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BACKGROUND: Known genetic causes of congenital heart disease (CHD) explain METHODS: We tested de novo variants from trio studies of 2649 CHD probands and their parents, as well as rare (allele frequency, − 6 ) variants from 4472 CHD probands in the Pediatric Cardiac Genetics Consortium through a combined computational and in vitro approach. RESULTS: We identified 53 de novo and 74 rare variants in CHD cases that alter splicing and thus are loss of function. Of these, 77 variants are in known dominant, recessive, and candidate CHD genes, including KMT2D and RBFOX2 . In 1 case, we confirmed the variant’s predicted impact on RNA splicing in RNA transcripts from the proband’s cardiac tissue. Two probands were found to have 2 loss-of-function variants for recessive CHD genes HECTD1 and DYNC2H1 . In addition, SpliceAI—a predictive algorithm for altered RNA splicing—has a positive predictive value of ≈93% in our cohort. CONCLUSIONS: Through assessment of RNA splicing, we identified a new loss-of-function variant within a CHD gene in 78 probands, of whom 69 (1.5%; n=4472) did not have a previously established genetic explanation for CHD. Identification of splice-altering variants improves diagnostic classification and genetic diagnoses for CHD. REGISTRATION: URL: https://clinicaltrials.gov ; Unique identifier: NCT01196182.

Background: Congenital heart disease (CHD) is highly heritable, but the power to identify inherited risk has been limited to analyses of common variants in small cohorts. Methods: We performed reimputation of 4 CHD cohorts (n=55 342) to the TOPMed reference panel (freeze 5), permitting meta-analysis of 14 784 017 variants including 6 035 962 rare variants of high imputation quality as validated by whole genome sequencing. Results: Meta-analysis identified 16 novel loci, including 12 rare variants, which displayed moderate or large effect sizes (median odds ratio, 3.02) for 4 separate CHD categories. Analyses of chromatin structure link 13 of the genome-wide significant loci to key genes in cardiac development; rs373447426 (minor allele frequency, 0.003 [odds ratio, 3.37 for Conotruncal heart disease]; P =1.49×10 −8 ) is predicted to disrupt chromatin structure for 2 nearby genes BDH1 and DLG1 involved in Conotruncal development. A lead variant rs189203952 (minor allele frequency, 0.01 [odds ratio, 2.4 for left ventricular outflow tract obstruction]; P =1.46×10 − 8 ) is predicted to disrupt the binding sites of 4 transcription factors known to participate in cardiac development in the promoter of SPAG9 . A tissue-specific model of chromatin conformation suggests that common variant rs78256848 (minor allele frequency, 0.11 [odds ratio, 1.4 for Conotruncal heart disease]; P =2.6×10 − 8 ) physically interacts with NCAM1 ( P FDR =1.86×10 − 27 ), a neural adhesion molecule acting in cardiac development. Importantly, while each individual malformation displayed substantial heritability (observed h2 ranging from 0.26 for complex malformations to 0.37 for left ventricular outflow tract obstructive disease) the risk for different CHD malformations appeared to be separate, without genetic correlation measured by linkage disequilibrium score regression or regional colocalization. Conclusions: We describe a set of rare noncoding variants conferring significant risk for individual heart malformations which are linked to genes governing cardiac development. These results illustrate that the oligogenic basis of CHD and significant heritability may be linked to rare variants outside protein-coding regions conferring substantial risk for individual categories of cardiac malformation.

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.

The genetic architecture of atrial fibrillation (AF) encompasses low impact, common genetic variants and high impact, rare variants. Here, we characterize a high impact AF-susceptibility allele, KCNQ1 R231H, and describe its transcontinental geographic distribution and history. Induced pluripotent stem cell-derived cardiomyocytes procured from risk allele carriers exhibit abbreviated action potential duration, consistent with a gain-of-function effect. Using identity-by-descent (IBD) networks, we estimate the broad- and fine-scale population ancestry of risk allele carriers and their relatives. Analysis of ancestral migration routes reveals ancestors who inhabited Denmark in the 1700s, migrated to the Northeastern United States in the early 1800s, and traveled across the Midwest to arrive in Utah in the late 1800s. IBD/coalescent-based allele dating analysis reveals a relatively recent origin of the AF risk allele (~5000 years). Thus, our approach broadens the scope of study for disease susceptibility alleles to the context of human migration and ancestral origins., Many rare high-impact variants have been associated with disease, but the origins and functional impact are not always explored. Here, the authors trace the ancestry of a rare high impact atrial fibrillation allele in KCNQ1, and use iPSC-derived cardiomyocytes to characterize the effect of the allele.

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.

In studies of families with rare disease, it is common to screen for de novo mutations, as well as recessive or dominant variants that explain the phenotype. However, the filtering strategies and software used to prioritize high-confidence variants vary from study to study. In an effort to establish recommendations for rare disease research, we explore effective guidelines for variant (SNP and INDEL) filtering and report the expected number of candidates for de novo dominant, recessive, and autosomal dominant modes of inheritance. We derived these guidelines using two large family-based cohorts that underwent whole-genome sequencing, as well as two family cohorts with whole-exome sequencing. The filters are applied to common attributes, including genotype-quality, sequencing depth, allele balance, and population allele frequency. The resulting guidelines yield ~10 candidate SNP and INDEL variants per exome, and 18 per genome for recessive and de novo dominant modes of inheritance, with substantially more candidates for autosomal dominant inheritance. For family-based, whole-genome sequencing studies, this number includes an average of three de novo, ten compound heterozygous, one autosomal recessive, four X-linked variants, and roughly 100 candidate variants following autosomal dominant inheritance. The slivar software we developed to establish and rapidly apply these filters to VCF files is available at https://github.com/brentp/slivar under an MIT license, and includes documentation and recommendations for best practices for rare disease analysis.

We present the case of a singleton pregnancy presenting in utero with periods of 2:1 heart block with persistent fetal bradycardia and suspected fetal ventricular tachycardia. Postnatal clinical genetic testing identified a pathogenic variant in KCNH2. When familial site-specific genetic testing for the pathogenic variant was performed, the patient’s mother was identified as having possible mosaicism for the familial KCNH2 pathogenic variant. A subsequent pregnancy was also confirmed to have this pathogenic variant with similar prenatal and postnatal course as the proband. Other cases of mosaicism have been previously presented in genes related to inherited arrhythmias, including in SCN5A, but, to date, mosaicism has not been reported in KCNH2.

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.

The well-established manifestation of mitochondrial mutations in functional cardiac disease (e.g., mitochondrial cardiomyopathy) prompted the hypothesis that mitochondrial DNA (mtDNA) sequence and/or copy number (mtDNAcn) variation contribute to cardiac defects in congenital heart disease (CHD). MtDNAcns were calculated and rare, non-synonymous mtDNA mutations were identified in 1,837 CHD-affected proband-parent trios, 116 CHD-affected singletons, and 114 paired cardiovascular tissue/blood samples. The variant allele fraction (VAF) of heteroplasmic variants in mitochondrial RNA from 257 CHD cardiovascular tissue samples was also calculated. On average, mtDNA from blood had 0.14 rare variants and 52.9 mtDNA copies per nuclear genome per proband. No variation with parental age at proband birth or CHD-affected proband age was seen. mtDNAcns in valve/vessel tissue (320 ± 70) were lower than in atrial tissue (1,080 ± 320, p= 6.8E-21), which were lower than in ventricle tissue (1,340 ± 280, p= 1.4E-4). The frequency of rare variants in CHD-affected individual DNA was indistinguishable from the frequency in an unaffected cohort, and proband mtDNAcns did not vary from those of CHD cohort parents. In both the CHD and the comparison cohorts, mtDNAcns were significantly correlated between mother-child, father-child, and mother-father. mtDNAcns among people with European (mean= 52.0), African (53.0), and Asian haplogroups (53.5) were calculated and were significantly different for European and Asian haplogroups (p= 2.6E-3). Variant heteroplasmic fraction (HF) in blood correlated well with paired cardiovascular tissue HF (r= 0.975) and RNA VAF (r= 0.953), which suggests blood HF is a reasonable proxy for HF in heart tissue. We conclude that mtDNA mutations and mtDNAcns are unlikely to contribute significantly to CHD risk.

A genetic etiology is identified for one-third of patients with congenital heart disease (CHD), with 8% of cases attributable to coding de novo variants (DNVs). To assess the contribution of noncoding DNVs to CHD, we compared genome sequences from 749 CHD probands and their parents with those from 1,611 unaffected trios. Neural network prediction of noncoding DNV transcriptional impact identified a burden of DNVs in individuals with CHD (n = 2,238 DNVs) compared to controls (n = 4,177; P = 8.7 × 10-4). Independent analyses of enhancers showed an excess of DNVs in associated genes (27 genes versus 3.7 expected, P = 1 × 10-5). We observed significant overlap between these transcription-based approaches (odds ratio (OR) = 2.5, 95% confidence interval (CI) 1.1-5.0, P = 5.4 × 10-3). CHD DNVs altered transcription levels in 5 of 31 enhancers assayed. Finally, we observed a DNV burden in RNA-binding-protein regulatory sites (OR = 1.13, 95% CI 1.1-1.2, P = 8.8 × 10-5). Our findings demonstrate an enrichment of potentially disruptive regulatory noncoding DNVs in a fraction of CHD at least as high as that observed for damaging coding DNVs.

Visual Abstract, Highlights • Combining CHD phenotype–driven gene set enrichment and CRISPR knockdown screening in zebrafish is an effective approach to identifying novel CHD genes. • Mutations affecting genes coding for the WAVE2 protein complex and small GTPase-mediated signaling are associated with LVOTO lesions. • WAVE2 complex genes brk1, nckap1, and wasf2 and regulators of small GTPase signaling cul3a and racgap1 are critical to zebrafish heart development., Summary Genetic variants are the primary driver of congenital heart disease (CHD) pathogenesis. However, our ability to identify causative variants is limited. To identify causal CHD genes that are associated with specific molecular functions, the study used prior knowledge to filter de novo variants from 2,881 probands with sporadic severe CHD. This approach enabled the authors to identify an association between left ventricular outflow tract obstruction lesions and genes associated with the WAVE2 complex and regulation of small GTPase-mediated signal transduction. Using CRISPR zebrafish knockdowns, the study confirmed that WAVE2 complex proteins brk1, nckap1, and wasf2 and the regulators of small GTPase signaling cul3a and racgap1 are critical to cardiac development.

Background:Congenital heart disease (CHD) is the most common anomaly at birth, with a prevalence of ≈1%. While infants born to mothers with diabetes or obesity have a 2- to 3-fold increased incidence of CHD, the cause of the increase is unknown. Damaging de novo variants (DNV) in coding regions are more common among patients with CHD, but genome-wide rates of coding and noncoding DNVs associated with these prenatal exposures have not been studied in patients with CHD.Methods:DNV frequencies were determined for 1812 patients with CHD who had whole-genome sequencing and prenatal history data available from the Pediatric Cardiac Genomics Consortium’s CHD GENES study (Genetic Network). The frequency of DNVs was compared between subgroups usingttest or linear model.Results:Among 1812 patients with CHD, the number of DNVs per patient was higher with maternal diabetes (76.5 versus 72.1,ttestP=3.03×10−11), but the difference was no longer significant after including parental ages in a linear model (paternal and maternal correctionP=0.42). No interaction was observed between diabetes risk and parental age (paternal and maternal interactionP=0.80 and 0.68, respectively). No difference was seen in DNV count per patient based on maternal obesity (72.0 versus 72.2 for maternal body mass index 30,ttestP=0.86).Conclusions:After accounting for parental age, the offspring of diabetic or obese mothers have no increase in DNVs compared with other children with CHD. These results emphasize the role for other mechanisms in the cause of CHD associated with these prenatal exposures.Registration:URL:https://clinicaltrials.gov; NCT01196182.

Background: Familial hypercholesterolemia (FH) in children is diagnosed by identification of a known FH pathogenic variant or clinical criteria. We sought to determine the number of pediatric probands undergoing genetic testing for FH that met clinical criteria, and to evaluate differences between genotype positive and negative probands. Methods: All children ≤18 years old undergoing genetic testing for suspected FH were included. Clinical FH was defined as LDL ≥160 mg/dL and family history of premature CAD in 1 st /2 nd degree relatives. Screening of 1 st /appropriate 2 nd degree family members was recommended at all visits (lipid panel or genetic test) and was complete if obtained within 1 year of proband testing. Probands were stratified based on the presence or absence of known FH pathogenic variants. Wilcoxon rank sum or chi-squared tests compared lipid panel, LP(a), BMI percentile, number of screening tests performed, and family members diagnosed with FH. Results: Of 76 probands (age 12.5±2.9 years, 57% males) with suspected FH and genetic testing; 41 (54%) were genotype positive. Genotype positive probands were more likely to meet clinical criteria (44% vs. 17%, p=0.01), but only 19/41 (46%) had a positive family history. Genotype positive probands had higher total cholesterol (mg/dL) [median (IQR): 260 (242-297) vs. 226 (215-245), p Conclusion: Clinical criteria alone underdiagnosed FH in children and a negative family history occurred in over half of pediatric FH patients. A positive genetic diagnosis increased the number of screening tests performed and improved identification of at-risk family members. Therefore, genetic testing should be performed in all suspected pediatric FH patients.

BACKGROUND AND OBJECTIVES: Clinical evaluation of first-degree relatives of sudden death in the young (SDY) victims is recommended but reports of familial evaluation after SDY in population-based studies are limited. The SDY Case Registry is a prospective registry of sudden deaths in infants and children from multiple United States jurisdictions. Our objective was to describe familial evaluation after sudden death in this cohort. METHODS: Family members of the SDY Case Registry were invited to participate in the family substudy. Consented participants entered information about the decedent and personal medical history into an electronic database. First-degree relatives of noninfant SDY cases were informed of published recommendations for clinical evaluation and were provided with contact information for local inherited heart disease clinics. Saliva kits were mailed to participants for DNA sample collection. RESULTS: In total, 82 relatives of 44 SDY Registry cases (26 infants, 17 children, 1 unknown age) enrolled in the study. A copy of the medical examiner’s report was obtained from 20 (45%) cases. At the time of contact, only 26% were planning to see a physician because of the sudden death of the relative. A total of 62 (76%) participants provided DNA samples, but clinical records were uploaded by only 6 (14%) families. No new diagnoses of inherited diseases were identified with family screening. CONCLUSIONS: In a population-based registry of SDY, we found rare uptake of family evaluation, despite published recommendations. Educating families, primary care providers, and medical examiners is needed, including training of personnel who can help families navigate this process.

Genetic diseases that affect the cardiovascular system are relatively common and include cardiac channelopathies, cardiomyopathies, aortopathies, hypercholesterolemias, and structural diseases of the heart and great vessels. The rapidly expanding availability of clinical genetic testing leverages decades of research into the genetic origins of these diseases, helping inform diagnosis, clinical management, and prognosis. Although a number of guidelines and statements detail best practices for cardiovascular genetic testing, there is a paucity of pediatric-focused statements addressing the unique challenges in testing in this vulnerable population. In this scientific statement, we seek to coalesce the existing literature around the use of genetic testing for cardiovascular disease in infants, children, and adolescents.

Heart failure (HF) is a leading cause of morbidity and mortality in the United States and worldwide. As a multifactorial syndrome with unpredictable clinical outcomes, identifying the common molecular underpinnings that drive HF pathogenesis remains a major focus of investigation. Disruption of cardiac gene expression has been shown to mediate a common final cascade of pathological hallmarks wherein the heart reactivates numerous developmental pathways. Although the central regulatory mechanisms that drive this cardiac transcriptional reprogramming remain unknown, epigenetic contributions are likely. In the current study, we examined whether the epigenome, specifically DNA methylation, is reprogrammed in HF to potentiate a pathological shift in cardiac gene expression. To accomplish this, we used paired-end whole genome bisulfite sequencing and next-generation RNA sequencing of left ventricle tissue obtained from seven patients with end-stage HF and three nonfailing donor hearts. We found that differential methylation was localized to promoter-associated cytosine-phosphate-guanine islands, which are established regulatory regions of downstream genes. Hypermethylated promoters were associated with genes involved in oxidative metabolism, whereas promoter hypomethylation enriched glycolytic pathways. Overexpression of plasmid-derived DNA methyltransferase 3A in vitro was sufficient to lower the expression of numerous oxidative metabolic genes in H9c2 rat cardiomyoblasts, further supporting the importance of epigenetic factors in the regulation of cardiac metabolism. Last, we identified binding-site competition via hypermethylation of the nuclear respiratory factor 1 (NRF1) motif, an established upstream regulator of mitochondrial biogenesis. These preliminary observations are the first to uncover an etiology-independent shift in cardiac DNA methylation that corresponds with altered metabolic gene expression in HF. NEW & NOTEWORTHY The failing heart undergoes profound metabolic changes because of alterations in cardiac gene expression, reactivating glycolytic genes and suppressing oxidative metabolic genes. In the current study, we discover that alterations to cardiac DNA methylation encode this fetal-like metabolic gene reprogramming. We also identify novel epigenetic interference of nuclear respiratory factor 1 via hypermethylation of its downstream promoter targets, further supporting a novel contribution of DNA methylation in the metabolic remodeling of heart failure.

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.

Introduction: With improved survival of children with congenital heart disease (CHD), more parents are managing medical care for their child into adulthood, which can negatively affect parental health. The prevalence of mental and physical health conditions in affected mothers are not well quantified. Hypothesis: Mothers of a child with CHD (excluding mild CHD) have increased odds of being diagnosed with mental and physical health conditions compared to mothers of a child without CHD. Methods: A retrospective study with linked maternal-child EHR data was conducted. Kernel density plots, two sample Kolmogorov Smirnov test, and Bayesian networks were used for analyses. Results: A total of 3,480 mothers of a child with CHD and 252,206 mothers without a child with CHD were identified. Densities significantly differed between groups for most diagnoses and often occurred earlier for mothers with a child with CHD. Mothers of child with CHD (≤40 years) had increased odds of having a mental or physical health diagnosis (Figure & Table). Conclusion: Understanding how to support mothers of a child with CHD and improve engagement in preventive health behaviors is critical.

Calcium (Ca2+) entering mitochondria potently stimulates ATP synthesis. Increases in Ca2+ preserve energy synthesis in cardiomyopathies caused by mitochondrial dysfunction, and occur due to enhanced activity of the mitochondrial Ca2+ uniporter channel. The signaling mechanism that mediates this compensatory increase remains unknown. Here, we find that increases in the uniporter are due to impairment in Complex I of the electron transport chain (ETC). In normal physiology, Complex I promotes uniporter degradation via an interaction with the uniporter pore-forming subunit, a process we term Complex I-induced protein turnover (CLIPT). When Complex I dysfunction ensues, contact with the uniporter is inhibited, preventing degradation, and leading to a build-up in functional channels. Preventing uniporter activity leads to early demise in Complex I-deficient animals. Conversely, enhancing uniporter stability rescues survival and function in Complex I deficiency. Taken together, our data identify a fundamental pathway producing compensatory increases in Ca2+ influx during Complex I impairment.

Calcium entering mitochondria potently stimulates ATP synthesis. Increases in calcium preserve energy synthesis in cardiomyopathies caused by mitochondrial dysfunction, and occur due to enhanced activity of the mitochondrial calcium uniporter channel. The signaling mechanism that mediates this compensatory increase remains unknown. Here, we find that increases in the uniporter are due to impairment in Complex I of the electron transport chain. In normal physiology, Complex I promotes uniporter degradation via an interaction with the uniporter pore-forming subunit, a process we term Complex I-induced protein turnover. When Complex I dysfunction ensues, contact with the uniporter is inhibited, preventing degradation, and leading to a build-up in functional channels. Preventing uniporter activity leads to early demise in Complex I-deficient animals. Conversely, enhancing uniporter stability rescues survival and function in Complex I deficiency. Taken together, our data identify a fundamental pathway producing compensatory increases in calcium influx during Complex I impairment.

There is strong evidence for a genetic contribution to non-syndromic congenital heart defects (CHDs). However, exome- and genome-wide studies conducted at the variant and gene-level have identified few genome-wide significant CHD-related genes. Gene-set analyses are a useful complement to such studies and candidate gene-set analyses of rare variants have provided insight into the genetics of CHDs. However, similar analyses have not been conducted using data on common genetic variants. Consequently, we conducted common variant analyses of 15 CHD candidate gene-sets, using data from two common types of CHDs: conotruncal heart defects (1431 cases) and left ventricular outflow tract defects (509 cases). After Bonferroni correction for evaluation of multiple gene-sets, the cytoskeletal gene-set was significantly associated with conotruncal heart defects (βS = 0.09, 95% confidence interval (CI) 0.03–0.15). This association was stronger when analyses were restricted to the sub-set of cytoskeletal genes that have been observed to harbor rare damaging genotypes in at least two CHD cases (βS = 0.32, 95% CI 0.08–0.56). These findings add to the evidence linking cytoskeletal genes to CHDs and suggest that, for cytoskeletal genes, common variation may contribute to the risk of CHDs.

Rapid whole genome sequencing (rapid WGS) is a powerful diagnostic tool that is becoming increasingly practical for widespread clinical use. However, protocols for its use are challenging to implement. A significant obstacle to clinical adoption is that laboratory certification requires an initial research development phase, which is constrained by regulations from returning results. Regulations preventing return of results have ethical implications in cases which might impact patient outcomes. Here, we describe our experience with the development of a rapid WGS research protocol, that balanced the requirements for laboratory-validated test development with the ethical needs of clinically relevant return of results.

Background: The electronic medical record contains a wealth of information buried in free text. We created a natural language processing algorithm to identify patients with atrial fibrillation (AF) using text alone. Methods and Results: We created 3 data sets from patients with at least one AF billing code from 2010 to 2017: a training set (n=886), an internal validation set from site no. 1 (n=285), and an external validation set from site no. 2 (n=276). A team of clinicians reviewed and adjudicated patients as AF present or absent, which served as the reference standard. We trained 54 algorithms to classify each patient, varying the model, number of features, number of stop words, and the method used to create the feature set. The algorithm with the highest F-score (the harmonic mean of sensitivity and positive predictive value) in the training set was applied to the validation sets. F-scores and area under the receiver operating characteristic curves were compared between site no. 1 and site no. 2 using bootstrapping. Adjudicated AF prevalence was 75.1% at site no. 1 and 86.2% at site no. 2. Among 54 algorithms, the best performing model was logistic regression, using 1000 features, 100 stop words, and term frequency-inverse document frequency method to create the feature set, with sensitivity 92.8%, specificity 93.9%, and an area under the receiver operating characteristic curve of 0.93 in the training set. The performance at site no. 1 was sensitivity 92.5%, specificity 88.7%, with an area under the receiver operating characteristic curve of 0.91. The performance at site no. 2 was sensitivity 89.5%, specificity 71.1%, with an area under the receiver operating characteristic curve of 0.80. The F-score was lower at site no. 2 compared with site no. 1 (92.5% [SD, 1.1%] versus 94.2% [SD, 1.1%]; P Conclusions: We developed a natural language processing algorithm to identify patients with AF using text alone, with >90% F-score at 2 separate sites. This approach allows better use of the clinical narrative and creates an opportunity for precise, high-throughput cohort identification.