbidities (the concomitant occurrence of distinct medical conditions in the same patient) is a prerequisite for creating forecasting tools for downstream outcomes research. Current comorbidity discovery applications are designed for small datasets and use stratification to control for confounding variables such as age, sex, or ancestry. Stratification lowers false positive rates, but reduces power, as the size of the study cohort is decreased. Here, we describe a Poisson Binomial based approach to comorbidity discovery (PBC) designed for big-data applications that circumvents the need for stratification. PBC adjusts for confounding demographic variables on a per-patient basis, and models temporal relationships. We benchmark PBC using two datasets, the publicly available MIMIC-IV; and the entire Electronic Health Record (EHR) corpus of the University of Utah Hospital System, encompassing over 1.6 million patients, to compute comorbidity statistics on 4,623,841 pairs of potentially comorbid medical terms. The results of this computation are provided as a searchable web resource. Compared to current methods, the PBC approach reduces false positive associations, while retaining statistical power to discover true comorbidities.

BackgroundThe contribution of somatic mosaicism, or genetic mutations arising after oocyte fertilization, to congenital heart disease (CHD) is not well understood. Further, the relationship between mosaicism in blood and cardiovascular tissue has not been determined.ResultsWe developed a computational method, Expectation-Maximization-based detection of Mosaicism (EM-mosaic), to analyze mosaicism in exome sequences of 2530 CHD proband-parent trios. EM-mosaic detected 326 mosaic mutations in blood and/or cardiac tissue DNA. Of the 309 detected in blood DNA, 85/97 (88%) tested were independently confirmed, while 7/17 (41%) candidates of 17 detected in cardiac tissue were confirmed. MosaicHunter detected an additional 64 mosaics, of which 23/46 (50%) among 58 candidates from blood and 4/6 (67%) of 6 candidates from cardiac tissue confirmed. Twenty-five mosaic variants altered CHD-risk genes, affecting 1% of our cohort. Of these 25, 22/22 candidates tested were confirmed. Variants predicted as damaging had higher variant allele fraction than benign variants, suggesting a role in CHD. The frequency of mosaic variants above 10% mosaicism was 0.13/person in blood and 0.14/person in cardiac tissue. Analysis of 66 individuals with matched cardiac tissue available revealed both tissue-specific and shared mosaicism, with shared mosaics generally having higher allele fraction.ConclusionsWe estimate that ~1% of CHD probands have a mosaic variant detectable in blood that could contribute to cardiac malformations, particularly those damaging variants expressed at higher allele fraction compared to benign variants. Although blood is a readily-available DNA source, cardiac tissues analyzed contributed ~5% of somatic mosaic variants identified, indicating the value of tissue mosaicism analyses.

Many forms of congenital heart disease (CHD) have high morbidity-mortality rates and require challenging surgeries. Human amniocytes have important stem cell characteristics and could potentially provide patient-specific tissue for repairs of some types of CHDs. We report that amniocytes express features of poised cardiomyocytes. However, a variety of direct reprogramming approaches failed to convert their fetal and transcriptionally repressed state into bona fide cardiomyocytes. Induced-pluripotent stem cell (iPSC) reprogramming removes repression and converts amniocytes to a baseline pluripotent state. Based on molecular and electrophysiological signatures, iPSC reprogrammed amniocytes can be induced to differentiate into functionally immature, predominantly ventricular cardiomyocytes and a heterogeneous mixture of vascular and unspecified epithelial cells. Developmental time course analyses and pattern clustering of amniocyte-derived cardiomyocytes identifies numerous temporal co-regulators of cardiac induction and maturation as well as distinct sarcomeric and ion channel gene signatures. Normal fetal cardiomyocytes are derived by overcoming complex forms of transcriptional repression that suppress direct transdifferentiation of human amniocytes. These results suggest the possibility of using amniocytes as a source of patient-specific ventricular cardiomyocytes for cell therapies.SUMMARY STATEMENTAmniocytes are a possible source of patient-specific cardiomyocytes for newborns with congenital heart disease. Genome-wide DNA methylation patterns and transcriptional repressors preclude direct differentiation, but pluripotent reprogramming provides cardiomyocytes for dissecting genetic pathways contributing to this disease.