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Genetic mutations account for a significant percentage of cardiomyopathies, which are a leading cause of congestive heart failure. In hypertrophic cardiomyopathy (HCM), cardiac output is limited by the thickened myocardium through impaired filling and outflow. Mutations in the genes encoding the thick filament components myosin heavy chain and myosin binding protein C (MYH7 and MYBPC3) together explain 75% of inherited HCMs, leading to the observation that HCM is a disease of the sarcomere. Many mutations are \"private\" or rare variants, often unique to families. In contrast, dilated cardiomyopathy (DCM) is far more genetically heterogeneous, with mutations in genes encoding cytoskeletal, nucleoskeletal, mitochondrial, and calcium-handling proteins. DCM is characterized by enlarged ventricular dimensions and impaired systolic and diastolic function. Private mutations account for most DCMs, with few hotspots or recurring mutations. More than 50 single genes are linked to inherited DCM, including many genes that also link to HCM. Relatively few clinical clues guide the diagnosis of inherited DCM, but emerging evidence supports the use of genetic testing to identify those patients at risk for faster disease progression, congestive heart failure, and arrhythmia.

Myocarditis is clinically characterized by chest pain, arrhythmias, and heart failure, and treatment is often supportive. Mutations in DSP, a gene encoding the desmosomal protein desmoplakin, have been increasingly implicated in myocarditis. To model DSP-associated myocarditis and assess the role of innate immunity, we generated engineered heart tissues (EHTs) using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) from patients with heterozygous DSP truncating variants (DSPtvs) and a gene-edited homozygous deletion cell line (DSP-/-). At baseline, DSP-/- EHTs displayed a transcriptomic signature of innate immune activation, which was mirrored by cytokine release. Importantly, DSP-/- EHTs were hypersensitive to Toll-like receptor (TLR) stimulation, demonstrating more contractile dysfunction compared with isogenic controls. Relative to DSP-/- EHTs, heterozygous DSPtv EHTs had less functional impairment. DSPtv EHTs displayed heightened sensitivity to TLR stimulation, and when subjected to strain, DSPtv EHTs developed functional deficits, indicating reduced contractile reserve compared with healthy controls. Colchicine or NF-κB inhibitors improved strain-induced force deficits in DSPtv EHTs. Genomic correction of DSP p.R1951X using adenine base editing reduced inflammatory biomarker release from EHTs. Thus, EHTs replicate electrical and contractile phenotypes seen in human myocarditis, implicating cytokine release as a key part of the myogenic susceptibility to inflammation. The heightened innate immune activation and sensitivity are targets for clinical intervention.

Lamins A and C, encoded by the LMNA gene, are filamentous proteins that form the core scaffold of the nuclear lamina. Dominant LMNA gene mutations cause multiple human diseases including cardiac and skeletal myopathies. The nuclear lamina is thought to regulate gene expression by its direct interaction with chromatin. LMNA gene mutations may mediate disease by disrupting normal gene expression. To investigate the hypothesis that mutant lamin A/C changes the lamina's ability to interact with chromatin, we studied gene misexpression resulting from the cardiomyopathic LMNA E161K mutation and correlated this with changes in chromosome positioning. We identified clusters of misexpressed genes and examined the nuclear positioning of two such genomic clusters, each harboring genes relevant to striated muscle disease including LMO7 and MBNL2. Both gene clusters were found to be more centrally positioned in LMNA-mutant nuclei. Additionally, these loci were less compacted. In LMNA mutant heart and fibroblasts, we found that chromosome 13 had a disproportionately high fraction of misexpressed genes. Using three-dimensional fluorescence in situ hybridization we found that the entire territory of chromosome 13 was displaced towards the center of the nucleus in LMNA mutant fibroblasts. Additional cardiomyopathic LMNA gene mutations were also shown to have abnormal positioning of chromosome 13, although in the opposite direction. These data support a model in which LMNA mutations perturb the intranuclear positioning and compaction of chromosomal domains and provide a mechanism by which gene expression may be altered.

Background Sudden 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. Methods The SDY Case Registry is a National Institutes of Health/Centers for Disease Control and Prevention 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 < 20 years of age. SDY cases were collected from medical examiner and coroner offices spanning 13 US jurisdictions from 2015 to 2019. The cohort included 211 children (median age 0.33 year; range 0–20 years), determined to have died suddenly and unexpectedly and from whom DNA biospecimens for DNA extractions and next-of-kin consent were ascertained. A control cohort consisted of 211 randomly sampled, sex- and ancestry-matched individuals from the 1000 Genomes Project. Genetic variation was evaluated in epilepsy, cardiomyopathy, and arrhythmia genes in the SDY and control cohorts. American College of Medical Genetics/Genomics guidelines were used to classify variants as pathogenic or likely pathogenic. Additionally, pathogenic and likely pathogenic genetic variation was identified using a Bayesian-based artificial intelligence (AI) tool. Results The SDY cohort was 43% European, 29% African, 3% Asian, 16% Hispanic, and 9% 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, potentially damaging variants in epilepsy, cardiomyopathy, and arrhythmia-related genes. A greater number of rare epilepsy genetic variants correlated with younger age at death. Conclusions While 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.

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGS BMI ) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R 2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R 2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGS BMI –covariate interaction effects, modifying PGS BMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R 2 differences among strata and interaction effects – across all covariates, their main effects on BMI were correlated with their maximum R 2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGS BMI individuals have highest R 2 and increase in PGS effect. Using quantile regression, we show the effect of PGS BMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R 2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGS BMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R 2 (mean 23%) across datasets. Finally, creating PGS BMI directly from GxAge genome-wide association studies effects increased relative R 2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGS BMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

Phenotypic heterogeneity is apparent among individuals with putative monogenic disease, such as familial hypertrophic cardiomyopathy. Genome sequencing (GS) allows interrogation of the full spectrum of inborn genetic variation in an individual and RNA profiling provides a snapshot of the cardiac-specific pathogenic effects on gene expression. Identify candidate genetic modifiers of hypertrophic cardiomyopathy phenotype. We performed GS of 48 individuals with variants in , the gene encoding beta myosin heavy chain, and a personal or family history of cardiomyopathy. The genome sequences were annotated with a custom pipeline optimized for cardiovascular gene variant detection. We utilized multiple lines of evidence to prioritize genes together with rare variant gene-based association testing to identify candidate genetic modifiers. GS identified the variant in all 48 cases. Several variants were reclassified based on best available data. We identified known disease-associated genes ( ), candidate modifiers ( , and novel candidate modifiers of cardiomyopathy including and . We identified regulatory variants and intergenic regions associated with the phenotypes. Using RNA profiling, we show that several genes identified through gene-based association testing are differentially regulated in human hypertrophic cardiomyopathy, and in models of disease. Evaluation of the whole genome, even in the case of alleged monogenic disease, leads to important new insights. The identified variants, regions, and genes are candidates to modify disease presentation in cardiomyopathy.

Background: A 25-base pair (25bp) intronic deletion in the MYBPC3 gene enriched in South Asians (SAs) is a risk allele for late-onset left ventricular (LV) dysfunction, hypertrophy, and heart failure (HF) with several forms of cardiomyopathy. However, the effect of this variant on exercise parameters has not been evaluated. Methods: As a pilot study, 10 asymptomatic SA carriers of the MYBPC3 Δ25bp variant (52.9 ± 2.14 years) and 10 age- and gender-matched non-carriers (NCs) (50.1 ± 2.7 years) were evaluated at baseline and under exercise stress conditions using bicycle exercise echocardiography and continuous cardiac monitoring. Results: Baseline echocardiography parameters were not different between the two groups. However, in response to exercise stress, the carriers of Δ25bp had significantly higher LV ejection fraction (%) (CI: 4.57 ± 1.93; p < 0.0001), LV outflow tract peak velocity (m/s) (CI: 0.19 ± 0.07; p < 0.0001), and higher aortic valve (AV) peak velocity (m/s) (CI: 0.103 ± 0.08; p = 0.01) in comparison to NCs, and E/A ratio, a marker of diastolic compliance, was significantly lower in Δ25bp carriers (CI: 0.107 ± 0.102; p = 0.038). Interestingly, LV end-diastolic diameter (LVID dia ) was augmented in NCs in response to stress, while it did not increase in Δ25bp carriers (CI: 0.239 ± 0.125; p = 0.0002). Further, stress-induced right ventricular systolic excursion velocity s' (m/s), as a marker of right ventricle function, increased similarly in both groups, but tricuspid annular plane systolic excursion increased more in carriers (slope: 0.008; p = 0.0001), suggesting right ventricle functional differences between the two groups. Conclusions: These data support that MYBPC3 Δ25bp is associated with LV hypercontraction under stress conditions with evidence of diastolic impairment.

Heart failure is a complex trait, influenced by environmental and genetic factors, affecting over 30 million individuals worldwide. Here we report common-variant and rare-variant association studies of all-cause heart failure and examine how different classes of genetic variation impact its heritability. We identify 176 common-variant risk loci at genome-wide significance in 2,358,556 individuals and cluster these signals into five broad modules based on pleiotropic associations with anthropomorphic traits/obesity, blood pressure/renal function, atherosclerosis/lipids, immune activity and arrhythmias. In parallel, we uncover exome-wide significant associations for heart failure and rare predicted loss-of-function variants in TTN , MYBPC3 , FLNC and BAG3 using exome sequencing of 376,334 individuals. We find that total burden heritability of rare coding variants is highly concentrated in a small set of Mendelian cardiomyopathy genes, while common-variant heritability is diffusely spread throughout the genome. Finally, we show that common-variant background modifies heart failure risk among carriers of rare pathogenic truncating variants in TTN . Together, these findings discern genetic links between dysregulated metabolism and heart failure and highlight a polygenic component to heart failure not captured by current clinical genetic testing. Common-variant and rare-variant association analyses combining datasets from multiple populations yield insights into the genetic architecture of all-cause heart failure across the allele-frequency spectrum.

Myocarditis is clinically characterized by chest pain, arrhythmias, and heart failure, and treatment is often supportive. Mutations in DSP, a gene encoding the desmosomal protein desmoplakin, have been increasingly implicated in myocarditis. To model DSP-associated myocarditis and assess the role of innate immunity, we generated engineered heart tissues (EHTs) using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) from patients with heterozygous DSP truncating variants (DSPtvs) and a gene-edited homozygous deletion cell line ([DSP.sup.-/-]). At baseline, [DSP.sup.-/-] EHTs displayed a transcriptomic signature of innate immune activation, which was mirrored by cytokine release. Importantly, [DSP.sup.- /-] EHTs were hypersensitive to Toll-like receptor (TLR) stimulation, demonstrating more contractile dysfunction compared with isogenic controls. Relative to [DSP.sup.-/-] EHTs, heterozygous DSPtv EHTs had less functional impairment. DSPtv EHTs displayed heightened sensitivity to TLR stimulation, and when subjected to strain, DSPtv EHTs developed functional deficits, indicating reduced contractile reserve compared with healthy controls. Colchicine or NF-[kappa]B inhibitors improved straininduced force deficits in DSPtv EHTs. Genomic correction of DSP p.R1951X using adenine base editing reduced inflammatory biomarker release from EHTs. Thus, EHTs replicate electrical and contractile phenotypes seen in human myocarditis, implicating cytokine release as a key part of the myogenic susceptibility to inflammation. The heightened innate immune activation and sensitivity are targets for clinical intervention.

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGS BMI ) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R 2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R 2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGS BMI –covariate interaction effects, modifying PGS BMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R 2 differences among strata and interaction effects – across all covariates, their main effects on BMI were correlated with their maximum R 2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGS BMI individuals have highest R 2 and increase in PGS effect. Using quantile regression, we show the effect of PGS BMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R 2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGS BMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R 2 (mean 23%) across datasets. Finally, creating PGS BMI directly from GxAge genome-wide association studies effects increased relative R 2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGS BMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.