Interpretation of Incidental Genetic Findings Localizing to Genes Associated With Cardiac Channelopathies and Cardiomyopathies

Predicted Deleterious Variants in Cardiomyopathy Genes Prognosticate Mortality and Composite Outcomes in UK Biobank

BACKGROUND

Inherited cardiomyopathies present with broad variation of phenotype. Data are limited regarding genetic screening strategies and outcomes associated with predicted deleterious variants in cardiomyopathy-associated genes in the general population.

OBJECTIVES

The authors aimed to determine the risk of mortality and composite cardiomyopathy-related outcomes associated with predicted deleterious variants in cardiomyopathy-associated genes in the UK Biobank.

METHODS

Using whole exome sequencing data, variants in dilated, hypertrophic, and arrhythmogenic right ventricular cardiomyopathy-associated genes with at least moderate evidence of disease causality according to ClinGen Expert Panel curations were annotated using REVEL (≥0.65) and ANNOVAR (predicted loss-of-function) considering gene-disease mechanisms. Genotype-positive and genotype-negative groups were compared using time-to-event analyses for the primary (all-cause mortality) and secondary outcomes (diagnosis of cardiomyopathy; composite outcome of diagnosis of cardiomyopathy, heart failure, arrhythmia, stroke, and death).

RESULTS

Among 200,619 participants (age at recruitment 56.46 ± 8.1 years), 5,292 (2.64%) were found to host ≥1 predicted deleterious variants in cardiomyopathy-associated genes (CMP-G+). After adjusting for age and sex, CMP-G+ individuals had higher risk for all-cause mortality (HR: 1.13 [95% CI: 1.01-1.25]; P = 0.027), increased risk for being diagnosed with cardiomyopathy later in life (HR: 5.75 [95% CI: 4.58-7.23]; P < 0.0001), and elevated risk for composite outcome (HR: 1.29 [95% CI: 1.20-1.39]; P < 0.0001) than CMP-G- individuals. The higher risk for being diagnosed with cardiomyopathy and composite outcomes in the genotype-positive subjects remained consistent across all cardiomyopathy subgroups.

CONCLUSIONS

Adults with predicted deleterious variants in cardiomyopathy-associated genes exhibited a slightly higher risk of mortality and a significantly increased risk of developing cardiomyopathy, and cardiomyopathy-related composite outcomes, in comparison with genotype-negative controls.

DiscoVari: A Web-Based Precision Medicine Tool for Predicting Variant Pathogenicity in Cardiomyopathy- and Channelopathy-Associated Genes

BACKGROUND

With genetic testing advancements, the burden of incidentally identified cardiac disease-associated gene variants is rising. These variants may carry a risk of sudden cardiac death, highlighting the need for accurate diagnostic interpretation. We sought to identify pathogenic hotspots in sudden cardiac death-associated genes using amino acid-level signal-to-noise (S:N) analysis and develop a web-based precision medicine tool, DiscoVari, to improve variant evaluation.

METHODS

The minor allele frequency of putatively pathogenic variants was derived from cohort-based cardiomyopathy and channelopathy studies in the literature. We normalized disease-associated minor allele frequencies to rare variants in an ostensibly healthy population (Genome Aggregation Database) to calculate amino acid-level S:N. Amino acids with S:N above the gene-specific threshold were defined as hotspots. DiscoVari was built using JavaScript ES6 and using open-source JavaScript library ReactJS, web development framework Next.js, and JavaScript runtime NodeJS. We validated the ability of DiscoVari to identify pathogenic variants using variants from ClinVar and individuals clinically evaluated at the Duke University Hospitals with cardiac genetic testing.

RESULTS

We developed DiscoVari as an internet-based tool for S:N-based variant hotspots. Upon validation, a higher proportion of ClinVar likely pathogenic/pathogenic variants localized to DiscoVari hotspots (43.1%) than likely benign/benign variants (17.8%; P<0.0001). Further, 75.3% of ClinVar variants reclassified to likely pathogenic/pathogenic were in hotspots, compared with 41.3% of those reclassified as variants of uncertain significance (P<0.0001) and 23.4% of those reclassified as likely benign/benign (P<0.0001). Of the clinical cohort variants, 73.1% of likely pathogenic/pathogenic were in hotspots, compared with 0.0% of likely benign/benign (P<0.01).

CONCLUSIONS

DiscoVari reliably identifies disease-susceptible amino acid residues to evaluate variants by searching amino acid-specific S:N ratios.

Should young athletes be screened for cardiomyopathies to reduce the burden of sudden cardiac death in athletes?

In this correspondence, we highlight the risk of sudden cardiac death associated with undiagnosed cardiomyopathies. Life-threatening arrhythmias, which underlie sudden cardiac death, can be triggered by high-intensity exercise. It raises the question whether, and if so, how athletes should be screened for cardiomyopathies. The example of practice from Italy is discussed. We also briefly discuss novel developments, such as wearable biosensors and machine learning, which could be applied to screening for cardiomyopathies in future.

From diagnostic testing to precision medicine: the evolving role of genomics in cardiac channelopathies and cardiomyopathies in children

Pediatric sudden cardiac death (SCD) is the sudden unexpected death of a child or adolescent due to a presumed cardiac etiology. Heritable causes of pediatric SCD are predominantly cardiomyopathies and cardiac ion channelopathies. This review illustrates recent advances in determining the genetic cause of established and emerging channelopathies and cardiomyopathies, and how broader genomic sequencing is uncovering complex interactions between genetic architecture and disease manifestation. We discuss innovative models and experimental platforms for resolving the variant of uncertain significance as both the variants and genes associated with disease continue to evolve. Finally, we highlight the growing problem of incidentally identified variants in cardiovascular disease-causing genes and review innovative methods to determining whether these variants may ultimately result in penetrant disease. Overall, we seek to illustrate both the promise and inherent challenges in bridging the traditional role for genetics in diagnosing cardiomyopathies and channelopathies to one of true risk-predictive precision medicine.

Determining the Likelihood of Disease Pathogenicity Among Incidentally Identified Genetic Variants in Rare Dilated Cardiomyopathy-Associated Genes

Background As utilization of clinical exome sequencing (ES) has expanded, criteria for evaluating the diagnostic weight of incidentally identified variants are critical to guide clinicians and researchers. This is particularly important in genes associated with dilated cardiomyopathy (DCM), which can cause heart failure and sudden death. We sought to compare the frequency and distribution of incidentally identified variants in DCM-associated genes between a clinical referral cohort with those in control and known case cohorts to determine the likelihood of pathogenicity among those undergoing genetic testing for non-DCM indications. Methods and Results A total of 39 rare, non-TTN DCM-associated genes were identified and evaluated from a clinical ES testing referral cohort (n=14 005, Baylor Genetic Laboratories) and compared with a DCM case cohort (n=9442) as well as a control cohort of population variants (n=141 456) derived from the gnomAD database. Variant frequencies in each cohort were compared. Signal-to-noise ratios were calculated comparing the DCM and ES cohort with the gnomAD cohort. The likely pathogenic/pathogenic variant yield in the DCM cohort (8.2%) was significantly higher than in the ES cohort (1.9%). Based on signal-to-noise and correlation analysis, incidental variants found in FLNC, RBM20, MYH6, DSP, ABCC9, JPH2, and NEXN had the greatest chance of being DCM-associated. Conclusions The distribution of pathogenic variants between the ES cohort and the DCM case cohort was gene specific, and variants found in the ES cohort were similar to variants found in the control cohort. Incidentally identified variants in specific genes are more associated with DCM than others.

Signal-to-Noise Analysis Can Inform the Likelihood That Incidentally Identified Variants in Sarcomeric Genes Are Associated with Pediatric Cardiomyopathy

Background: Hypertrophic cardiomyopathy (HCM) is the most common heritable cardiomyopathy and can predispose individuals to sudden death. Most pediatric HCM patients host a known pathogenic variant in a sarcomeric gene. With the increase in exome sequencing (ES) in clinical settings, incidental variants in HCM-associated genes are being identified more frequently. Diagnostic interpretation of incidental variants is crucial to enhance clinical patient management. We sought to use amino acid-level signal-to-noise (S:N) analysis to establish pathogenic hotspots in sarcomeric HCM-associated genes as well as to refine the 2015 American College of Medical Genetics (ACMG) criteria to predict incidental variant pathogenicity. Methods and Results: Incidental variants in HCM genes (MYBPC3, MYH7, MYL2, MYL3, ACTC1, TPM1, TNNT2, TNNI3, and TNNC1) were obtained from a clinical ES referral database (Baylor Genetics) and compared to rare population variants (gnomAD) and variants from HCM literature cohort studies. A subset of the ES cohort was clinically evaluated at Texas Children’s Hospital. We compared the frequency of ES and HCM variants at specific amino acid locations in coding regions to rare variants (MAF < 0.0001) in gnomAD. S:N ratios were calculated at the gene- and amino acid-level to identify pathogenic hotspots. ES cohort variants were re-classified using ACMG criteria with S:N analysis as a correlate for PM1 criteria, which reduced the burden of variants of uncertain significance. In the clinical validation cohort, the majority of probands with cardiomyopathy or family history hosted likely pathogenic or pathogenic variants. Conclusions: Incidental variants in HCM-associated genes were common among clinical ES referrals, although the majority were not disease-associated. Leveraging amino acid-level S:N as a clinical tool may improve the diagnostic discriminatory ability of ACMG criteria by identifying pathogenic hotspots.