Genetic constraint at single amino acid resolution in protein domains improves missense variant prioritisation and gene discovery.

BACKGROUND: One of the major hurdles in clinical genetics is interpreting the clinical consequences associated with germline missense variants in humans. Recent significant advances have leveraged natural variation observed in large-scale human populations to uncover genes or genomic regions that show a depletion of natural variation, indicative of selection pressure. We refer to this as "genetic constraint". Although existing genetic constraint metrics have been demonstrated to be successful in prioritising genes or genomic regions associated with diseases, their spatial resolution is limited in distinguishing pathogenic variants from benign variants within genes. METHODS: We aim to identify missense variants that are significantly depleted in the general human population. Given the size of currently available human populations with exome or genome sequencing data, it is not possible to directly detect depletion of individual missense variants, since the average expected number of observations of a variant at most positions is less than one. We instead focus on protein domains, grouping homologous variants with similar functional impacts to examine the depletion of natural variations within these comparable sets. To accomplish this, we develop the Homologous Missense Constraint (HMC) score. We utilise the Genome Aggregation Database (gnomAD) 125 K exome sequencing data and evaluate genetic constraint at quasi amino-acid resolution by combining signals across protein homologues. RESULTS: We identify one million possible missense variants under strong negative selection within protein domains. Though our approach annotates only protein domains, it nonetheless allows us to assess 22% of the exome confidently. It precisely distinguishes pathogenic variants from benign variants for both early-onset and adult-onset disorders. It outperforms existing constraint metrics and pathogenicity meta-predictors in prioritising de novo mutations from probands with developmental disorders (DD). It is also methodologically independent of these, adding power to predict variant pathogenicity when used in combination. We demonstrate utility for gene discovery by identifying seven genes newly significantly associated with DD that could act through an altered-function mechanism. CONCLUSIONS: Grouping variants of comparable functional impacts is effective in evaluating their genetic constraint. HMC is a novel and accurate predictor of missense consequence for improved variant interpretation.

A novel tool for arrhythmic risk stratification in desmoplakin gene variant carriers.

BACKGROUND AND AIMS: Pathogenic desmoplakin (DSP) gene variants are associated with the development of a distinct form of arrhythmogenic cardiomyopathy known as DSP cardiomyopathy. Patients harbouring these variants are at high risk for sustained ventricular arrhythmia (VA), but existing tools for individualized arrhythmic risk assessment have proven unreliable in this population. METHODS: Patients from the multi-national DSP-ERADOS (Desmoplakin SPecific Effort for a RAre Disease Outcome Study) Network patient registry who had pathogenic or likely pathogenic DSP variants and no sustained VA prior to enrolment were followed longitudinally for the development of first sustained VA event. Clinically guided, step-wise Cox regression analysis was used to develop a novel clinical tool predicting the development of incident VA. Model performance was assessed by c-statistic in both the model development cohort (n = 385) and in an external validation cohort (n = 86). RESULTS: In total, 471 DSP patients [mean age 37.8 years, 65.6% women, 38.6% probands, 26% with left ventricular ejection fraction (LVEF) < 50%] were followed for a median of 4.0 (interquartile range: 1.6-7.3) years; 71 experienced first sustained VA events {2.6% [95% confidence interval (CI): 2.0, 3.5] events/year}. Within the development cohort, five readily available clinical parameters were identified as independent predictors of VA and included in a novel DSP risk score: female sex [hazard ratio (HR) 1.9 (95% CI: 1.1-3.4)], history of non-sustained ventricular tachycardia [HR 1.7 (95% CI: 1.1-2.8)], natural logarithm of 24-h premature ventricular contraction burden [HR 1.3 (95% CI: 1.1-1.4)], LVEF < 50% [HR 1.5 (95% CI: .95-2.5)], and presence of moderate to severe right ventricular systolic dysfunction [HR 6.0 (95% CI: 2.9-12.5)]. The model demonstrated good risk discrimination within both the development [c-statistic .782 (95% CI: .77-.80)] and external validation [c-statistic .791 (95% CI: .75-.83)] cohorts. The negative predictive value for DSP patients in the external validation cohort deemed to be at low risk for VA (<5% at 5 years; n = 26) was 100%. CONCLUSIONS: The DSP risk score is a novel model that leverages readily available clinical parameters to provide individualized VA risk assessment for DSP patients. This tool may help guide decision-making for primary prevention implantable cardioverter-defibrillator placement in this high-risk population and supports a gene-first risk stratification approach.

Long-Term Arrhythmic Follow-Up and Risk Stratification of Patients With Desmoplakin-Associated Arrhythmogenic Right Ventricular Cardiomyopathy.

Background: Patients with likely pathogenic/pathogenic desmoplakin (DSP) variants are poorly characterized. Some of them meet diagnostic criteria for arrhythmogenic right ventricular cardiomyopathy (ARVC), but it is unclear how risk stratification strategies for ARVC perform in this setting. Objectives: The purpose of this study was to characterize arrhythmic outcomes and to test the performance of the recently validated ARVC risk calculator in patients with DSP likely pathogenic/pathogenic variants fulfilling definite 2010 ARVC Task Force Criteria (DSP-TFC+). Methods: DSP-TFC+ patients were enrolled from 20 institutions across 3 continents. Ventricular arrhythmias (VA), defined as a composite of sustained ventricular tachycardia (VT), appropriate implantable cardioverter defibrillator therapies, and ventricular fibrillation/sudden cardiac death events in follow-up, were reported as the primary outcome. We tested the performance of the ARVC risk calculator for VA prediction, reporting c-statistics. Results: Among 252 DSP-TFC+ patients (age 39.6 ± 16.9 years, 35.3% male), 94 (37.3%) experienced VA over 44.5 [IQR: 19.6-78.3] months. Patients with left ventricle involvement (n = 194) were at higher VA risk (log-rank P = 0.0239). History of nonsustained VT (aHR 2.097; P = 0.004) showed the strongest association with VA occurrence during the first 5-year follow-up. Neither age (P = 0.723) nor male sex (P = 0.200) was associated with VAs at follow-up. In 204 patients without VA at diagnosis, incident VA rate was high (32.8%; 7.37%/y). The ARVC risk calculator performed poorly overall (c-statistic 0.604 [0.594-0.614]) and very poorly in patients with left ventricular disease (c-statistic 0.558 [0.556-0.560]). Conclusions: DSP-TFC+ patients are at substantial risk for VAs. The ARVC risk calculator performs poorly in DSP-TFC+ patients suggesting need for a gene-specific risk algorithm. Meanwhile, DSP-TFC+ patients with nonsustained VT should be considered as high-risk.

Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease.

The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950 12-lead ECGs from the Beth Israel Deaconess Medical Center (BIDMC), a secondary care cohort, and validated on UK Biobank (UKB) (n = 42,386), the model achieved a Pearson correlation coefficient (r) of 0.65 and 0.62, and an R2 of 0.43 and 0.39 in the BIDMC cohort and UK Biobank, respectively for AI-ECG BMI vs. measured BMI. We found delta-BMI, the difference between measured BMI and AI-ECG-predicted BMI (AI-ECG-BMI), to be a biomarker of cardiometabolic health. The top tertile of delta-BMI showed increased risk of future cardiometabolic disease (BIDMC: HR 1.15, p < 0.001; UKB: HR 1.58, p < 0.001) and diabetes mellitus (BIDMC: HR 1.25, p < 0.001; UKB: HR 2.28, p < 0.001) after adjusting for covariates including measured BMI. Significant enhancements in model fit, reclassification and improvements in discriminatory power were observed with the inclusion of delta-BMI in both cohorts. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits, including SCN10A, SCN5A, EXOG and RXRG. In summary, our AI-ECG-BMI model accurately predicts BMI and introduces delta-BMI as a non-invasive biomarker for cardiometabolic risk stratification.

Bariatric surgery partially reverses subclinical proarrhythmic structural, electrophysiological, and autonomic changes in obesity.

BACKGROUND: Obesity confers higher risks of cardiac arrhythmias. The extent to which weight loss reverses subclinical proarrhythmic adaptations in arrhythmia-free obese individuals is unknown. OBJECTIVE: The purpose of this study was to study structural, electrophysiological, and autonomic remodeling in arrhythmia-free obese patients and their reversibility with bariatric surgery using electrocardiographic imaging (ECGi). METHODS: Sixteen arrhythmia-free obese patients (mean age 43 ± 12 years; 13 females; BMI 46.7 ± 5.5 kg/m2) had ECGi pre-bariatric surgery, of whom 12 had ECGi postsurgery (BMI 36.8 ± 6.5 kg/m2). Sixteen age- and sex-matched lean healthy individuals (mean age 42 ± 11 years; BMI 22.8 ± 2.6 kg/m2) acted as controls and had ECGi only once. RESULTS: Obesity was associated with structural (increased epicardial fat volumes and left ventricular mass), autonomic (blunted heart rate variability), and electrophysiological (slower atrial conduction and steeper ventricular repolarization gradients) remodeling. After bariatric surgery, there was partial structural reverse remodeling, with a reduction in epicardial fat volumes (68.7 cm3 vs 64.5 cm3; P = .0010) and left ventricular mass (33 g/m2.7 vs 25 g/m2.7; P < .0005). There was also partial electrophysiological reverse remodeling with a reduction in mean spatial ventricular repolarization gradients (26 mm/ms vs 19 mm/ms; P = .0009), although atrial activation remained prolonged. Heart rate variability, quantified by standard deviation of successive differences in R-R intervals, was also partially improved after bariatric surgery (18.7 ms vs 25.9 ms; P = .017). Computational modeling showed that presurgery obese hearts had a larger window of vulnerability to unidirectional block and had an earlier spiral-wave breakup with more complex reentry patterns than did postsurgery counterparts. CONCLUSION: Obesity is associated with adverse electrophysiological, structural, and autonomic remodeling that is partially reversed after bariatric surgery. These data have important implications for bariatric surgery weight thresholds and weight loss strategies.

Comparative prognostic importance of measures of left atrial structure and function in non-ischaemic dilated cardiomyopathy.

AIMS: To compare the association between measures of left atrial (LA) structure and function, derived from cardiovascular magnetic resonance (CMR), with cardiovascular (CV) death or non-fatal heart failure (HF) events in patients with non-ischaemic dilated cardiomyopathy (DCM). METHODS AND RESULTS: CMR studies of 580 prospectively recruited patients with DCM in sinus rhythm (median age 54 [interquartile range 44-64] years, 61% men, median LVEF 42% [30-51%]) were analysed for measures of LA structure (left atrial maximum volume index [LAVImax], left atrial minimum volume index [LAVImin]) and function (left atrial emptying fraction [LAEF], left atrial reservoir strain [LARS], left atrial conduit strain [LACS] and left atrial booster strain [LABS]). Over median follow-up of 7.4 years, 103 patients (18%) met the primary endpoint. Apart from LACS, each measure of LA structure and function was associated with the primary endpoint after adjusting for other important prognostic variables. The addition of each LA metric to a baseline model containing the same important prognostic covariates improved model discrimination, with LAVImin providing the greatest improvement (C-statistic improvement: 0.702 to 0.738; χ2 test comparing likelihood ratio p < 0.0001; categorical net reclassification index: 0.210 (95% CI 0.023-0.392)). Patients in the highest tercile of LAVImin had similar event rates to those with persistent atrial fibrillation. Measures of LA strain did not enhance model discrimination above LA volumetric measures. CONCLUSION: Measure of left atrial structure and function offer important prognostic information in patients with DCM and enhance prediction of adverse outcomes. LA strain was not incremental to volumetric analysis for risk prediction.

LAVASET: Latent Variable Stochastic Ensemble of Trees. An ensemble method for correlated datasets with spatial, spectral, and temporal dependencies.

MOTIVATION: Random forests (RFs) can deal with a large number of variables, achieve reasonable prediction scores, and yield highly interpretable feature importance values. As such, RFs are appropriate models for feature selection and further dimension reduction. However, RFs are often not appropriate for correlated datasets due to their mode of selecting individual features for splitting. Addressing correlation relationships in high-dimensional datasets is imperative for reducing the number of variables that are assigned high importance, hence making the dimension reduction most efficient. Here, we propose the LAtent VAriable Stochastic Ensemble of Trees (LAVASET) method that derives latent variables based on the distance characteristics of each feature and aims to incorporate the correlation factor in the splitting step. RESULTS: Without compromising on performance in the majority of examples, LAVASET outperforms RF by accurately determining feature importance across all correlated variables and ensuring proper distribution of importance values. LAVASET yields mostly non-inferior prediction accuracies to traditional RFs when tested in simulated and real 1D datasets, as well as more complex and high-dimensional 3D datatypes. Unlike traditional RFs, LAVASET is unaffected by single 'important' noisy features (false positives), as it considers the local neighbourhood. LAVASET, therefore, highlights neighbourhoods of features, reflecting real signals that collectively impact the model's predictive ability. AVAILABILITY AND IMPLEMENTATION: LAVASET is freely available as a standalone package from https://github.com/melkasapi/LAVASET.

Assessing the association between genetic and phenotypic features of dilated cardiomyopathy and outcome in patients with coronary artery disease.

AIMS: To examine the relevance of genetic and cardiovascular magnetic resonance (CMR) features of dilated cardiomyopathy (DCM) in individuals with coronary artery disease (CAD). METHODS AND RESULTS: This study includes two cohorts. First, individuals with CAD recruited into the UK Biobank (UKB) were evaluated. Second, patients with CAD referred to a tertiary centre for evaluation with late gadolinium enhancement (LGE)-CMR were recruited (London cohort); patients underwent genetic sequencing as part of the research protocol and long-term follow-up. From 31 154 individuals with CAD recruited to UKB, rare pathogenic variants in DCM genes were associated with increased risk of death or major adverse cardiac events (hazard ratio 1.57, 95% confidence interval [CI] 1.22-2.01, p < 0.001). Of 1619 individuals with CAD included from the UKB CMR substudy, participants with a rare variant in a DCM-associated gene had lower left ventricular ejection fraction (LVEF) compared to genotype negative individuals (mean 47 ± 10% vs. 57 ± 8%, p < 0.001). Of 453 patients in the London cohort, 63 (14%) had non-infarct pattern LGE (NI-LGE) on CMR. Patients with NI-LGE had lower LVEF (mean 38 ± 18% vs. 48 ± 16%, p < 0.001) compared to patients without NI-LGE, with no significant difference in the burden of rare protein altering variants in DCM-associated genes between groups (9.5% vs. 6.7%, odds ratio 1.5, 95% CI 0.4-4.3, p = 0.4). NI-LGE was not independently associated with adverse clinical outcomes. CONCLUSION: Rare pathogenic variants in DCM-associated genes impact left ventricular remodelling and outcomes in stable CAD. NI-LGE is associated with adverse remodelling but is not an independent predictor of outcome and had no rare genetic basis in our study.

Sex Differences in the Clinical Presentation and Natural History of Dilated Cardiomyopathy.

BACKGROUND: Biological sex has a diverse impact on the cardiovascular system. Its influence on dilated cardiomyopathy (DCM) remains unresolved. OBJECTIVES: This study aims to investigate sex-specific differences in DCM presentation, natural history, and prognostic factors. METHODS: The authors conducted a prospective observational cohort study of DCM patients assessing baseline characteristics, cardiac magnetic resonance imaging, biomarkers, and genotype. The composite outcome was cardiovascular mortality or major heart failure (HF) events. RESULTS: Overall, 206 females and 398 males with DCM were followed for a median of 3.9 years. At baseline, female patients had higher left ventricular ejection fraction, smaller left ventricular volumes, less prevalent mid-wall myocardial fibrosis (23% vs 42%), and lower high-sensitivity cardiac troponin I than males (all P < 0.05) with no difference in time from diagnosis, age at enrollment, N-terminal pro-B-type natriuretic peptide levels, pathogenic DCM genetic variants, myocardial fibrosis extent, or medications used for HF. Despite a more favorable profile, the risk of the primary outcome at 2 years was higher in females than males (8.6% vs 4.4%, adjusted HR: 3.14; 95% CI: 1.55-6.35; P = 0.001). Between 2 and 5 years, the effect of sex as a prognostic modifier attenuated. Age, mid-wall myocardial fibrosis, left ventricular ejection fraction, left atrial volume, N-terminal pro-B-type natriuretic peptide, high-sensitivity cardiac troponin I, left bundle branch block, and NYHA functional class were not sex-specific prognostic factors. CONCLUSIONS: The authors identified a novel paradox in prognosis for females with DCM. Female DCM patients have a paradoxical early increase in major HF events despite less prevalent myocardial fibrosis and a milder phenotype at presentation. Future studies should interrogate the mechanistic basis for these sex differences.

Toward robust clinical genome interpretation: Developing a consistent terminology to characterize Mendelian disease-gene relationships-allelic requirement, inheritance modes, and disease mechanisms.

PURPOSE: The terminology used for gene-disease curation and variant annotation to describe inheritance, allelic requirement, and both sequence and functional consequences of a variant is currently not standardized. There is considerable discrepancy in the literature and across clinical variant reporting in the derivation and application of terms. Here, we standardize the terminology for the characterization of disease-gene relationships to facilitate harmonized global curation and to support variant classification within the ACMG/AMP framework. METHODS: Terminology for inheritance, allelic requirement, and both structural and functional consequences of a variant used by Gene Curation Coalition members and partner organizations was collated and reviewed. Harmonized terminology with definitions and use examples was created, reviewed, and validated. RESULTS: We present a standardized terminology to describe gene-disease relationships, and to support variant annotation. We demonstrate application of the terminology for classification of variation in the ACMG SF 2.0 genes recommended for reporting of secondary findings. Consensus terms were agreed and formalized in both Sequence Ontology (SO) and Human Phenotype Ontology (HPO) ontologies. Gene Curation Coalition member groups intend to use or map to these terms in their respective resources. CONCLUSION: The terminology standardization presented here will improve harmonization, facilitate the pooling of curation datasets across international curation efforts and, in turn, improve consistency in variant classification and genetic test interpretation.

Genotype-Phenotype Taxonomy of Hypertrophic Cardiomyopathy.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is an important cause of sudden cardiac death associated with heterogeneous phenotypes, but there is no systematic framework for classifying morphology or assessing associated risks. Here, we quantitatively survey genotype-phenotype associations in HCM to derive a data-driven taxonomy of disease expression. METHODS: We enrolled 436 patients with HCM (median age, 60 years; 28.8% women) with clinical, genetic, and imaging data. An independent cohort of 60 patients with HCM from Singapore (median age, 59 years; 11% women) and a reference population from the UK Biobank (n=16 691; mean age, 55 years; 52.5% women) were also recruited. We used machine learning to analyze the 3-dimensional structure of the left ventricle from cardiac magnetic resonance imaging and build a tree-based classification of HCM phenotypes. Genotype and mortality risk distributions were projected on the tree. RESULTS: Carriers of pathogenic or likely pathogenic variants for HCM had lower left ventricular mass, but greater basal septal hypertrophy, with reduced life span (mean follow-up, 9.9 years) compared with genotype negative individuals (hazard ratio, 2.66 [95% CI, 1.42-4.96]; P<0.002). Four main phenotypic branches were identified using unsupervised learning of 3-dimensional shape: (1) nonsarcomeric hypertrophy with coexisting hypertension; (2) diffuse and basal asymmetrical hypertrophy associated with outflow tract obstruction; (3) isolated basal hypertrophy; and (4) milder nonobstructive hypertrophy enriched for familial sarcomeric HCM (odds ratio for pathogenic or likely pathogenic variants, 2.18 [95% CI, 1.93-2.28]; P=0.0001). Polygenic risk for HCM was also associated with different patterns and degrees of disease expression. The model was generalizable to an independent cohort (trustworthiness, M1: 0.86-0.88). CONCLUSIONS: We report a data-driven taxonomy of HCM for identifying groups of patients with similar morphology while preserving a continuum of disease severity, genetic risk, and outcomes. This approach will be of value in understanding the causes and consequences of disease diversity.

Beyond gene-disease validity: capturing structured data on inheritance, allelic requirement, disease-relevant variant classes, and disease mechanism for inherited cardiac conditions.

BACKGROUND: As the availability of genomic testing grows, variant interpretation will increasingly be performed by genomic generalists, rather than domain-specific experts. Demand is rising for laboratories to accurately classify variants in inherited cardiac condition (ICC) genes, including secondary findings. METHODS: We analyse evidence for inheritance patterns, allelic requirement, disease mechanism and disease-relevant variant classes for 65 ClinGen-curated ICC gene-disease pairs. We present this information for the first time in a structured dataset, CardiacG2P, and assess application in genomic variant filtering. RESULTS: For 36/65 gene-disease pairs, loss of function is not an established disease mechanism, and protein truncating variants are not known to be pathogenic. Using the CardiacG2P dataset as an initial variant filter allows for efficient variant prioritisation whilst maintaining a high sensitivity for retaining pathogenic variants compared with two other variant filtering approaches. CONCLUSIONS: Access to evidence-based structured data representing disease mechanism and allelic requirement aids variant filtering and analysis and is a pre-requisite for scalable genomic testing.

Phenotype, outcomes and natural history of early-stage non-ischaemic cardiomyopathy.

AIMS: To characterize the phenotype, clinical outcomes and rate of disease progression in patients with early-stage non-ischaemic cardiomyopathy (early-NICM). METHODS AND RESULTS: We conducted a prospective observational cohort study of patients with early-NICM assessed by late gadolinium enhancement cardiovascular magnetic resonance (CMR). Cases were classified into the following subgroups: isolated left ventricular dilatation (early-NICM H-/D+), non-dilated left ventricular cardiomyopathy (early-NICM H+/D-), or early dilated cardiomyopathy (early-NICM H+/D+). Clinical follow-up for major adverse cardiovascular events (MACE) included non-fatal life-threatening arrhythmia, unplanned cardiovascular hospitalization or cardiovascular death. A subset of patients (n = 119) underwent a second CMR to assess changes in cardiac structure and function. Of 254 patients with early-NICM (median age 46 years [interquartile range 36-58], 94 [37%] women, median left ventricular ejection fraction [LVEF] 55% [52-59]), myocardial fibrosis was present in 65 (26%). There was no difference in the prevalence of fibrosis between subgroups (p = 0.90), however fibrosis mass was lowest in early-NICM H-/D+, higher in early-NICM H+/D- and highest in early-NICM H+/D+ (p = 0.03). Over a median follow-up of 7.9 (5.5-10.0) years, 28 patients (11%) experienced MACE. Non-sustained ventricular tachycardia (hazard ratio [HR] 5.1, 95% confidence interval [CI] 2.36-11.00, p < 0.001), myocardial fibrosis (HR 3.77, 95% CI 1.73-8.20, p < 0.001) and diabetes mellitus (HR 5.12, 95% CI 1.73-15.18, p = 0.003) were associated with MACE in a multivariable model. Only 8% of patients progressed from early-NICM to dilated cardiomyopathy with LVEF <50% over a median of 16 (11-34) months. CONCLUSION: Early-NICM is not benign. Fibrosis develops early in the phenotypic course. In-depth characterization enhances risk stratification and might aid clinical management.

The penetrance of rare variants in cardiomyopathy-associated genes: A cross-sectional approach to estimating penetrance for secondary findings.

Understanding the penetrance of pathogenic variants identified as secondary findings (SFs) is of paramount importance with the growing availability of genetic testing. We estimated penetrance through large-scale analyses of individuals referred for diagnostic sequencing for hypertrophic cardiomyopathy (HCM; 10,400 affected individuals, 1,332 variants) and dilated cardiomyopathy (DCM; 2,564 affected individuals, 663 variants), using a cross-sectional approach comparing allele frequencies against reference populations (293,226 participants from UK Biobank and gnomAD). We generated updated prevalence estimates for HCM (1:543) and DCM (1:220). In aggregate, the penetrance by late adulthood of rare, pathogenic variants (23% for HCM, 35% for DCM) and likely pathogenic variants (7% for HCM, 10% for DCM) was substantial for dominant cardiomyopathy (CM). Penetrance was significantly higher for variant subgroups annotated as loss of function or ultra-rare and for males compared to females for variants in HCM-associated genes. We estimated variant-specific penetrance for 316 recurrent variants most likely to be identified as SFs (found in 51% of HCM- and 17% of DCM-affected individuals). 49 variants were observed at least ten times (14% of affected individuals) in HCM-associated genes. Median penetrance was 14.6% (±14.4% SD). We explore estimates of penetrance by age, sex, and ancestry and simulate the impact of including future cohorts. This dataset reports penetrance of individual variants at scale and will inform the management of individuals undergoing genetic screening for SFs. While most variants had low penetrance and the costs and harms of screening are unclear, some individuals with highly penetrant variants may benefit from SFs.

Environmental and genetic predictors of human cardiovascular ageing.

Cardiovascular ageing is a process that begins early in life and leads to a progressive change in structure and decline in function due to accumulated damage across diverse cell types, tissues and organs contributing to multi-morbidity. Damaging biophysical, metabolic and immunological factors exceed endogenous repair mechanisms resulting in a pro-fibrotic state, cellular senescence and end-organ damage, however the genetic architecture of cardiovascular ageing is not known. Here we use machine learning approaches to quantify cardiovascular age from image-derived traits of vascular function, cardiac motion and myocardial fibrosis, as well as conduction traits from electrocardiograms, in 39,559 participants of UK Biobank. Cardiovascular ageing is found to be significantly associated with common or rare variants in genes regulating sarcomere homeostasis, myocardial immunomodulation, and tissue responses to biophysical stress. Ageing is accelerated by cardiometabolic risk factors and we also identify prescribed medications that are potential modifiers of ageing. Through large-scale modelling of ageing across multiple traits our results reveal insights into the mechanisms driving premature cardiovascular ageing and reveal potential molecular targets to attenuate age-related processes.

Ethnicity, consanguinity, and genetic architecture of hypertrophic cardiomyopathy.

AIMS: Hypertrophic cardiomyopathy (HCM) is characterized by phenotypic heterogeneity that is partly explained by the diversity of genetic variants contributing to disease. Accurate interpretation of these variants constitutes a major challenge for diagnosis and implementing precision medicine, especially in understudied populations. The aim is to define the genetic architecture of HCM in North African cohorts with high consanguinity using ancestry-matched cases and controls. METHODS AND RESULTS: Prospective Egyptian patients (n = 514) and controls (n = 400) underwent clinical phenotyping and genetic testing. Rare variants in 13 validated HCM genes were classified according to standard clinical guidelines and compared with a prospective HCM cohort of majority European ancestry (n = 684). A higher prevalence of homozygous variants was observed in Egyptian patients (4.1% vs. 0.1%, P = 2 × 10-7), with variants in the minor HCM genes MYL2, MYL3, and CSRP3 more likely to present in homozygosity than the major genes, suggesting these variants are less penetrant in heterozygosity. Biallelic variants in the recessive HCM gene TRIM63 were detected in 2.1% of patients (five-fold greater than European patients), highlighting the importance of recessive inheritance in consanguineous populations. Finally, rare variants in Egyptian HCM patients were less likely to be classified as (likely) pathogenic compared with Europeans (40.8% vs. 61.6%, P = 1.6 × 10-5) due to the underrepresentation of Middle Eastern populations in current reference resources. This proportion increased to 53.3% after incorporating methods that leverage new ancestry-matched controls presented here. CONCLUSION: Studying consanguineous populations reveals novel insights with relevance to genetic testing and our understanding of the genetic architecture of HCM.