Whole genome sequencing delineates regulatory, copy number, and cryptic splice variants in early onset cardiomyopathy.

Cardiomyopathy (CMP) is a heritable disorder. Over 50% of cases are gene-elusive on clinical gene panel testing. The contribution of variants in non-coding DNA elements that result in cryptic splicing and regulate gene expression has not been explored. We analyzed whole-genome sequencing (WGS) data in a discovery cohort of 209 pediatric CMP patients and 1953 independent replication genomes and exomes. We searched for protein-coding variants, and non-coding variants predicted to affect the function or expression of genes. Thirty-nine percent of cases harbored pathogenic coding variants in known CMP genes, and 5% harbored high-risk loss-of-function (LoF) variants in additional candidate CMP genes. Fifteen percent harbored high-risk regulatory variants in promoters and enhancers of CMP genes (odds ratio 2.25, p = 6.70 × 10-7 versus controls). Genes involved in α-dystroglycan glycosylation (FKTN, DTNA) and desmosomal signaling (DSC2, DSG2) were most highly enriched for regulatory variants (odds ratio 6.7-58.1). Functional effects were confirmed in patient myocardium and reporter assays in human cardiomyocytes, and in zebrafish CRISPR knockouts. We provide strong evidence for the genomic contribution of functionally active variants in new genes and in regulatory elements of known CMP genes to early onset CMP.

Common deletion variants causing protocadherin-α deficiency contribute to the complex genetics of BAV and left-sided congenital heart disease.

Bicuspid aortic valve (BAV) with ~1%-2% prevalence is the most common congenital heart defect (CHD). It frequently results in valve disease and aorta dilation and is a major cause of adult cardiac surgery. BAV is genetically linked to rare left-heart obstructions (left ventricular outflow tract obstructions [LVOTOs]), including hypoplastic left heart syndrome (HLHS) and coarctation of the aorta (CoA). Mouse and human studies indicate LVOTO is genetically heterogeneous with a complex genetic etiology. Homozygous mutation in the Pcdha protocadherin gene cluster in mice can cause BAV, and also HLHS and other LVOTO phenotypes when accompanied by a second mutation. Here we show two common deletion copy number variants (delCNVs) within the PCDHA gene cluster are associated with LVOTO. Analysis of 1,218 white individuals with LVOTO versus 463 disease-free local control individuals yielded odds ratios (ORs) at 1.47 (95% confidence interval [CI], 1.13-1.92; p = 4.2 × 10-3) for LVOTO, 1.47 (95% CI, 1.10-1.97; p = 0.01) for BAV, 6.13 (95% CI, 2.75-13.7; p = 9.7 × 10-6) for CoA, and 1.49 (95% CI, 1.07-2.08; p = 0.019) for HLHS. Increased OR was observed for all LVOTO phenotypes in homozygous or compound heterozygous PCDHA delCNV genotype comparison versus wild type. Analysis of an independent white cohort (381 affected individuals, 1,352 control individuals) replicated the PCDHA delCNV association with LVOTO. Generalizability of these findings is suggested by similar observations in Black and Chinese individuals with LVOTO. Analysis of Pcdha mutant mice showed reduced PCDHA expression at regions of cell-cell contact in aortic smooth muscle and cushion mesenchyme, suggesting potential mechanisms for BAV pathogenesis and aortopathy. Together, these findings indicate common variants causing PCDHA deficiency play a significant role in the genetic etiology of common and rare LVOTO-CHD.

A Validated Model for Sudden Cardiac Death Risk Prediction in Pediatric Hypertrophic Cardiomyopathy.

BACKGROUND: Hypertrophic cardiomyopathy is the leading cause of sudden cardiac death (SCD) in children and young adults. Our objective was to develop and validate a SCD risk prediction model in pediatric hypertrophic cardiomyopathy to guide SCD prevention strategies. METHODS: In an international multicenter observational cohort study, phenotype-positive patients with isolated hypertrophic cardiomyopathy <18 years of age at diagnosis were eligible. The primary outcome variable was the time from diagnosis to a composite of SCD events at 5-year follow-up: SCD, resuscitated sudden cardiac arrest, and aborted SCD, that is, appropriate shock following primary prevention implantable cardioverter defibrillators. Competing risk models with cause-specific hazard regression were used to identify and quantify clinical and genetic factors associated with SCD. The cause-specific regression model was implemented using boosting, and tuned with 10 repeated 4-fold cross-validations. The final model was fitted using all data with the tuned hyperparameter value that maximizes the c-statistic, and its performance was characterized by using the c-statistic for competing risk models. The final model was validated in an independent external cohort (SHaRe [Sarcomeric Human Cardiomyopathy Registry], n=285). RESULTS: Overall, 572 patients met eligibility criteria with 2855 patient-years of follow-up. The 5-year cumulative proportion of SCD events was 9.1% (14 SCD, 25 resuscitated sudden cardiac arrests, and 14 aborted SCD). Risk predictors included age at diagnosis, documented nonsustained ventricular tachycardia, unexplained syncope, septal diameter z-score, left ventricular posterior wall diameter z score, left atrial diameter z score, peak left ventricular outflow tract gradient, and presence of a pathogenic variant. Unlike in adults, left ventricular outflow tract gradient had an inverse association, and family history of SCD had no association with SCD. Clinical and clinical/genetic models were developed to predict 5-year freedom from SCD. Both models adequately discriminated between patients with and without SCD events with a c-statistic of 0.75 and 0.76, respectively, and demonstrated good agreement between predicted and observed events in the primary and validation cohorts (validation c-statistic 0.71 and 0.72, respectively). CONCLUSION: Our study provides a validated SCD risk prediction model with >70% prediction accuracy and incorporates risk factors that are unique to pediatric hypertrophic cardiomyopathy. An individualized risk prediction model has the potential to improve the application of clinical practice guidelines and shared decision making for implantable cardioverter defibrillator insertion. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT0403679.

Machine Learning Identifies Clinical and Genetic Factors Associated With Anthracycline Cardiotoxicity in Pediatric Cancer Survivors.

BACKGROUND: Despite known clinical risk factors, predicting anthracycline cardiotoxicity remains challenging. OBJECTIVES: This study sought to develop a clinical and genetic risk prediction model for anthracycline cardiotoxicity in childhood cancer survivors. METHODS: We performed exome sequencing in 289 childhood cancer survivors at least 3 years from anthracycline exposure. In a nested case-control design, 183 case patients with reduced left ventricular ejection fraction despite low-dose doxorubicin (≤250 mg/m2), and 106 control patients with preserved left ventricular ejection fraction despite doxorubicin >250 mg/m2 were selected as extreme phenotypes. Rare/low-frequency variants were collapsed to identify genes differentially enriched for variants between case patients and control patients. The expression levels of 5 top-ranked genes were evaluated in human induced pluripotent stem cell-derived cardiomyocytes, and variant enrichment was confirmed in a replication cohort. Using random forest, a risk prediction model that included genetic and clinical predictors was developed. RESULTS: Thirty-one genes were differentially enriched for variants between case patients and control patients (p < 0.001). Only 42.6% case patients harbored a variant in these genes compared to 89.6% control patients (odds ratio: 0.09; 95% confidence interval: 0.04 to 0.17; p = 3.98 × 10-15). A risk prediction model for cardiotoxicity that included clinical and genetic factors had a higher prediction accuracy and lower misclassification rate compared to the clinical-only model. In vitro inhibition of gene-associated pathways (PI3KR2, ZNF827) provided protection from cardiotoxicity in cardiomyocytes. CONCLUSIONS: Our study identified variants in cardiac injury pathway genes that protect against cardiotoxicity and informed the development of a prediction model for delayed anthracycline cardiotoxicity, and it also provided new targets in autophagy genes for the development of cardio-protective drugs. (Preventing Cardiac Sequelae in Pediatric Cancer Survivors [PCS2]; NCT01805778).

Return of genetic and genomic research findings: experience of a pediatric biorepository.

BACKGROUND: Assess process, uptake, validity and resource needs for return of actionable research findings to biobank participants. METHODS: Participants were prospectively enrolled in a multicenter biorepository of childhood onset heart disease. Clinically actionable research findings were reviewed by a Return of Research Results Committee (RRR) and returned to the physician or disclosed directly to the participant through a research genetic counselor. Action taken following receipt of this information was reviewed. RESULTS: Genetic data was generated in 1963 of 7408 participants. Fifty-nine new findings were presented to the RRR committee; 20 (34%) were deemed reportable. Twelve were returned to the physician, of which 7 were disclosed to participants (median time to disclosure, 192 days). Seven findings were returned to the research genetic counselor; all have been disclosed (median time to disclosure, 19 days). Twelve families (86%) opted for referral to clinical genetics after disclosure of findings; 7 results have been validated, 5 results are pending. Average cost of return and disclosure per reportable finding incurred by the research program was $750 when utilizing a research genetic counselor; clinical costs associated with return were not included. CONCLUSIONS: Return of actionable research findings was faster if disclosed directly to the participant by a research genetic counselor. There was a high acceptability amongst participants for receiving the findings, for referral to clinical genetics, and for clinical validation of research findings, with all referred cases being clinically confirmed.