For more than a decade, risk stratification for hypertrophic cardiomyopathy has been enhanced by targeted genetic testing. Using sequencing results, clinicians routinely assess the risk of hypertrophic cardiomyopathy in a patient’s relatives and diagnose the condition in patients who have ambiguous clinical presentations. However, the benefits of genetic testing come with the risk that variants may be misclassified. Using publicly accessible exome data, we identified variants that have previously been considered causal in hypertrophic cardiomyopathy and that are overrepresented in the general population. We studied these variants in diverse populations and reevaluated their initial ascertainments in the medical literature. We reviewed patient records at a leading genetic-testing laboratory for occurrences of these variants during the near-decade-long history of the laboratory. Multiple patients, all of whom were of African or unspecified ancestry, received positive reports, with variants misclassified as pathogenic on the basis of the understanding at the time of testing. Subsequently, all reported variants were recategorized as benign. The mutations that were most common in the general population were significantly more common among black Americans than among white Americans (P<0.001). Simulations showed that the inclusion of even small numbers of black Americans in control cohorts probably would have prevented these misclassifications. We identified methodologic shortcomings that contributed to these errors in the medical literature. The misclassification of benign variants as pathogenic that we found in our study shows the need for sequencing the genomes of diverse populations, both in asymptomatic controls and the tested patient population. These results expand on current guidelines, which recommend the use of ancestry-matched controls to interpret variants. As additional populations of different ancestry backgrounds are sequenced, we expect variant reclassifications to increase, particularly for ancestry groups that have historically been less well studied. (Funded by the National Institutes of Health.)

Cardiomyopathies are a heterogeneous group of diseases with various etiologies. We focused on three genetically determined cardiomyopathies: hypertrophic (HCM), dilated (DCM), and arrhythmogenic right ventricular cardiomyopathy (ARVC). Eighty-four genes have so far been associated with these cardiomyopathies, but the disease-causing effect of reported variants is often dubious. In order to identify possible false-positive variants, we investigated the prevalence of previously reported cardiomyopathy-associated variants in recently published exome data. We searched for reported missense and nonsense variants in the NHLBI-Go Exome Sequencing Project (ESP) containing exome data from 6500 individuals. In ESP, we identified 94 variants out of 687 (14%) variants previously associated with HCM, 58 out of 337 (17%) variants associated with DCM, and 38 variants out of 209 (18%) associated with ARVC. These findings correspond to a genotype prevalence of 1:4 for HCM, 1:6 for DCM, and 1:5 for ARVC. PolyPhen-2 predictions were conducted on all previously published cardiomyopathy-associated missense variants. We found significant overrepresentation of variants predicted as being benign among those present in ESP compared with the ones not present. In order to validate our findings, seven variants associated with cardiomyopathy were genotyped in a control population and this revealed frequencies comparable with the ones found in ESP. In conclusion, we identified genotype prevalences up to more than one thousand times higher than expected from the phenotype prevalences in the general population (HCM 1:500, DCM 1:2500, and ARVC 1:5000) and our data suggest that a high number of these variants are not monogenic causes of cardiomyopathy.

Background—: Atrial fibrillation (AF) affects >30 million individuals worldwide and is associated with an increased risk of stroke, heart failure, and death. AF is highly heritable, yet the genetic basis for the arrhythmia remains incompletely understood. Methods and Results—: To identify new AF-related genes, we used a multifaceted approach, combining large-scale genotyping in 2 ethnically distinct populations, cis-eQTL ( expression quantitative trait loci ) mapping, and functional validation. Four novel loci were identified in individuals of European descent near the genes NEURL (rs12415501; relative risk [RR]=1.18; 95% confidence interval [CI], 1.13–1.23; P =6.5×10 ⁻¹⁶ ), GJA1 (rs13216675; RR=1.10; 95% CI, 1.06–1.14; P =2.2×10 ⁻⁸ ), TBX5 (rs10507248; RR=1.12; 95% CI, 1.08–1.16; P =5.7×10 ⁻¹¹ ), and CAND2 (rs4642101; RR=1.10; 95% CI, 1.06–1.14; P =9.8×10 ⁻⁹ ). In Japanese, novel loci were identified near NEURL (rs6584555; RR=1.32; 95% CI, 1.26–1.39; P =2.0×10 ⁻²⁵ ) and CUX2 (rs6490029; RR=1.12; 95% CI, 1.08–1.16; P =3.9×10 ⁻⁹ ). The top single-nucleotide polymorphisms or their proxies were identified as cis-eQTLs for the genes CAND2 ( P =2.6×10 ⁻¹⁹ ), GJA1 ( P =2.66×10 ⁻⁶ ), and TBX5 ( P =1.36×10 ⁻⁵ ). Knockdown of the zebrafish orthologs of NEURL and CAND2 resulted in prolongation of the atrial action potential duration (17% and 45%, respectively). Conclusions—: We have identified 5 novel loci for AF. Our results expand the diversity of genetic pathways implicated in AF and provide novel molecular targets for future biological and pharmacological investigation.