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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.

Background: A large proportion of genetic risk remains unexplained for structural heart disease involving the interventricular septum (IVS) including hypertrophic cardiomyopathy and ventricular septal defects. This study sought to develop a reproducible proxy of IVS structure from standard medical imaging, discover novel genetic determinants of IVS structure, and relate these loci to diseases of the IVS, hypertrophic cardiomyopathy, and ventricular septal defect. Methods: We estimated the cross-sectional area of the IVS from the 4-chamber view of cardiac magnetic resonance imaging in 32 219 individuals from the UK Biobank which was used as the basis of genome wide association studies and Mendelian randomization. Results: Measures of IVS cross-sectional area at diastole were a strong proxy for the 3-dimensional volume of the IVS (Pearson r =0.814, P =0.004), and correlated with anthropometric measures, blood pressure, and diagnostic codes related to cardiovascular physiology. Seven loci with clear genomic consequence and relevance to cardiovascular biology were uncovered by genome wide association studies, most notably a single nucleotide polymorphism in an intron of CDKN1A (rs2376620; β, 7.7 mm 2 [95% CI, 5.8–11.0]; P =6.0×10 − 10 ), and a common inversion incorporating KANSL1 predicted to disrupt local chromatin structure (β, 8.4 mm 2 [95% CI, 6.3–10.9]; P =4.2×10 − 14 ). Mendelian randomization suggested that inheritance of larger IVS cross-sectional area at diastole was strongly associated with hypertrophic cardiomyopathy risk (p IVW =4.6×10 − 10 ) while inheritance of smaller IVS cross-sectional area at diastole was associated with risk for ventricular septal defect (p IVW =0.007). Conclusions: Automated estimates of cross-sectional area of the IVS supports discovery of novel loci related to cardiac development and Mendelian disease. Inheritance of genetic liability for either small or large IVS, appears to confer risk for ventricular septal defect or hypertrophic cardiomyopathy, respectively. These data suggest that a proportion of risk for structural and congenital heart disease can be localized to the common genetic determinants of size and shape of cardiovascular anatomy.

Background: Higher maternal body mass index (BMI) and abnormal glucose metabolism during early pregnancy are associated with congenital heart defects in the offspring, but the exact mechanisms are unknown.Methods: We evaluated the association between maternal first trimester metabolic profile and transposition of the great arteries (TGA) in the offspring in a matched case-control study with 100 TGA mothers and 200 controls born in Finland during 2004-2014. Cases and controls were matched by birth year, child sex, and maternal age and BMI. Serum samples collected between 10- and 14-weeks of gestation were analyzed for 73 metabolic measures. Conditional logistic regression was used to assess the risk for TGA in the offspring, and a subgroup analysis among mothers with high BMI was conducted.Results: Higher concentrations of four subtypes of extremely large very-low-density lipoprotein (VLDL) particles and one of large VLDL particles were observed in TGA mothers. This finding did not reach statistical significance after multiple testing correction. The pooled odds ratio (OR) of the all metabolic variables was slightly higher in TGA mothers in the subgroup with maternal BMI over 25 (OR 1.25) and significantly higher in the subgroup with maternal BMI over 30 (OR 1.95) compared to the original population (OR 1.18).Conclusions: Our findings indicate that an abnormal maternal early pregnancy metabolic profile might be associated with TGA in the offspring, especially in obese mothers. A trend indicating altered VLDL subtype composition in TGA pregnancies warrants further research.

Background: Observational studies identified elevated blood pressure (BP) as a strong risk factor for thoracic aortic dilation, and BP reduction is the primary medical intervention recommended to prevent progression of aortic aneurysms. However, although BP may impact aortic dilation, aortic size may also impact BP. The causal relationship between BP and thoracic aortic size has not been reliably established. Methods: Genome-wide association studies summary statistics were obtained for BP and ascending thoracic aortic diameter (AscAoD). Causal effects of BP on AscAoD were estimated using 2-sample Mendelian randomization using a range of pleiotropy-robust methods. Results: Genetically predicted increased systolic BP, diastolic BP, and mean arterial pressure all significantly associate with higher AscAoD (systolic BP: β estimate, 0.0041 mm/mm Hg [95% CI, 0.0008–0.0074]; P =0.02, diastolic BP: β estimate, 0.0272 mm/mm Hg [95% CI, 0.0224–0.0320]; P P P P P =0.02), while directly associated with diastolic BP (0.8203 mm Hg/mm [95% CI, 0.2735–1.3672]; P =0.004). Conclusions: BP likely contributes causally to ascending thoracic aortic dilation. Increased AscAoD likely contributes to lower systolic BP and pulse pressure, but not diastolic BP, consistent with the hemodynamic consequences of a reduced aortic diameter.

Rationale: Transposition of the great arteries (TGA) is one of the most severe types of congenital heart diseases. Understanding the clinical characteristics and pathogenesis of TGA is, therefore, urgently needed for patient management of this severe disease. However, the clinical characteristics and genetic cause underlying TGA remain largely unexplored. Objective: We sought to systematically examine the clinical characteristics and genetic cause for isolated nonsyndromic TGA. Methods and Results: We recruited 249 patients with TGA (66 family trios) and performed whole-exome sequencing. The incidence of patent ductus arteriosus in dextro-TGA (52.7%) and dextrocardia/mesocardia in congenitally corrected TGA (32.8%) were significantly higher than that in other subtypes. A high prevalence of bicuspid pulmonic valve (9.6%) was observed in patients with TGA. Similar results were observed in a replication group of TGA (n=132). Through a series of bioinformatics filtering steps, we obtained 82 candidate genes harboring potentially damaging de novo, loss of function, compound heterozygous, or X-linked recessive variants. Established congenital heart disease–causing genes, such as FOXH1 , were found among the list of candidate genes. A total of 19 ciliary genes harboring rare potentially damaging variants were also found; for example, DYNC2LI1 with a de novo putatively damaging variant. The enrichment of ciliary genes supports the roles of cilia in the pathogenesis of TGA. In total, 33% of the TGA probands had >1 candidate gene hit by putatively deleterious variants, suggesting that a portion of the TGA cases were probably affected by oligogenic or polygenic inheritance. Conclusions: The findings of clinical characteristic analyses have important implications for TGA patient stratification. The results of genetic analyses highlight the pathogenic role of ciliary genes and a complex genetic architecture underlying TGA.

BACKGROUND: Long QT syndrome (LQTS) is a hereditary disease that predisposes patients to life-threatening cardiac arrhythmias and sudden cardiac death. Our previous study of the human ether-à-go-go related gene (hERG)–encoded K(+) channel (K(v)11.1) supports an association between hERG and RING finger protein 207 (RNF207) variants in aggravating the onset and severity of LQTS, specifically T613M hERG (hERG(T613M)) and RNF207 frameshift (RNF207(G603fs)) mutations. However, the underlying mechanistic underpinning remains unknown. OBJECTIVE: The purpose of the present study was to test the role of RNF207 in the function of hERG-encoded K(+) channel subunits. METHODS: Whole-cell patch-clamp experiments were performed in human embryonic kidney (HEK 293) cells and human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) together with immunofluorescent confocal and high resolution microscopy, auto-ubiquitinylation assays, and co-immunoprecipitation experiments to test the functional interactions between hERG and RNF207. RESULTS: Here, we demonstrated that RNF207 serves as an E3 ubiquitin ligase and targets misfolded hERG(T613M) proteins for degradation. RNF207(G603fs) exhibits decreased activity and hinders the normal degradation pathway; this increases the levels of hERG(T613M) subunits and their dominant-negative effect on the wild-type subunits, ultimately resulting in decreased current density. Similar findings are shown for hERG(A614V), a known dominant-negative mutant subunit. Finally, the presence of RNF207(G603fs) with hERG(T613M) results in significantly prolonged action potential durations and reduced hERG current in human-induced pluripotent stem cell–derived cardiomyocytes. CONCLUSION: Our study establishes RNF207 as an interacting protein serving as a ubiquitin ligase for hERG-encoded K(+) channel subunits. Normal function of RNF207 is critical for the quality control of hERG subunits and consequently cardiac repolarization. Moreover, our study provides evidence for protein quality control as a new paradigm in life-threatening cardiac arrhythmias in patients with LQTS.

ObjectiveTo evaluate the association between maternal first trimester metabolic profile and transposition of the great arteries (TGA) in the offspring.DesignA matched case-control study using national register data and maternal first trimester blood samples.SettingsFinlandPatientsA total of 100 mothers with TGA in their offspring and 200 controls matched by the year of birth and sex of the child, and the age and the body mass index (BMI) of the mother.InterventionsNone.Main outcome measuresTo identify the impact of 73 metabolic measure concentrations on the likelihood that a child would be born with TGA.ResultsA higher concentration of four subtypes in extremely large very-low-density lipoprotein (VLDL) particles and one in large VLDL particles were observed in mothers with TGA in their offspring. This finding did not reach statistical significance after multiple testing correction. In the analysis of the subgroups of maternal BMI over 25 and 30 only, the odds ratios (OR) of the metabolic variables were higher compared to the original population. In the group with a BMI over 25 the mean OR was 1.3 (N= 111, total) and in the group with a BMI over 30 the mean OR was 2.1 (N= 42, total).ConclusionsWe found no statistically significant difference between the metabolic profiles of mothers with TGA in their offspring, and their controls. However, the trend in our BMI-matched study potentially indicates that the likelihood of TGA development associates with maternal early-pregnancy metabolic profile, and the association is more pronounced in the subgroups of BMI over 25 and 30. Further studies are needed especially in the higher BMI groups.What is already known on this topic?The most important structures of the heart develop during early pregnancy, but studies on the association of maternal metabolomics during the first trimester with CHD development are scarce.Maternal obesity and diabetes are known risk factors for CHD in the offspring but the precise developmental mechanisms underlying this risk remain unknown.What this study adds to this topic?The maternal metabolomic profile in early pregnancy is potentially associated with the likelihood of TGA development in the offspring.Metabolic markers in obese women may have a stronger association with the development of TGA but this connection requires further studies.

Background: A large proportion of genetic risk remains unexplained for structural heart disease involving the interventricular septum (IVS) including hypertrophic cardiomyopathy (HCM) and ventricular septal defects (VSD). Methods: We performed machine learning to estimate the cross-sectional area of the interventricular septum (IVS.csad) from the 4-chamber view of cardiac MRI in 32,219 individuals from the UK Biobank. We related IVS.csad to relevant clinical phenotypes, and performed genome-wide association studies and Mendelian Randomization. Results: Automated measures of IVS.csad were highly accurate, and strongly correlated with anthropometric measures, blood pressure, and diagnostic codes related to cardiovascular physiology. A single nucleotide polymorphism in an intron of CDKN1A was associated with IVS.csad (rs2376620, Beta 8.4 mm2, 95% confidence intervals (CI) 5.8 to 11.0, p=2.0e-10), and a common inversion incorporating KANSL1 predicted to disrupt local chromatin structure was associated with an increase in IVS.csad (Beta 8.6 mm2, 95% CI 6.3-10.9, p=1.3e-13). Mendelian Randomization suggested that inheritance of larger IVS.csad was causal for HCM (Beta 2.45 log odds ratio (OR) HCM per increase in SD of IVS.csad, standard error (SE) 0.48, pIVW = 2.8e-7) while inheritance of smaller IVS.csad was causal for VSD (Beta -2.06 log odds ratio (OR) VSD per decrease in SD of IVS.csad, SE 0.75, pIVW = 0.006) Conclusion: Automated derivation of the cross sectional area of the IVS allowed discovery of loci related to cardiac development and Mendelian disease. Inheritance of genetic liability for either large or small interventricular septum, appears to confer risk for HCM or VSD respectively, suggesting that a proportion of risk for structural and congenital heart disease may localize to the common genetic determinants of cardiovascular anatomy.

The migratory cardiac neural crest cells (CNCCs) contribute greatly to cardiovascular development. A thorough understanding of the cell lineages, developmental chronology, and transcriptomic states of CNCC derivatives during normal development is essential for deciphering the pathogenesis of CNCC‐associated congenital anomalies. Here, we perform single‐cell transcriptomic sequencing of 34,131 CNCC‐derived cells in mouse hearts covering eight developmental stages between E10.5 and P7. We report the presence of CNCC‐derived mural cells that comprise pericytes and microvascular smooth muscle cells (mVSMCs). Furthermore, we identify the transition from the CNCC‐derived pericytes to mVSMCs and the key regulators over the transition. In addition, our data support that many CNCC derivatives had already committed or differentiated to a specific lineage when migrating into the heart. We explore the spatial distribution of some critical CNCC‐derived subpopulations with single‐molecule fluorescence in situ hybridization. Finally, we computationally reconstruct the differentiation path and regulatory dynamics of CNCC derivatives. Our study provides novel insights into the cell lineages, developmental chronology, and regulatory dynamics of CNCC derivatives during development., A single‐cell transcriptomic atlas of mouse cardiac neural crest cell (CNCC) derivatives reveals that many had already committed or differentiated to a specific lineage when migrating into the heart.

Hypertrophic cardiomyopathy (HCM) has historically been diagnosed phenotypically. Through genetic testing, identification of a molecular diagnosis (MolDx) is increasingly common but the impact on pediatric patients is unknown. This was a retrospective study of next-generation sequencing data for 602 pediatric patients with a clinician-reported history of HCM. Diagnostic yield was stratified by gene and self-reported race/ethnicity. A MolDx of HCM was identified in 242 (40%) individuals. Sarcomeric genes were the highest yielding, but pathogenic and/or likely pathogenic (P/LP) variants in syndromic genes were found in 36% of individuals with a MolDx, often in patients without documented clinical suspicion for a genetic syndrome. Among all MolDx, 73% were in genes with established clinical management recommendations and 2.9% were in genes that conferred eligibility for clinical trial enrollment. Black patients were the least likely to receive a MolDx. In the current era, genetic testing can impact management of HCM, beyond diagnostics or prognostics, through disease-specific guidelines or clinical trial eligibility. Genetic testing frequently can help identify syndromes in patients for whom syndromes may not be suspected. These findings highlight the importance of pursuing broad genetic testing, independent of suspicion based on phenotype. Lower rates of MolDx in Black patients may contribute to health inequities. Further research is needed evaluating the genetics of HCM in underrepresented/underserved populations. Additionally, research related to the impact of genetic testing on clinical management of other diseases is warranted. Supplementary Information The online version contains supplementary material available at 10.1007/s00246-021-02764-1.

Copy-number variations (CNVs) represent a significant proportion of the genetic differences between individuals and many CNVs associate causally with syndromic disease and clinical outcomes. Here, we characterize the landscape of copy-number variation and their phenome-wide effects in a sample of 472,228 array-genotyped individuals from the UK Biobank. In addition to population-level selection effects against genic loci conferring high mortality, we describe genetic burden from potentially pathogenic and previously uncharacterized CNV loci across more than 3,000 quantitative and dichotomous traits, with separate analyses for common and rare classes of variation. Specifically, we highlight the effects of CNVs at two well-known syndromic loci 16p11.2 and 22q11.2, previously uncharacterized variation at 9p23, and several genic associations in the context of acute coronary artery disease and high body mass index. Our data constitute a deeply contextualized portrait of population-wide burden of copy-number variation, as well as a series of dosage-mediated genic associations across the medical phenome.

Biomedical repositories such as the UK Biobank provide increasing access to prospectively collected cardiac imaging, however these data are unlabeled, which creates barriers to their use in supervised machine learning. We develop a weakly supervised deep learning model for classification of aortic valve malformations using up to 4,000 unlabeled cardiac MRI sequences. Instead of requiring highly curated training data, weak supervision relies on noisy heuristics defined by domain experts to programmatically generate large-scale, imperfect training labels. For aortic valve classification, models trained with imperfect labels substantially outperform a supervised model trained on hand-labeled MRIs. In an orthogonal validation experiment using health outcomes data, our model identifies individuals with a 1.8-fold increase in risk of a major adverse cardiac event. This work formalizes a deep learning baseline for aortic valve classification and outlines a general strategy for using weak supervision to train machine learning models using unlabeled medical images at scale., The availability of labelled training data is one of the practical obstacles towards wide application of machine learning models in medicine. Here the authors develop a weakly supervised deep learning model for the classification of aortic malformations using unlabelled cardiac MRI sequences from the UK biobank.

Summary: Cardiac outflow tract (OFT) is a major hotspot for congenital heart diseases. A thorough understanding of the cellular diversity, transitions, and regulatory networks of normal OFT development is essential to decipher the etiology of OFT malformations. We performed single-cell transcriptomic sequencing of 55,611 mouse OFT cells from three developmental stages that generally correspond to the early, middle, and late stages of OFT remodeling and septation. Known cellular transitions, such as endothelial-to-mesenchymal transition, have been recapitulated. In particular, we identified convergent development of the vascular smooth muscle cell (VSMC) lineage where intermediate cell subpopulations were found to be involved in either myocardial-to-VSMC trans-differentiation or mesenchymal-to-VSMC transition. Finally, we uncovered transcriptional regulators potentially governing cellular transitions. Our study provides a single-cell reference map of cell states for normal OFT development and paves the way for further studies of the etiology of OFT malformations at the single-cell level. : Liu et al. present single-cell transcriptomes of over 50,000 cells from the developing cardiac outflow tract in mice. They identify convergent development of the vascular smooth muscle cell (VSMC) lineage, with these cells arising either by a myocardial-to-VSMC trans-differentiation or mesenchymal-to-VSMC transition. Keywords: cardiac outflow tract, single-cell RNA-seq, trans-differentiation, vascular smooth muscle, convergent development

Background: Although lower-complexity cardiac malformations constitute the majority of adult congenital heart disease (ACHD), the long-term risks of adverse cardiovascular events and relationship with conventional risk factors in this population are poorly understood. We aimed to quantify the risk of adverse cardiovascular events associated with lower-complexity ACHD that is unmeasured by conventional risk factors. Methods: A multitiered classification algorithm was used to select individuals with lower-complexity ACHD and individuals without ACHD for comparison among >500 000 British adults in the UK Biobank. ACHD diagnoses were subclassified as isolated aortic valve and noncomplex defects. Time-to-event analyses were conducted for the primary end points of fatal or nonfatal acute coronary syndrome, ischemic stroke, heart failure, and atrial fibrillation and a secondary combined end point for major adverse cardiovascular events. Maximum follow-up time for the study period was 22 years with retrospectively and prospectively collected data from the UK Biobank. Results: We identified 2006 individuals with lower-complexity ACHD and 497 983 unexposed individuals in the UK Biobank (median age at enrollment, 58 [interquartile range, 51–63] years). Of the ACHD-exposed group, 59% were male, 51% were current or former smokers, 30% were obese, and 69%, 41%, and 7% were diagnosed or treated for hypertension, hyperlipidemia, and diabetes mellitus, respectively. After adjustment for 12 measured cardiovascular risk factors, ACHD remained strongly associated with the primary end points, with hazard ratios ranging from 2.0 (95% CI, 1.5–2.8; P P Conclusions: Individuals with lower-complexity ACHD had a higher burden of adverse cardiovascular events relative to the general population that was unaccounted for by conventional cardiovascular risk factors. These findings highlight the need for closer surveillance of patients with mild to moderate ACHD and further investigation into management and mechanisms of cardiovascular risk unique to this growing population of high-risk adults.

Loss of function variants in NOTCH1 cause left ventricular outflow tract obstructive defects (LVOTO). However, the risk conferred by rare and non-coding variants in NOTCH1 for LVOTO remains largely uncharacterized. In a cohort of 49 families affected by hypoplastic left heart syndrome, a severe form of LVOTO, we discovered predicted loss of function NOTCH1 variants in 6% of individuals. Rare or low-frequency missense variants were found in 16% of families. To make a quantitative estimate of the genetic risk posed by variants in NOTCH1 for LVOTO, we studied associations of 400 coding and non-coding variants in NOTCH1 in 1,085 cases and 332,788 controls from the UK Biobank. Two rare intronic variants in strong linkage disequilibrium displayed significant association with risk for LVOTO amongst European-ancestry individuals. This result was replicated in an independent analysis of 210 cases and 68,762 controls of non-European and mixed ancestry. In conclusion, carrying rare predicted loss of function variants in NOTCH1 confer significant risk for LVOTO. In addition, the two intronic variants seem to be associated with an increased risk for these defects. Our approach demonstrates the utility of population-based datasets in quantifying the specific risk of individual variants for disease related phenotypes.

Genetic factors undoubtedly affect the development of congenital heart disease (CHD) but still remain ill defined. We sought to identify genetic risk factors associated with CHD and to accomplish a functional analysis of SNP-carrying genes. We performed a genome-wide association study (GWAS) of 4034 White patients with CHD and 8486 healthy controls. One SNP on chromosome 5q22.2 reached genome-wide significance across all CHD phenotypes and was also indicative for septal defects. One region on chromosome 20p12.1 pointing to the MACROD2 locus identified 4 highly significant SNPs in patients with transposition of the great arteries (TGA). Three highly significant risk variants on chromosome 17q21.32 within the GOSR2 locus were detected in patients with anomalies of thoracic arteries and veins (ATAV). Genetic variants associated with ATAV are suggested to influence the expression of WNT3, and the variant rs870142 related to septal defects is proposed to influence the expression of MSX1. We analyzed the expression of all 4 genes during cardiac differentiation of human and murine induced pluripotent stem cells in vitro and by single-cell RNA-Seq analyses of developing murine and human hearts. Our data show that MACROD2, GOSR2, WNT3, and MSX1 play an essential functional role in heart development at the embryonic and newborn stages.

The fibrous annulus of the mitral valve defines the functional orifice and anchors the anterior and posterior leaflets, playing an important role in normal cardiovascular physiology and valvular function. We derived automated estimates of mitral valve annular diameter in the 4-chamber view from 32,220 MRI images from the UK Biobank at ventricular systole and diastole as the basis for genome wide association studies. Mitral annular dimensions correspond to previously described anatomical norms and GWAS inclusive of four population strata identify ten loci, including novel loci (GOSR2, ERBB4, MCTP2, MCPH1) and genes related to cardiac contractility (BAG3, TTN, RBFOX1). ATAC-seq of primary mitral valve tissue localize multiple variants to regions of open chromatin in biologically relevant cell types and rs17608766 to an algorithmically predicted enhancer element in GOSR2. We observed strong genetic correlation with measures of contractility and mitral valve disease, and clinical correlations with heart failure, cerebrovascular disease, and ventricular arrythmias. A polygenic score of mitral valve annular diameter in systole was predictive of risk mitral valve prolapse across four cohorts (Odds ratio 1.19 per SD increase in polygenic score, 95% confidence interval 1.14 to 1.24, p=4.9E-11). In summary genetic and clinical studies of mitral valve annular diameter reveal new genetic determinants of mitral valve biology while highlighting known and previously unrecognized clinical associations. Polygenic determinants of mitral valve annular diameter may represent an independent risk-factor for mitral prolapse. Overall, computationally estimated phenotypes derived at scale from medical imaging represent an important substrate for genetic discovery and clinical risk prediction.

The fibrous annulus of the mitral valve plays an important role in valvular function and cardiac physiology, while normal variation in the size of cardiovascular anatomy may share a genetic link with common and rare disease. We derived automated estimates of mitral valve annular diameter in the 4-chamber view from 32,220 MRI images from the UK Biobank at ventricular systole and diastole as the basis for GWAS. Mitral annular dimensions corresponded to previously described anatomical norms, and GWAS inclusive of 4 population strata identified 10 loci, including possibly novel loci (GOSR2, ERBB4, MCTP2, MCPH1) and genes related to cardiac contractility (BAG3, TTN, RBFOX1). ATAC-Seq of primary mitral valve tissue localized multiple variants to regions of open chromatin in biologically relevant cell types and rs17608766 to an algorithmically predicted enhancer element in GOSR2. We observed strong genetic correlation with measures of contractility and mitral valve disease and clinical correlations with heart failure, cerebrovascular disease, and ventricular arrhythmias. Polygenic scoring of mitral valve annular diameter in systole was predictive of risk mitral valve prolapse across 4 cohorts. In summary, genetic and clinical studies of mitral valve annular diameter revealed genetic determinants of mitral valve biology, while highlighting clinical associations. Polygenic determinants of mitral valve annular diameter may represent an independent risk factor for mitral prolapse. Overall, computationally estimated phenotypes derived at scale from medical imaging represent an important substrate for genetic discovery and clinical risk prediction.

Population based studies demonstrate strong familial recurrence of cardiac malformations particularly for individuals affected with a specific class of CHD, left-ventricular outflow tract (LVOT) obstruction. Recently we linked 99 lead variants across 71 loci to diameter of the ascending aorta derived from MRI measurements across multiple ethnicities in the UK Biobank. Using these data we created a polygenic risk score capturing ascending aortic diameter (PRSAoD) Among 2,594 individuals with CHD; a decrease by one standard deviation in PRSAoD was associated with an increased risk of congenital LVOT (OR=1.14[1.03-1.26], p=0.014) but not with other subtypes of CHD. Using Mendelian Randomization we observed strong evidence of a causal effect where inheritance of a smaller diameter of the ascending aorta corresponded to an increase in risk for congenital LVOT (p_IVW = 0.008). Our data may suggest that genetic determinants of a smaller ascending aorta act during early development to disturb blood flow through left-sided structures to increasing the risk of LVOT CHD, which is consistent with experimental evidence and the “no flow, no grow” paradigm in the formation of the left ventricular outflow tract.

Detecting epistatic drivers of human phenotypes remains a challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving single pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests from interactions by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that evaluate the stability of improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline using the phenotype of red-hair from the UK Biobank, where several genes are known to demonstrate epistatic interactions. epiTree recovers both previously reported and novel interactions, which represent forms of non-linearities not captured by logistic regression models. Additionally, epiTree suggests interactions between genes such as PKHD1 and XPOTP1, which are unlinked to MC1R, as novel candidate interactions associated with the red hair phenotype. Last but not least, we find that individual Boolean or tree-based epistasis models generally provide higher prediction accuracy than classical logistic regression.

The clinical evaluation of a genetic syndrome relies upon recognition of a characteristic pattern of signs or symptoms to guide targeted genetic testing for confirmation of the diagnosis. However, individuals displaying a single phenotype of a complex syndrome may not meet criteria for clinical diagnosis or genetic testing. Here, we present a phenome-wide association study (PheWAS) approach to systematically explore the phenotypic expressivity of common and rare alleles in genes associated with four well-described syndromic diseases (Alagille (AS), Marfan (MS), DiGeorge (DS), and Noonan (NS) syndromes) in the general population. Using human phenotype ontology (HPO) terms, we systematically mapped 60 phenotypes related to AS, MS, DS and NS in 337,198 unrelated white British from the UK Biobank (UKBB) based on their hospital admission records, self-administrated questionnaires, and physiological measurements. We performed logistic regression adjusting for age, sex, and the first 5 genetic principal components, for each phenotype and each variant in the target genes (JAG1, NOTCH2 FBN1, PTPN1 and RAS-opathy genes, and genes in the 22q11.2 locus) and performed a gene burden test. Overall, we observed multiple phenotype-genotype correlations, such as the association between variation in JAG1, FBN1, PTPN11 and SOS2 with diastolic and systolic blood pressure; and pleiotropy among multiple variants in syndromic genes. For example, rs11066309 in PTPN11 was significantly associated with a lower body mass index, an increased risk of hypothyroidism and a smaller size for gestational age, all in concordance with NS-related phenotypes. Similarly, rs589668 in FBN1 was associated with an increase in body height and blood pressure, and a reduced body fat percentage as observed in Marfan syndrome. Our findings suggest that the spectrum of associations of common and rare variants in genes involved in syndromic diseases can be extended to individual phenotypes within the general population., Author summary Standard medical evaluation of genetic syndromes relies upon recognizing a characteristic pattern of signs or symptoms to guide targeted genetic testing for confirmation of the diagnosis. This may lead to missing diagnoses in patients with silent or a low expressed form of the syndrome. Here we take advantage of a rich electronic health record, various phenotypic measurements, and genetic information in 337,198 unrelated white British from the UKBB, to study the relation between single syndromic disease phenotypes and genes related to syndromic disease. We show multiple phenotype-genotype associations in concordance with phenotypes variations found in syndromic diseases. For example, we show that a commonly found variant in FBN1 was associated with high standing/sitting height ratio and reduced body fat percentage as observed in individuals with Marfan syndrome. Our findings suggest that common and rare alleles in syndromic disease genes are causative of individual component phenotypes present in a general population; further research is needed to characterize the pleiotropic effect of alleles in syndromic genes in persons without the syndromic disease.

Background: The aortic valve is an important determinant of cardiovascular physiology and anatomic location of common human diseases. Methods: From a sample of 34 287 white British ancestry participants, we estimated functional aortic valve area by planimetry from prospectively obtained cardiac magnetic resonance imaging sequences of the aortic valve. Aortic valve area measurements were submitted to genome-wide association testing, followed by polygenic risk scoring and phenome-wide screening, to identify genetic comorbidities. Results: A genome-wide association study of aortic valve area in these UK Biobank participants showed 3 significant associations, indexed by rs71190365 (chr13:50764607, DLEU1 , P =1.8×10 −9 ), rs35991305 (chr12:94191968, CRADD , P =3.4×10 −8 ), and chr17:45013271:C:T ( GOSR2 , P =5.6×10 −8 ). Replication on an independent set of 8145 unrelated European ancestry participants showed consistent effect sizes in all 3 loci, although rs35991305 did not meet nominal significance. We constructed a polygenic risk score for aortic valve area, which in a separate cohort of 311 728 individuals without imaging demonstrated that smaller aortic valve area is predictive of increased risk for aortic valve disease (odds ratio, 1.14; P =2.3×10 −6 ). After excluding subjects with a medical diagnosis of aortic valve stenosis (remaining n=308 683 individuals), phenome-wide association of >10 000 traits showed multiple links between the polygenic score for aortic valve disease and key health-related comorbidities involving the cardiovascular system and autoimmune disease. Genetic correlation analysis supports a shared genetic etiology with between aortic valve area and birth weight along with other cardiovascular conditions. Conclusions: These results illustrate the use of automated phenotyping of cardiac imaging data from the general population to investigate the genetic etiology of aortic valve disease, perform clinical prediction, and uncover new clinical and genetic correlates of cardiac anatomy.

Background Neurocognitive impairment is a common complication of congenital heart disease (CHD) as well as acquired cardiovascular disease. Data are limited on neurocognitive function in adults with CHD (ACHD). Methods and Results A total of 1020 individuals with mild‐to‐moderate ACHD and 497 987 individuals without ACHD from the volunteer‐based UK Biobank study underwent neurocognitive tests for fluid intelligence, reaction time, numeric memory, symbol‐digit substitution, and trail making at enrollment and follow‐up. Performance scores were compared before and after exclusion of preexisting stroke or coronary artery disease as measures of cerebro‐ and cardiovascular disease. Individuals with ACHD had significantly poorer performance on alpha‐numeric trail making, a measure of visual attention and cognitive flexibility, spending 6.4 seconds longer on alpha‐numeric trail making (95% CI, 3.0–9.9 seconds, P =0.002) and 2.5 seconds longer on numeric trail making (95% CI, 0.5–4.6 seconds, P =0.034), a measure of visual attention and processing speed. The ACHD cohort had modestly lower performance on symbol‐digit substitution, a measure of processing speed, with 0.9 fewer correct substitutions (95% CI, − 1.5 to − 0.2 substitutions, P =0.021). After excluding preexisting stroke or coronary artery disease, individuals with ACHD continued to show poorer performance in all 6 domains ( P =NS). Conclusions Individuals with mild‐to‐moderate ACHD had poorer neurocognitive performance, most significantly in tests of cognitive flexibility, analogous to deficits in children with CHD. These differences appear to be driven by increased burden of cerebro‐ and cardiovascular disease among individuals with ACHD.

Atherosclerosis is the process underlying heart attack and stroke. Despite decades of research, its pathogenesis remains unclear. Dogma suggests that atherosclerotic plaques expand primarily via the accumulation of cholesterol and inflammatory cells. However, recent evidence suggests that a substantial portion of the plaque may arise from a subset of “dedifferentiated” vascular smooth muscle cells (SMCs) which proliferate in a clonal fashion. Herein we use multicolor lineage-tracing models to confirm that the mature SMC can give rise to a hyperproliferative cell which appears to promote inflammation via elaboration of complement-dependent anaphylatoxins. Despite being extensively opsonized with prophagocytic complement fragments, we find that this cell also escapes immune surveillance by neighboring macrophages, thereby exacerbating its relative survival advantage. Mechanistic studies indicate this phenomenon results from a generalized opsonin-sensing defect acquired by macrophages during polarization. This defect coincides with the noncanonical up-regulation of so-called don’t eat me molecules on inflamed phagocytes, which reduces their capacity for programmed cell removal (PrCR). Knockdown or knockout of the key antiphagocytic molecule CD47 restores the ability of macrophages to sense and clear opsonized targets in vitro, allowing for potent and targeted suppression of clonal SMC expansion in the plaque in vivo. Because integrated clinical and genomic analyses indicate that similar pathways are active in humans with cardiovascular disease, these studies suggest that the clonally expanding SMC may represent a translational target for treating atherosclerosis.

Enlargement of the aorta is an important risk factor for aortic aneurysm and dissection, a leading cause of morbidity in the developed world. While Mendelian genetics account for a portion of thoracic aortic disease, the contribution of common variation is not known. Using standard techniques in computer vision, we performed automated extraction of Ascending Aortic Diameter (AsAoD) from cardiac MRI of 36,021 individuals from the UK Biobank. A multi-ethnic genome wide association study and trans-ethnic meta-analysis identified 99 lead variants across 71 loci including genes related to cardiovascular development (HAND2, TBX20) and Mendelian forms of thoracic aortic disease (ELN, FBN1). A polygenic risk score predicted prevalent risk of thoracic aortic aneurysm within the UK Biobank (OR 1.50 per standard deviation (SD) polygenic risk score (PRS), p=6.30x10-03) which was validated across three additional biobanks including FinnGen, the Penn Medicine Biobank, and the Million Veterans Program (MVP) in individuals of European descent (OR 1.37 [1.31 - 1.43] per SD PRS), individuals of Hispanic descent (OR 1.40 [1.16 - 1.69] per SD PRS, p=5.6x10-04), and individuals of African American descent (OR 1.08 [1.00 - 1.18] per SD PRS, p=0.05). Within individuals of European descent who carried a diagnosis of thoracic aneurysm, the PRS was specifically predictive of the need for surgical intervention (OR 1.57 [1.15 - 2.15] per SD PRS, p=4.45x10-03). Using Mendelian Randomization our data highlight the primary causal role of blood pressure in reducing dilation of the thoracic aorta. Overall our findings link normal anatomic variation to extremes observed in Mendelian syndromes and provide a roadmap for the use of genetic determinants of human anatomy in both understanding cardiovascular development while simultaneously improving prediction and prevention of human disease.

Congenital heart disease (CHD) is the most common anatomical malformation occurring live‐born infants and an increasing cause of morbidity and mortality across the lifespan and throughout the world. Population‐based observations have long described associations between maternal cardiometabolic disorders and the risk of CHD in the offspring. Here we review the epidemiological evidence and clinical observations relating maternal obesity and diabetes mellitus to the risk of CHD offspring with particular attention to mechanistic models of maternal‐fetal risk transmission and first trimester disturbances of fetal cardiac development. A deeper understanding of maternal risk factors holds the potential to improve both prenatal detection of CHD by identifying at‐risk pregnancies, along with primary prevention of disease by improving preconception and prenatal treatment of at‐risk mothers.

The aortic valve is an important determinant of cardiovascular physiology and anatomic location of common human diseases. From a sample of 26,142 European-ancestry participants, we estimated functional aortic valve area by planimetry from prospectively obtained cardiac MRI sequences of the aortic valve. A genome-wide association study of aortic valve area in these UK Biobank participants showed two significant associations indexed by rs71190365 (chr13:50764607,DLEU1, p=1.8×10−9) and rs35991305 (chr12:94191968,CRADD, p=3.4×10−8). From the GWAS findings we constructed a polygenic risk score for aortic valve area, which in a separate cohort of 311,728 individuals without imaging demonstrated that smaller aortic valve area is predictive of increased risk for aortic valve disease (Odds Ratio 0.88,p=2.3×10−6). After excluding subjects with a medical diagnosis of aortic valve stenosis (remaining n=310,546 individuals), phenome-wide association of >10,000 traits showed multiple links between the polygenic score for aortic valve disease and key health-related comorbidities involving the cardiovascular system and autoimmune disease. Genetic correlation analysis supports a shared genetic etiology with between aortic valve size and birthweight along with other cardiovascular conditions. These results illustrate the use of automated phenotyping of cardiac imaging data from the general population to investigate the genetic etiology of aortic valve disease, perform clinical prediction, and uncover new clinical and genetic correlates of cardiac anatomy.

In a retrospective study of 19 171 mother-child dyads, elevated random plasma glucose values during early pregnancy were directly correlated with increased risk for congenital heart disease in offspring. Plasma glucose levels proximal to the period of cardiac development may represent a modifiable risk factor for congenital heart disease in expectant mothers without diabetes.

The clinical evaluation of a genetic syndrome relies upon recognition of a characteristic pattern of signs or symptoms to guide targeted genetic testing for confirmation of the diagnosis. However, individuals displaying a few phenotypes of a complex syndrome may not meet criteria for clinical diagnosis or genetic testing. Here, we present a phenome-wide association study (PheWAS) approach to systematically explore pleiotropy of common and rare alleles in genes associated with four well-described syndromic diseases (Alagille (AS), Marfan (MS), DiGeorge (DS), and Noonan (NS) syndromes) in the general population.Using human phenotype ontology (HPO) terms, we systematically mapped 60 phenotypes related to AS, MS, DS and NS in 337,198 unrelated white British from the UK Biobank (UKBB) based on their hospital admission records, self-administrated questionnaires, and physiological measurements. We performed logistic regression adjusting for age, sex, and the first 5 genetic principal components, for each phenotype and each variant in the target genes (JAG1, TBX1, FBN1, PTPN11, NOTCH2, and MAP2K1) and performed a gene burden testing.Overall, we observed multiple phenotype-genotype correlations, such as the association between variation in JAG1, FBN1, PTPN11 and SOS2 with diastolic and systolic blood pressure; and pleiotropy among multiple variants in syndromic genes. For example, rs11066309 in PTPN11 was significantly associated with a lower body mass index, an increased risk of hypothyroidism and a smaller size for gestational age, all in concordance with NS-related phenotypes. Similarly, rs589668 in FBN1 was associated with an increase in body height and blood pressure, and a reduced body fat percentage as observed in Marfan syndrome.Our findings suggest that the spectrum of associations of common and rare variants in genes involved in syndromic diseases can be extended to individual phenotypes within the general population.Author SummaryStandard medical evaluation of genetic syndromes relies upon recognizing a characteristic pattern of signs or symptoms to guide targeted genetic testing for confirmation of the diagnosis. This may lead to missing diagnoses in patients with silent or a low expressed form of the syndrome. Here we take advantage of a rich electronic health record, various phenotypic measurements, and genetic information in 337,198 unrelated white British from the UKBB, to study the relation between single syndromic disease phenotypes and genes related to syndromic disease. We show multiple phenotype-genotypes associations in concordance with phenotypes variations found in syndromic diseases. For example, we show that mutation in FBN1 was associated with high standing/sitting height ratio and reduced body fat percentage as observed in individuals with Marfan syndrome. Our findings suggest that common and rare alleles in SD genes are causative of individual component phenotypes present in a general population; further research is needed to characterize the pleiotropic effect of alleles in syndromic genes in persons without the syndromic disease.

SUMMARYMendelian Randomization (MR) is an important causal inference method primarily used in biomedical research. This work applies contemporary techniques in machine learning to improve the robustness and power of traditional MR tools. By denoising and combining candidate genetic variants through techniques from unsupervised probabilistic graphical models, an influential latent instrumental variable is constructed for causal effect estimation. We present results on identifying relationships between biomarkers and the occurrence of coronary artery disease using individual-level real-world data from UK-BioBank via the proposed method. The approach, termed Instrumental Variable sYnthesis (IVY) is proposed as a complement to current methods, and is able to improve results based on allele scoring, particularly at moderate sample sizes.

RationaleCardiac neural crest cells (CNCCs) contribute greatly to cardiovascular development. A thorough understanding of the cell lineages, transcriptomic states and regulatory networks of CNCC derivatives during normal development is essential for deciphering the pathogenesis of CNCC-associated congenital anomalies. However, the transcriptomic landscape of CNCC derivatives during development has not yet been examined at a single-cell resolution.ObjectiveWe sought to systematically characterize the cell lineages, define the developmental chronology and elucidate the transcriptomic dynamics of CNCC derivatives during embryonic and neonatal development.Methods and ResultsWe performed single-cell transcriptomic sequencing of 34,131 CNCC-derived cells in mouse hearts from eight developmental stages between E10.5 and P7. Through single-cell analyses and single-molecule fluorescencein situhybridization, we confirmed the presence of CNCC-derived mural cells. Furthermore, we found the transition from CNCC-derived pericytes to microvascular smooth muscle cells, and identified the genes that were significantly regulated during this transition through pseudo-temporal analysis. CNCC-derived neurons first appeared at E10.5, which was earlier than previously recognized. In addition, the CNCC derivatives switched from a proliferative to a quiescent state with the progression of development. Gradual loss of the neural crest molecular signature with development was also observed in the CNCC derivatives. Our data suggested that many CNCC-derivatives had already committed or differentiated to a specific lineage when migrating to the heart. Finally, we characterized some previously unknown subpopulations of CNCC derivatives during development. For example, we found thatPenk+ cells, which were mainly localized in outflow tract cushions, were all derived from CNCCs.ConclusionsOur study provides novel insights into the cell lineages, molecular signatures, developmental chronology and state change dynamics of CNCC derivatives during embryonic and neonatal development. Our dataset constitutes a valuable resource that will facilitate future efforts in exploring the role of CNCC derivatives in development and disease.

Purpose We sought to determine whether the presence of a systemic SA with potential complicating factors affects waitlist and post-HT outcomes in pediatric patients. Methods This is a single-center retrospective review of pediatric patients listed for HT between January 1, 2009, and July 1, 2018. Patients were selected based on the presence of any underlying syndromes, which included chromosomal anomalies, skeletal myopathies, connective tissue disorders, mitochondrial disease,and other systemic disorders. Waitlist and post-HT outcomes were compared to those without SA. Results A total of 243 patients were listed for HT, of which 21 (9%) patients had associated SA. Of those, 16 (76%) survived to transplant, 3 (14%) died while on the waitlist, 1 (5%) improved and was removed from the waitlist, and 1 (5%) patient is currently listed. Waitlist survival was not different between those with/without an associated syndrome (P = 1.0). Among those who survived to HT, there was no difference in listing days (70 vs 90, P = .8), survival to hospital discharge [14 (93%) vs 150 (95%), P = .6], post-HT intubation days (2 vs 2 days, P = .6), or post-HT hospital length of stay (18 vs 18 days, P = .8). Overall survival during the study period post-HT was not different between groups (P = .8). Conclusion A SA was present in 9% of pediatric patients wait-listed for HT, but was not associated with an increased waitlist mortality or post-HT hospital morbidity or long-term survival. For several anomalies, HT is safe and feasible.

Congenital heart disease is the most common birth defect in newborns and the leading cause of death in infancy, affecting nearly 1% of live births. A locus in chromosome 4p16, adjacent to MSX1 and STX18, has been associated with atrial septal defects (ASD) in multiple European and Chinese cohorts. Here, genotyping data from the UK Biobank was used to test for associations between this locus and congenital heart disease in adult survivors of left ventricular outflow tract obstruction (n=164) and ASD (n=223), with a control sample of 332,788 individuals, and a meta-analysis of the new and existing ASD data was performed.The results show an association between the previously reported markers at 4p16 and risk for either ASD or left ventricular outflow tract obstruction, with effect sizes similar to the published data (OR between 1.27-1.45; all pp−4) for the association with ASD.The findings show that the genetic associations with ASD can be generalized to adult survivors of both ASD and left ventricular lesions. Although the 4p16 associations are statistically compelling, the mentioned alleles confer only a small risk for disease and their frequencies in this adult sample are the same as in children, likely limiting their clinical significance. Further epidemiological and functional studies may elicit factors triggering disease in interaction with the risk alleles.

Copy number variations (CNV) represent a significant proportion of the genetic differences between individuals and many CNVs associate causally with syndromic disease and clinical outcomes. Here, we characterize the landscape of copy number variation and their phenome-wide effects in a sample of 472,228 array-genotyped individuals from the UK Biobank. In addition to population-level selection effects against genic loci conferring high-mortality, we describe genetic burden from syndromic and previously uncharacterized CNV loci across nearly 2,000 quantitative and dichotomous traits, with separate analyses for common and rare classes of variation. Specifically, we highlight the effects of CNVs at two well-known syndromic loci 16p11.2 and 22q11.2, as well as novel associations at 9p23, in the context of acute coronary artery disease and high body mass index. Our data constitute a deeply contextualized portrait of population-wide burden of copy number variation, as well as a series of known and novel dosage-mediated genic associations across the medical phenome.

National Institutes of Health (NIH), Monarch Initiative [OD #5R24OD011883]; Forums for Integrative Phenomics [U13 CA221044-01]; NCATS Data Translator [1OT3TR002019]; NCATS National Center for Digital Health Informatics Innovation [U24 TR002306];NIH Data Commons [1 OT3 OD02464-01 UNCCH]; Cost Action CA 16118 Neuro-MIG; British Heart Foundation Programme Grant [RG/13/5/30112]; Division of Intramural Research; NIAID; NIH; E-RARE project Hipbi-RD [01GM1608]; European Union's Horizon 2020 Research and Innovation Programme [779257]. Funding for open access charge: NIH; Donald A. Roux Family Fund (to P.N.R.). The Human Phenotype Ontology (HPO) - a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases - is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO's interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes.

Somatic mosaicism, the occurrence and propagation of genetic variation in cell lineages after fertilization, is increasingly recognized to play a causal role in a variety of human diseases. We investigated the case of life-threatening arrhythmia in a 10-day-old infant with long QT syndrome (LQTS). Rapid genome sequencing suggested a variant in the sodium channel NaV1.5 encoded by SCN5A, NM_000335:c.5284G > T predicting p.(V1762L), but read depth was insufficient to be diagnostic. Exome sequencing of the trio confirmed read ratios inconsistent with Mendelian inheritance only in the proband. Genotyping of single circulating leukocytes demonstrated the mutation in the genomes of 8% of patient cells, and RNA sequencing of cardiac tissue from the infant confirmed the expression of the mutant allele at mosaic ratios. Heterologous expression of the mutant channel revealed significantly delayed sodium current with a dominant negative effect. To investigate the mechanism by which mosaicism might cause arrhythmia, we built a finite element simulation model incorporating Purkinje fiber activation. This model confirmed the pathogenic consequences of cardiac cellular mosaicism and, under the presenting conditions of this case, recapitulated 2:1 AV block and arrhythmia. To investigate the extent to which mosaicism might explain undiagnosed arrhythmia, we studied 7,500 affected probands undergoing commercial gene-panel testing. Four individuals with pathogenic variants arising from early somatic mutation events were found. Here we establish cardiac mosaicism as a causal mechanism for LQTS and present methods by which the general phenomenon, likely to be relevant for all genetic diseases, can be detected through single-cell analysis and next-generation sequencing.

Background— Maternal diabetes mellitus is associated with an increased risk of offspring congenital heart defects (CHD); however, the causal mechanism is poorly understood. We further investigated this association in a Danish nationwide cohort. Methods and Results— In a national cohort study, we identified 2 025 727 persons born from 1978 to 2011; among them were 7296 (0.36%) persons exposed to maternal pregestational diabetes mellitus. Pregestational diabetes mellitus was identified by using the National Patient Register and individual-level information on all prescriptions filled in Danish pharmacies. Persons with CHD (n=16 325) were assigned to embryologically related cardiac phenotypes. The CHD prevalence in the offspring of mothers with pregestational diabetes mellitus was 318 per 10 000 live births (n=232) in comparison with a baseline risk of 80 per 10 000; the adjusted relative risk for CHD was 4.00 (95% confidence interval, 3.51–4.53). The association was not modified by year of birth, maternal age at diabetes onset, or diabetes duration, and CHD risks associated with type 1 (insulin-dependent) and type 2 (insulin-independent) diabetes mellitus did not differ significantly. Persons born to women with previous acute diabetes complications had a higher CHD risk than those exposed to maternal diabetes mellitus without complications (relative risk, 7.62; 95% confidence interval, 5.23–10.6, and relative risk, 3.49; 95% confidence interval, 2.91–4.13, respectively; P =0.0004). All specific CHD phenotypes were associated with maternal pregestational diabetes mellitus (relative risk range, 2.74–13.8). Conclusions— The profoundly increased CHD risk conferred by maternal pregestational diabetes mellitus neither changed over time nor differed by diabetes subtype. The association with acute pregestational diabetes complications was particularly strong, suggesting a role for glucose in the causal pathway.

This review provides an updated summary of the state of our knowledge of the genetic contributions to the pathogenesis of congenital heart disease. Since 2007, when the initial American Heart Association scientific statement on the genetic basis of congenital heart disease was published, new genomic techniques have become widely available that have dramatically changed our understanding of the causes of congenital heart disease and, clinically, have allowed more accurate definition of the pathogeneses of congenital heart disease in patients of all ages and even prenatally. Information is presented on new molecular testing techniques and their application to congenital heart disease, both isolated and associated with other congenital anomalies or syndromes. Recent advances in the understanding of copy number variants, syndromes, RASopathies, and heterotaxy/ciliopathies are provided. Insights into new research with congenital heart disease models, including genetically manipulated animals such as mice, chicks, and zebrafish, as well as human induced pluripotent stem cell–based approaches are provided to allow an understanding of how future research breakthroughs for congenital heart disease are likely to happen. It is anticipated that this review will provide a large range of health care–related personnel, including pediatric cardiologists, pediatricians, adult cardiologists, thoracic surgeons, obstetricians, geneticists, genetic counselors, and other related clinicians, timely information on the genetic aspects of congenital heart disease. The objective is to provide a comprehensive basis for interdisciplinary care for those with congenital heart disease.

Background Congenital heart disease (CHD) is the most common birth defect group and a significant contributor to neonatal and infant death. CHD with single ventricle anatomy, including hypoplastic left heart syndrome (HLHS), tricuspid atresia (TA), and various double-inlet ventricle (DIV) malformations, is the most complex with the highest mortality. Prenatal risk factors associated with HLHS have been studied, but such data for DIV are lacking. Methods We analyzed DIV cases and nonmalformed controls in the National Birth Defects Prevention Study, a case-control, multicenter population-based study of birth defects. Random forest analysis identified potential predictor variables for DIV, which were included in multivariable models to estimate effect magnitude and directionality. Results Random forest analysis identified pre-pregnancy diabetes, history of maternal insulin use, maternal total lipid intake, paternal race, and intake of several foods and nutrients as potential predictors of DIV. Logistic regression confirmed pre-pregnancy diabetes, maternal insulin use, and paternal race as risk factors for having a child with DIV. Additionally, higher maternal total fat intake was associated with a reduced risk. Conclusions Maternal pre-pregnancy diabetes and history of insulin use were associated with an increased risk of having an infant with DIV, while maternal lipid intake had an inverse association. These novel data provide multiple metabolic pathways for investigation to identify better the developmental etiologies of DIV and suggest that public health interventions targeting diabetes prevention and management in women of childbearing age could reduce CHD risk.

See Article by Szot and Cuny et al Driven by next-generation sequencing, rapid advances in our knowledge of inherited cardiovascular diseases have greatly improved our ability to make molecular diagnoses. Focused gene panels covering select sets of genes implicated in a specific disease are the mainstay of clinical genetic testing. Panel testing has proven to be successful in the diagnosis and management of Mendelian diseases attributable to a single gene such as cystic fibrosis or a limited number of genes as in the case of several cardiomyopathies, channelopathies, and connective tissue disorders. Although gene panels have diagnostic rates surpassing 70% for some inherited cardiovascular diseases, the utility of gene panels has not translated as effectively to the detection of diseases characterized by more complex genetic architecture such as congenital heart disease (CHD). CHD, a heterogeneous group of structural cardiac malformations, is the most common human birth defect and the leading cause of neonatal death because of a congenital illness.1 Hundreds of genes have been identified as having confirmed or putative pathogenicity in causing syndromic and nonsyndromic CHD.2,3 For familial, nonsyndromic cases of CHD, the diagnosis rate for gene panels inclusive of >50 CHD-associated genes is 31% to 46% at best,4,5 a far cry from the diagnosis rate of 75% achieved by a 17-gene panel for long QT syndrome.6 Moreover, the diagnostic yield for nonfamilial (sporadic), nonsyndromic CHD is even slimmer. Although ≈20% of the genetic attributable risk for sporadic CHD has been uncovered to date,7 the genetic cause of the vast majority …

Loss of function variants in NOTCH1 cause left ventricular outflow tract obstructive defects (LVOTO) in a small percentage of families. Clinical surveys report an increased prevalence of missense variants in NOTCH1 in family members of individuals with LVOTO and other types of congenital heart disease (CHD). However, the risk conferred by rare variants in NOTCH1 for LVOTO remains largely uncharacterized. In a cohort of 49 families affected by hypoplastic left heart syndrome, a severe form of LVOTO, we discovered predicted loss of function NOTCH1 variants in 6% of individuals. Rare missense variants were found in an additional 16% of families. To make a quantitative estimate of the genetic risk posed by variants in NOTCH1 for LVOTO, we studied associations of 400 coding and non-coding variants in NOTCH1 in 271 adult cases and 333,571 controls from the UK Biobank. Two rare intronic variants in strong linkage disequilibrium displayed significant association with risk for LVOTO (g.chr9:139427582C>T, Odds Ratio 16.9, p=3.12e-6; g.chr9:139435649C>T, Odds Ratio 19.6, p = 2.44e-6) amongst European-ancestry British individuals. This result was replicated in an independent analysis of 51 cases and 68,901 controls of non-European and mixed ancestry. We conclude that carrying rare predicted loss of function variants or either of two intronic variants in NOTCH1 confer significant risk for LVOTO. Our approach demonstrates the utility of population-based datasets in quantifying the specific risk of individual variants for disease related phenotypes.Author summaryCongenital heart defects are the most common class of birth defect and are present in 1% of live births. Although CHD cases are often clustered in families, and thus the causal variant(s) are seemingly inherited, finding genetic variants causing these defects has been challenging. With the knowledge that variation in the NOTCH1 gene previously has been associated with CHDs affecting the left side of the heart, our aim was to further investigate the role of different types of NOTCH1 variants in left sided CHDs in two cohorts – a cohort of Finnish families with severe lesions affecting the left side of the heart, and the UK Biobank population including individuals with less severe left-sided lesions such as bicuspid aortic valve, congenital aortic stenosis, and coarctation of the aorta. We found a causal loss-of-function NOTCH1 variant in 6% of the families in the Finnish cohort and in the UK Biobank cohort, we identified two rare variants in the non-coding region of NOTCH1, associated with severe left-sided CHDs. These findings support screening of NOTCH1 loss-of-function variants in patients with severe left sided congenital heart defects and suggests that non-coding region variants in NOTCH1 play a role in CHDs.