HomeCirculation: Genomic and Precision MedicineAhead of PrintReal-World Genetic Testing Utilization Among Patients With Cardiomyopathy No AccessResearch ArticleRequest AccessAboutView PDFSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toNo AccessResearch ArticleRequest AccessReal-World Genetic Testing Utilization Among Patients With Cardiomyopathy Ana Morales, Chad Moretz, Sheng Ren, Elizabeth Smith, Thomas E. Callis, Taryn Hall, Kathryn E. Hatchell, Robert L. Nussbaum, Ellen Regalado, Susan Rojahn, Matteo Vatta, Edward D. Esplin and Jaime Murillo Ana MoralesAna Morales https://orcid.org/0000-0002-5882-1341 Invitae Corporation, San Francisco, CA (A.M., C.M., T.E.C., K.E.H., R.L.N., E.R., S. Rojahn, M.V., E.D.E.). , Chad MoretzChad Moretz https://orcid.org/0000-0003-2255-4330 Invitae Corporation, San Francisco, CA (A.M., C.M., T.E.C., K.E.H., R.L.N., E.R., S. Rojahn, M.V., E.D.E.). , Sheng RenSheng Ren Optum Labs, Eden Prairie, MN (S. Ren, E.S., T.H., J.M.). , Elizabeth SmithElizabeth Smith https://orcid.org/0000-0002-0239-5021 Optum Labs, Eden Prairie, MN (S. Ren, E.S., T.H., J.M.). , Thomas E. CallisThomas E. Callis https://orcid.org/0000-0001-7670-5443 Invitae Corporation, San Francisco, CA (A.M., C.M., T.E.C., K.E.H., R.L.N., E.R., S. Rojahn, M.V., E.D.E.). , Taryn HallTaryn Hall https://orcid.org/0000-0001-6018-8613 Optum Labs, Eden Prairie, MN (S. Ren, E.S., T.H., J.M.). , Kathryn E. HatchellKathryn E. Hatchell https://orcid.org/0000-0003-0849-7018 Invitae Corporation, San Francisco, CA (A.M., C.M., T.E.C., K.E.H., R.L.N., E.R., S. Rojahn, M.V., E.D.E.). , Robert L. NussbaumRobert L. Nussbaum https://orcid.org/0000-0003-3445-8880 Invitae Corporation, San Francisco, CA (A.M., C.M., T.E.C., K.E.H., R.L.N., E.R., S. Rojahn, M.V., E.D.E.). , Ellen RegaladoEllen Regalado https://orcid.org/0000-0002-5859-218X Invitae Corporation, San Francisco, CA (A.M., C.M., T.E.C., K.E.H., R.L.N., E.R., S. Rojahn, M.V., E.D.E.). , Susan RojahnSusan Rojahn https://orcid.org/0000-0001-5888-7693 Invitae Corporation, San Francisco, CA (A.M., C.M., T.E.C., K.E.H., R.L.N., E.R., S. Rojahn, M.V., E.D.E.). , Matteo VattaMatteo Vatta https://orcid.org/0000-0002-1742-7719 Invitae Corporation, San Francisco, CA (A.M., C.M., T.E.C., K.E.H., R.L.N., E.R., S. Rojahn, M.V., E.D.E.). , Edward D. EsplinEdward D. Esplin https://orcid.org/0000-0001-9205-3756 Invitae Corporation, San Francisco, CA (A.M., C.M., T.E.C., K.E.H., R.L.N., E.R., S. Rojahn, M.V., E.D.E.). and Jaime MurilloJaime Murillo Correspondence to: Jaime Murillo, MD, Optum Labs, 11000 Optum Cir, Eden Prairie, MN 55344. Email E-mail Address: [email protected] https://orcid.org/0000-0003-2439-8685 Optum Labs, Eden Prairie, MN (S. Ren, E.S., T.H., J.M.). Originally published13 Dec 2023https://doi.org/10.1161/CIRCGEN.122.004028Circulation: Genomic and Precision Medicine. 2023;0:e004028FootnotesFor Sources of Funding and Disclosures, see page xxx.Correspondence to: Jaime Murillo, MD, Optum Labs, 11000 Optum Cir, Eden Prairie, MN 55344. Email jaime_murillo@uhg.com eLetters(0) eLetters should relate to an article recently published in the journal and are not a forum for providing unpublished data. Comments are reviewed for appropriate use of tone and language. Comments are not peer-reviewed. Acceptable comments are posted to the journal website only. Comments are not published in an issue and are not indexed in PubMed. Comments should be no longer than 500 words and will only be posted online. References are limited to 10. Authors of the article cited in the comment will be invited to reply, as appropriate. Comments and feedback on AHA/ASA Scientific Statements and Guidelines should be directed to the AHA/ASA Manuscript Oversight Committee via its Correspondence page. Sign In to Submit a Response to This Article Previous Back to top Next FiguresReferencesRelatedDetails Advertisement Article Information Metrics © 2023 American Heart Association, Inc.https://doi.org/10.1161/CIRCGEN.122.004028PMID: 38088168 Originally publishedDecember 13, 2023 Keywordsadultcardiomyopathiesgenetic testinghumansmolecular diagnosisPDF download Advertisement Subjects Genetic, Association Studies

Importance:Genetic testing can guide management of both cardiomyopathies and arrhythmias, but cost, yield, and uncertain results can be barriers to its use. It is unknown whether combined disease testing can improve diagnostic yield and clinical utility for patients with a suspected genetic cardiomyopathy or arrhythmia. Objective:To evaluate the diagnostic yield and clinical management implications of combined cardiomyopathy and arrhythmia genetic testing through a no-charge, sponsored program for patients with a suspected genetic cardiomyopathy or arrhythmia. Design, Setting, and Participants:This cohort study involved a retrospective review of DNA sequencing results for cardiomyopathy- and arrhythmia-associated genes. The study included 4782 patients with a suspected genetic cardiomyopathy or arrhythmia who were referred for genetic testing by 1203 clinicians; all patients participated in a no-charge, sponsored genetic testing program for cases of suspected genetic cardiomyopathy and arrhythmia at a single testing site from July 12, 2019, through July 9, 2020. Main Outcomes and Measures:Positive gene findings from combined cardiomyopathy and arrhythmia testing were compared with findings from smaller subtype-specific gene panels and clinician-provided diagnoses. Results:Among 4782 patients (mean [SD] age, 40.5 [21.3] years; 2551 male [53.3%]) who received genetic testing, 39 patients (0.8%) were Ashkenazi Jewish, 113 (2.4%) were Asian, 571 (11.9%) were Black or African American, 375 (7.8%) were Hispanic, 2866 (59.9%) were White, 240 (5.0%) were of multiple races and/or ethnicities, 138 (2.9%) were of other races and/or ethnicities, and 440 (9.2%) were of unknown race and/or ethnicity. A positive result (molecular diagnosis) was confirmed in 954 of 4782 patients (19.9%). Of those, 630 patients with positive results (66.0%) had the potential to inform clinical management associated with adverse clinical outcomes, increased arrhythmia risk, or targeted therapies. Combined cardiomyopathy and arrhythmia gene panel testing identified clinically relevant variants for 1 in 5 patients suspected of having a genetic cardiomyopathy or arrhythmia. If only patients with a high suspicion of genetic cardiomyopathy or arrhythmia had been tested, at least 137 positive results (14.4%) would have been missed. If testing had been restricted to panels associated with the clinician-provided diagnostic indications, 75 of 689 positive results (10.9%) would have been missed; 27 of 75 findings (36.0%) gained through combined testing involved a cardiomyopathy indication with an arrhythmia genetic finding or vice versa. Cascade testing of family members yielded 402 of 958 positive results (42.0%). Overall, 2446 of 4782 patients (51.2%) had only variants of uncertain significance. Patients referred for arrhythmogenic cardiomyopathy had the lowest rate of variants of uncertain significance (81 of 176 patients [46.0%]), and patients referred for catecholaminergic polymorphic ventricular tachycardia had the highest rate (48 of 76 patients [63.2%]). Conclusions and Relevance:In this study, comprehensive genetic testing for cardiomyopathies and arrhythmias revealed diagnoses that would have been missed by disease-specific testing. In addition, comprehensive testing provided diagnostic and prognostic information that could have potentially changed management and monitoring strategies for patients and their family members. These results suggest that this improved diagnostic yield may outweigh the burden of uncertain results.

Background Pathogenic variation in the ATP1A3 ‐encoded sodium‐potassium ATPase, ATP1A3, is responsible for alternating hemiplegia of childhood (AHC). Although these patients experience a high rate of sudden unexpected death in epilepsy, the pathophysiologic basis for this risk remains unknown. The objective was to determine the role of ATP1A3 genetic variants on cardiac outcomes as determined by QT and corrected QT (QTc) measurements. Methods and Results We analyzed 12‐lead ECG recordings from 62 patients (male subjects=31, female subjects=31) referred for AHC evaluation. Patients were grouped according to AHC presentation (typical versus atypical), ATP1A3 variant status (positive versus negative), and ATP1A3 variant (D801N versus other variants). Manual remeasurements of QT intervals and QTc calculations were performed by 2 pediatric electrophysiologists. QTc measurements were significantly shorter in patients with positive ATP1A3 variant status ( P <0.001) than in patients with genotype‐negative status, and significantly shorter in patients with the ATP1A3‐D801N variant than patients with other variants ( P <0.001). The mean QTc for ATP1A3‐D801N was 344.9 milliseconds, which varied little with age, and remained <370 milliseconds throughout adulthood. ATP1A3 genotype status was significantly associated with shortened QTc by multivariant regression analysis. Two patients with the ATP1A3‐D801N variant experienced ventricular fibrillation, resulting in death in 1 patient. Rare variants in ATP1A3 were identified in a large cohort of genotype‐negative patients referred for arrhythmia and sudden unexplained death. Conclusions Patients with AHC who carry the ATP1A3‐D801N variant have significantly shorter QTc intervals and an increased likelihood of experiencing bradycardia associated with life‐threatening arrhythmias. ATP1A3 variants may represent an independent cause of sudden unexplained death. Patients with AHC should be evaluated to identify risk of sudden death.

HomeCirculation: Genomic and Precision MedicineVol. 14, No. 1Common Variants in KCNE1, KCNH2, and SCN5A May Impact Cardiac Arrhythmia Risk Free AccessLetterPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyRedditDiggEmail Jump toFree AccessLetterPDF/EPUBCommon Variants in KCNE1, KCNH2, and SCN5A May Impact Cardiac Arrhythmia Risk Matteo Vatta, PhD, Rebecca Truty, PhD, John Garcia, PhD, Thomas E. Callis, PhD, Kathryn Hatchell, PhD, Susan Rojahn, PhD, Ana Morales, MS, Swaroop Aradhya, PhD and Robert Nussbaum, MD Matteo VattaMatteo Vatta Correspondence to: Matteo Vatta, Invitae, 1400 16th St, San Francisco, CA 94103, Tel: 415-231-6976, Email: E-mail Address: [email protected] https://orcid.org/0000-0002-1742-7719 Invitae, San Francisco, CA. Search for more papers by this author , Rebecca TrutyRebecca Truty https://orcid.org/0000-0001-9035-9639 Invitae, San Francisco, CA. Search for more papers by this author , John GarciaJohn Garcia https://orcid.org/0000-0001-6631-4174 Invitae, San Francisco, CA. Search for more papers by this author , Thomas E. CallisThomas E. Callis https://orcid.org/0000-0001-7670-5443 Invitae, San Francisco, CA. Search for more papers by this author , Kathryn HatchellKathryn Hatchell https://orcid.org/0000-0003-0849-7018 Invitae, San Francisco, CA. Search for more papers by this author , Susan RojahnSusan Rojahn https://orcid.org/0000-0001-5888-7693 Invitae, San Francisco, CA. Search for more papers by this author , Ana MoralesAna Morales https://orcid.org/0000-0002-5882-1341 Invitae, San Francisco, CA. Search for more papers by this author , Swaroop AradhyaSwaroop Aradhya https://orcid.org/0000-0001-6219-2931 Invitae, San Francisco, CA. Search for more papers by this author and Robert NussbaumRobert Nussbaum https://orcid.org/0000-0003-3445-8880 Invitae, San Francisco, CA. Search for more papers by this author Originally published1 Feb 2021https://doi.org/10.1161/CIRCGEN.120.003206Circulation: Genomic and Precision Medicine. 2021;14:e003206Other version(s) of this articleYou are viewing the most recent version of this article. Previous versions: February 1, 2021: Ahead of Print Genetic testing for long QT syndrome (LQTS) and other inherited arrhythmias can guide diagnosis and clinical management, but some variants suspected to affect arrhythmia risk are underreported almost exclusively due to their high population frequency.1,2 As reviewed by Giudicessi et al,1 over a dozen common variants in ion channel genes KCNE1, KCNE2, KCNH2, KCNQ1, and SCN5A have substantial epidemiological and functional evidence supporting their role as independent risk alleles, genetic modifiers, or exposure-dependent pathogenic variants for arrhythmia. However, the prevalence of these provisional risk-modifying variants exceeds the prevalence of LQTS, arguing against their classification as pathogenic or likely pathogenic per current variant interpretation guidelines.3 Large case-control studies showing significant differences in the prevalence of these variants in affected and unaffected populations could strengthen arguments for including them in diagnostic reports.Here, we conducted a retrospective review of next generation sequencing data to evaluate the allele frequencies of 17 provisional arrhythmia risk-modifying variants (Table) among individuals referred for diagnostic genetic testing on a LQTS gene panel (N=3148) and controls referred for genetic testing on other gene panels (N>225 000). The variant list was based on prior literature, especially Giudicessi et al.1 Samples were accessed June 2013 to February 2020. Sequencing and variant classification were as previously described, with an average of 350× read coverage (minimum 50×).4,5 Among the panel-tested cohort, 681 individuals (21.6%) had a pathogenic or likely pathogenic finding in a LQTS panel gene and were considered panel positive; the remaining 2467 panel-negative individuals were of particular interest for evaluating the provisional risk-modifying variants.Table. Allelic ORs of Provisional Arrhythmia Risk-Modifying Alleles Among Individuals Suspected to Have a Genetic ArrhythmiaVariantgnomAD MAFCohortOR (95% CI)P valueKCNE1 p.Asp85Asn0.0093LQTS panel tested2.57* (2.18–3.01)9.3×10−24Panel positive1.63 (1.03–2.46)0.53Panel negative2.83* (2.37–3.35)2.0×10−24KCNH2 p.Arg176Trp0.0004LQTS panel tested10.23* (6.15–16.22)5.8×10−13Panel positive9.01* (2.42–23.6)0.021Panel negative10.57* (5.99–17.52)7.8×10−11KCNH2 p.Lys897Thr0.1818LQTS panel tested0.85* (0.80–0.91)2.8×10−5Panel positive0.85 (0.73–0.98)0.37Panel negative0.85* (0.79–0.92)4.7×10−4SCN5A p.His558Arg0.2231LQTS panel tested1.12* (1.05–1.18)0.0031Panel positive1.28* (1.13–1.44)0.0013Panel negative1.07 (1.01–1.15)0.53KCNE1 p.Ser38Gly0.6445No significant findingsKCNE2 p.Thr8Ala0.0037No significant findingsKCNE2 p.Gln9Glu0.0015No significant findingsKCNE2 p.Ile57Thr0.0010No significant findingsKCNH2 p.Arg1047Leu0.0180No significant findingsKCNQ1 p.Gly643Ser0.0063No significant findingsSCN5A p.Arg481Trp0.0013No significant findingsSCN5A p.Ser524Tyr0.0042No significant findingsSCN5A p.Pro1090Leu0.0017No significant findingsSCN5A p.Ser1103Tyr0.0077No significant findingsSCN5A p.Arg1193Gln0.0052No significant findingsSCN5A p.Ser1787Asn0.0008No significant findingsSCN5A p.Pro2006Ala0.0011No significant findingsgnomAD MAF: minor allele frequency as assessed in the Genome Aggregation Database, version 2.1.1, https://gnomad.broadinstitute.org. LQTS panel tested refers to individuals tested on Invitae’s LQTS panel of 13 arrhythmia-associated genes (ANK2, CACNA1C, CALM1, CALM2, CALM3, CAV3, KCNE1, KCNE2, KCNH2, KCNJ2, KCNQ1, SCN5A, and TRDN). Panel positive refers to individuals with a pathogenic or likely pathogenic variant on that panel; panel negative refers to individuals lacking such findings. The size of the control cohort was 225,914 individuals for KCNE1 and KCNE2 variants and 272,470 individuals for KCNH2, KCNQ1, and SCN5A variants.LQTS indicates long QT syndrome; and OR, odds ratio.* Statistically significant finding; significance was determined with Bonferroni correction for 17 different variants and set at P<0.05.Data were deidentified and approved for use in this study by an independent institutional review board (Western Institutional Review Board no. 20161796). Odds ratios with 95% CIs were calculated among panel-tested individuals, panel-positive individuals, and panel-negative individuals, compared with controls. The data that support the findings of this study are available from the corresponding author upon reasonable request. Statistical significance was determined with Bonferroni correction for 17 different variants and set at P<0.05.The minor allele frequencies of the provisional risk-modifying variants ranged from 0.0004 to 0.6445 (Table); across all individuals in the study, about 59% who had ≥1 variant had multiple variants. Four of the 17 variants had significant findings among the LQTS panel-tested groups (Table). KCNE1 p.Asp85Asn and KCNH2 p.Arg176Trp were significantly enriched among panel-tested individuals, especially panel-negative individuals. Conversely, KCNH2 p.Lys897Thr was significantly depleted among panel-tested and panel-negative individuals. We also observed a significant enrichment of SCN5A p.His558Arg among panel-positive individuals, of whom 48.5% harbored the variant.We also stratified our analyses by self-reported ancestral backgrounds of Ashkenazi Jewish, Asian, Black, Hispanic, and White. Significant results for KCNE1 p.Asp85Asn, KCNH2 p.Arg176Trp, and KCNH2 p.Lys897Thr persisted only for the White group. Stratification by ancestry yielded no significant findings for SCN5A p.His558Arg.These findings add weight to a growing consensus that limiting clinically actionable findings to rare variants may sometimes limit the clinical utility of genetic testing. We show that 2 common variants in LQTS-genes, KCNE1 p.Asp85Asn and KCNH2 p.Arg176Trp, are enriched in patients referred for diagnostic LQTS gene testing but lacking a molecular diagnosis. Therefore, these variants may increase the risk of arrhythmia under certain exposures, such as exercise or medication. As these common alleles are enriched with odds ratios >2 among LQTS panel-tested and panel-negative individuals, we suggest their clinical effects should be reassessed and integrated into clinical guidelines. In contrast, KCNH2 p.Lys897Thr was under-represented in individuals suspected to have a genetic arrhythmia and, therefore, may be a protective allele that reduces risk in unaffected individuals. Finally, SCN5A p.His558Arg was enriched in individuals with a separate positive LQTS panel finding and thus may modify the effects of other variants.Although statistical significance was observed only for White individuals after ancestry stratification, we suspect that this is due to limited statistical power among the other groups who represented only 0.3% to 11% of the panel-tested cohort. The variants studied are likely also important for other groups indeed, for other groups we observed enrichment and depletion trends that did not reach significance. Definitive conclusions for these other groups await more substantial data. In addition, this study is limited by the paucity of clinical information, including ECG data or history of syncope, and a lack of whole-genome sequence data.In conclusion, our large cohort study bolsters proposals that common variants in LQTS-associated genes modify the risk of cardiac arrhythmia events. Reassessment of guidelines and recommendations related to their clinical interpretation may be warranted. Broadly, the prevailing focus solely on rare variants as the source of hereditary disease may be limiting the utility of genetic testing for inherited arrhythmia and other genetic diseases.Sources of FundingNone.Disclosures All authors are employees and stockholders of Invitae.FootnotesFor Sources of Funding and Disclosures, see page 139.Correspondence to: Matteo Vatta, Invitae, 1400 16th St, San Francisco, CA 94103, Tel: 415-231-6976, Email: matteo.[email protected]comReferences1. Giudicessi JR, Roden DM, Wilde AAM, Ackerman MJ. Classification and reporting of potentially proarrhythmic common genetic variation in long QT syndrome genetic testing.Circulation. 2018; 137:619–630. doi: 10.1161/CIRCULATIONAHA.117.030142LinkGoogle Scholar2. Tonchev I, Luria D, Orenstein D, Lotan C, Biton Y. For whom the bell tolls: refining risk assessment for sudden cardiac death.Curr Cardiol Rep. 2019; 21:106. doi: 10.1007/s11886-019-1191-zCrossrefMedlineGoogle Scholar3. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hegde M, Lyon E, Spector E, et al.; ACMG Laboratory Quality Assurance Committee. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.Genet Med. 2015; 17:405–424. doi: 10.1038/gim.2015.30CrossrefMedlineGoogle Scholar4. Lincoln SE, Kobayashi Y, Anderson MJ, Yang S, Desmond AJ, Mills MA, Nilsen GB, Jacobs KB, Monzon FA, Kurian AW, et al.. A systematic comparison of traditional and multigene panel testing for hereditary breast and ovarian cancer genes in more than 1000 patients.J Mol Diagn. 2015; 17:533–544. doi: 10.1016/j.jmoldx.2015.04.009CrossrefMedlineGoogle Scholar5. Nykamp K, Anderson M, Powers M, Garcia J, Herrera B, Ho YY, Kobayashi Y, Patil N, Thusberg J, Westbrook M, et al.; Invitae Clinical Genomics Group. Sherloc: a comprehensive refinement of the ACMG-AMP variant classification criteria.Genet Med. 2017; 19:1105–1117. doi: 10.1038/gim.2017.37CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetails February 2021Vol 14, Issue 1Article InformationMetrics © 2021 American Heart Association, Inc.https://doi.org/10.1161/CIRCGEN.120.003206PMID: 33517668 Originally publishedFebruary 1, 2021 Keywordsgenetic testingallelelong QT syndromedeathprevalencePDF download Advertisement SubjectsArrhythmiasGenetic, Association StudiesPrecision MedicineSudden Cardiac Death

IMPORTANCE Familial hypercholesterolemia (FH) is the most common inherited cardiovascular disease and carries significant morbidity and mortality risks. Genetic testing can identify affected individuals, but some array-based assays screen only a small subset of known pathogenic variants. OBJECTIVE To identify the number of clinically significant variants associated with FH that would be missed by an array-based, limited-variant screen when compared with next-generation sequencing (NGS)-based comprehensive testing. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study compared comprehensive genetic test results for clinically significant variants associated with FH with results for a subset of 24 variants screened by a limited-variant array. Data were deidentified next-generation sequencing results from indication-based or proactive gene panels. Individuals receiving next-generation sequencing-based genetic testing, either for an FH indication between November 2015 and June 2020 or as proactive health screening between February 2016 and June 2020 were included. Ancestry was reported by clinicians who could select from preset options or enter free text on the test requisition form. MAIN OUTCOMES AND MEASURES Number of pathogenic or likely pathogenic (P/LP) variants identified. RESULTS This study included 4563 individuals who were referred for FH diagnostic testing and 6482 individuals who received next-generation sequencing of FH-associated genes as part of a proactive genetic test. Among individuals in the indication cohort, the median (interquartile range) age at testing was 49 (32-61) years, 55.4%(2528 of 4563) were female, and 63.6%(2902 of 4563) were self-reported White/Caucasian. In the indication cohort, the positive detection rate would have been 8.4%(382 of 4563) for a limited-variant screen compared with the 27.0%(1230 of 4563) observed with the next-generation sequencing-based comprehensive test. As a result, 68.9% (848 of 1230) of individuals with a P/LP finding in an FH-associated gene would have been missed by the limited screen. The potential for missed findings in the indication cohort varied by ancestry; among individuals with a P/LP finding, 93.7%(59 of 63) of self-reported Black/African American individuals and 84.7%(122 of 144) of Hispanic individuals would have been missed by the limited-variant screen, compared with 33.3%(4 of 12) of Ashkenazi Jewish individuals. In the proactive cohort, the prevalence of clinically significant FH variants was approximately 1:191 per the comprehensive test, and 61.8%(21 of 34) of individuals with an FH-associated P/LP finding would have been missed by a limited-variant screen. CONCLUSIONS AND RELEVANCE Limited-variant screens may falsely reassure the majority of individuals at risk for FH that they do not carry a disease-causing variant, especially individuals of self-reported Black/African American and Hispanic ancestry.

To reduce barriers to genetic testing and help implement existing guidelines, we initiated a program of counselling-supported, sponsored no-cost genetic testing for patients suspected of having a genetic arrhythmia or cardiomyopathy. Here, we describe the variant prevalence and clinical utility of testing. With ethics approval, we reviewed de-identified clinical data from individuals referred for genetic testing through the sponsored Detect Arrhythmia and Cardiomyopathy program. A comprehensive cardiomyopathy and arrhythmia panel of up to 150 genes analysing single nucleotide variants, indels and copy number variants was used. During the first year, ∼1,203 clinicians, from a range of medical specialties, referred patients for genetic testing for cardiomyopathies and arrhythmias. 954 of 4,782 patients (19.9%) received a molecular diagnosis, ranging from 3.9% to 26.1% depending on the referral indication. The molecular diagnosis for 10.9% (75/689) of patients was unexpected from the clinician-provided diagnosis and would have been missed using narrow, condition-specific panels. ∼3.1 family members per family (958/306) received cascade testing following identification of a positive proband with 42.1% (403/958 receiving positive results. 9.5% (18/190) of post mortem tests were positive, thereby identifying at-risk family members. Discovery of actionable variants may guide implementation of established management recommendations, enable identification of at-risk family members, and confer eligibility for gene-specific precision therapies.

Genetic evaluation is recommended to improve the diagnosis and management of cardiomyopathy patients and family members. The number of genetic tests is growing rapidly and includes FDA-authorized direct-to-consumer (DTC) tests and hybrid models where consumers order laboratory-developed tests (LDTs

Purpose To date, there has not been a large, systematic evaluation of the prevalence of germline risk variants in urothelial carcinoma (UC). Methods We evaluated the frequency of germline pathogenic and likely pathogenic variants in 1038 patients with high-risk UC who underwent targeted clinical germline testing. Case–control enrichment analysis was performed to screen for pathogenic variant enrichment in 17 DNA repair genes in 1038 UC patients relative to cancer-free individuals. Results Among 1038 patients with UC, the cumulative frequency of patients with pathogenic variants was 24%; 18.6% of patients harbored ≥1 actionable germline variant with preventive or therapeutic utility. MSH2 (34/969, 3.5%) and BRCA1/2 (38/867, 4.4%) germline variants had the highest frequency. Germline variants in DNA damage repair genes accounted for 78% of pathogenic germline variants. Compared to the cancer-free cohort, UC patients had significant variant enrichment in MSH2 (odds ratio [OR]: 15.4, 95% confidence interval [CI]: 7.1–32.7, p < 0.0001), MLH1 (OR: 15.9, 95% CI: 4.4–67.7, p < 0.0001), BRCA2 (OR: 5.7, 95% CI: 3.2–9.6, p < 0.0001), and ATM (OR: 3.8, 95% CI: 1.8–8.3, p = 0.02). Conclusion In this study, 24% of UC patients harbored pathogenic germline variants and 18.6% had clinically actionable variants. MLH1 and MSH2 were validated as UC risk genes while ATM and BRCA2 were highlighted as potential UC predisposition genes. This work emphasizes the utility of germline testing in selected high-risk UC cohorts.

Background: Pathogenic RYR2 variants account for ≈60% of clinically definite cases of catecholaminergic polymorphic ventricular tachycardia. However, the rate of rare benign RYR2 variants identified in the general population remains a challenge for genetic test interpretation. Therefore, we examined the results of the RYR2 genetic test among patients referred for commercial genetic testing and examined factors impacting variant interpretability. Methods: Frequency and location comparisons were made for RYR2 variants identified among 1355 total patients of varying clinical certainty and 60 706 Exome Aggregation Consortium controls. The impact of the clinical phenotype on the yield of RYR2 variants was examined. Six in silico tools were assessed using patient- and control-derived variants. Results: A total of 18.2% (218/1200) of patients referred for commercial testing hosted rare RYR2 variants, statistically less than the 59% (46/78) yield among clinically definite cases, resulting in a much higher potential genetic false discovery rate among referrals considering the 3.2% background rate of rare, benign RYR2 variants. Exclusion of clearly putative pathogenic variants further complicates the interpretation of the next novel RYR2 variant. Exonic/topologic analyses revealed overrepresentation of patient variants in exons covering only one third of the protein. In silico tools largely failed to show evidence toward enhancement of variant interpretation. Conclusions: Current expert recommendations have resulted in increased use of RYR2 genetic testing in patients with questionable clinical phenotypes. Using the largest to date catecholaminergic polymorphic ventricular tachycardia patient versus control comparison, this study highlights important variables in the interpretation of variants to overcome the 3.2% background rate that confounds RYR2 variant interpretation.

IMPORTANCE:Large-scale DNA sequencing identifies incidental rare variants in established Mendelian disease genes, but the frequency of related clinical phenotypes in unselected patient populations is not well established. Phenotype data from electronic medical records (EMRs) may provide a resource to assess the clinical relevance of rare variants.OBJECTIVE:To determine the clinical phenotypes from EMRs for individuals with variants designated as pathogenic by expert review in arrhythmia susceptibility genes.DESIGN, SETTING, AND PARTICIPANTS:This prospective cohort study included 2022 individuals recruited for nonantiarrhythmic drug exposure phenotypes from October 5, 2012, to September 30, 2013, for the Electronic Medical Records and Genomics Network Pharmacogenomics project from 7 US academic medical centers. Variants in SCN5A and KCNH2, disease genes for long QT and Brugada syndromes, were assessed for potential pathogenicity by 3 laboratories with ion channel expertise and by comparison with the ClinVar database. Relevant phenotypes were determined from EMRs, with data available from 2002 (or earlier for some sites) through September 10, 2014.EXPOSURES:One or more variants designated as pathogenic in SCN5A or KCNH2.MAIN OUTCOMES AND MEASURES:Arrhythmia or electrocardiographic (ECG) phenotypes defined by International Classification of Diseases, Ninth Revision (ICD-9) codes, ECG data, and manual EMR review.RESULTS:Among 2022 study participants (median age, 61 years [interquartile range, 56-65 years]; 1118 [55%] female; 1491 [74%] white), a total of 122 rare (minor allele frequency <0.5%) nonsynonymous and splice-site variants in 2 arrhythmia susceptibility genes were identified in 223 individuals (11% of the study cohort). Forty-two variants in 63 participants were designated potentially pathogenic by at least 1 laboratory or ClinVar, with low concordance across laboratories (Cohen κ = 0.26). An ICD-9 code for arrhythmia was found in 11 of 63 (17%) variant carriers vs 264 of 1959 (13%) of those without variants (difference, +4%; 95% CI, -5% to +13%; P = .35). In the 1270 (63%) with ECGs, corrected QT intervals were not different in variant carriers vs those without (median, 429 vs 439 milliseconds; difference, -10 milliseconds; 95% CI, -16 to +3 milliseconds; P = .17). After manual review, 22 of 63 participants (35%) with designated variants had any ECG or arrhythmia phenotype, and only 2 had corrected QT interval longer than 500 milliseconds.CONCLUSIONS AND RELEVANCE:Among laboratories experienced in genetic testing for cardiac arrhythmia disorders, there was low concordance in designating SCN5A and KCNH2 variants as pathogenic. In an unselected population, the putatively pathogenic genetic variants were not associated with an abnormal phenotype. These findings raise questions about the implications of notifying patients of incidental genetic findings.

Background— A 2% to 5% background rate of rare SCN5A nonsynonymous single nucleotide variants (nsSNVs) among healthy individuals confounds clinical genetic testing. Therefore, the purpose of this study was to enhance interpretation of SCN5A nsSNVs for clinical genetic testing using estimated predictive values derived from protein-topology and 7 in silico tools. Methods and Results— Seven in silico tools were used to assign pathogenic/benign status to nsSNVs from 2888 long-QT syndrome cases, 2111 Brugada syndrome cases, and 8975 controls. Estimated predictive values were determined for each tool across the entire SCN5A -encoded Na v 1.5 channel as well as for specific topographical regions. In addition, the in silico tools were assessed for their ability to correlate with cellular electrophysiology studies. In long-QT syndrome, transmembrane segments S3–S5+S6 and the DIII/DIV linker region were associated with high probability of pathogenicity. For Brugada syndrome, only the transmembrane spanning domains had a high probability of pathogenicity. Although individual tools distinguished case- and control-derived SCN5A nsSNVs, the composite use of multiple tools resulted in the greatest enhancement of interpretation. The use of the composite score allowed for enhanced interpretation for nsSNVs outside of the topological regions that intrinsically had a high probability of pathogenicity, as well as within the transmembrane spanning domains for Brugada syndrome nsSNVs. Conclusions— We have used a large case/control study to identify regions of Na v 1.5 associated with a high probability of pathogenicity. Although topology alone would leave the variants outside these identified regions in genetic purgatory, the synergistic use of multiple in silico tools may help promote or demote a variant’s pathogenic status.

Despite the overrepresentation of Kv7.1 mutations among patients with a robust diagnosis of long QT syndrome (LQTS), a background rate of innocuous Kv7.1 missense variants observed in healthy controls creates ambiguity in the interpretation of LQTS genetic test results. A recent study showed that the probability of pathogenicity for rare missense mutations depends in part on the topological location of the variant in Kv7.1's various structure-function domains. Since the Kv7.1's C-terminus accounts for nearly 50 % of the overall protein and nearly 50 % of the overall background rate of rare variants falls within the C-terminus, further enhancement in mutation calling may provide guidance in distinguishing pathogenic long QT syndrome type 1 (LQT1)-causing mutations from rare non-disease-causing variants in the Kv7.1's C-terminus. Therefore, we have used conservation analysis and a large case-control study to generate topology-based estimative predictive values to aid in interpretation, identifying three regions of high conservation within the Kv7.1's C-terminus which have a high probability of LQT1 pathogenicity.

Hypertrophic cardiomyopathy (HCM) is a sudden death predisposing disease often caused by sarcomeric gene mutations. Several metabolic storage diseases can also mimic the phenotypic expression of HCM. We sought to determine the spectrum and prevalence of sarcomeric and metabolic gene mutations in

Despite the significant progress that has been made in identifying disease-associated mutations, the utility of the hypertrophic cardiomyopathy (HCM) genetic test is limited by a lack of understanding of the background genetic variation inherent to these sarcomeric genes in seemingly healthy subjects. This study represents the first comprehensive analysis of genetic variation in 427 ostensibly healthy individuals for the HCM genetic test using the “gold standard” Sanger sequencing method validating the background rate identified in the publically available exomes. While mutations are clearly overrepresented in disease, a background rate as high as ∼5 % among healthy individuals prevents diagnostic certainty. To this end, we have identified a number of estimated predictive value-based associations including gene-specific, topology, and conservation methods generating an algorithm aiding in the probabilistic interpretation of an HCM genetic test.

Introduction: Recent publications from the 1000 genomes project (1kG) and the NHLBI Exome Sequencing project (ESP) have exposed the presence of rare (minor allele frequency (MAF) < 0.5%) non-synony...

Background Hundreds of SCN5A nonsynonymous single nucleotide variants (nsSNVs) have been identified in long QT syndrome (LQTS) and Brugada syndrome (BrS) cases. However, a 2% background rate of rare SCN5A nsSNVs among healthy white controls results in a signal-to-noise ratio (SNR) of 5:1 for LQT3 and 10:1 for BrS1, thus confounding clinical genetic testing. Here, we determine whether a set of 7 in silico prediction tools can enhance the SNR associated with LQTS/BrS genetic testing. Methods In this study, (1) conservation across species, (2) paralogs, (3) Grantham matrix values, (4) SIFT, (5) PolyPhen2, (6) Consensus Deleteriousness score (Condel), and (7) Mutation Assessor (Mass) algorithms were used to assign pathogenic or benign status to nsSNVs identified across 388 clinically definite LQTS index cases, 2500 suspected LQTS cases, 2111 BrS cases, the 1000 Genome project (n = 1092), 1380 ostensibly healthy control subjects, and the NHLBI Exome Sequencing Project (n = 6502). The estimated predictive values (EPVs) were determined for each tool independently, in concert with previously published protein topology-derived EPVs, and synergistically when >3 tools were in agreement. Results Although all 7 tools displayed a statistically significant ability to distinguish between SCN5A nsSNVs identified in cases from those in controls, a combination of >3 tools in agreement resulted in the greatest polarization of EPVs [>3 EPVs: LQTS=87 (81–90), BrS=96 (94–97); ≤3 EPVs: LQT=0 (0–35), BrS=21 (0–52)]. For LQTS and BrS, the in silico tools were able to enhance mutation calling within the interdomain linkers [>3 EPVs: LQTS=85 (70–93), BrS=86 (71–94)]. Interestingly for LQTS, transmembrane domains I and III had low EPVs [64 (27–82) and 68 (28-86), respectively]. The addition of in silico tools nearly doubles the SNR for both LQTS and BrS. Conclusions Although individual in silico tools alone can help upgrade/downgrade the pathogenicity of SCN5A nsSNVs, the development of tools that couple multiple independent algorithms appears more promising as the synergistic use of multiple existing in silico tools enhances the classification of nsSNVs that reside within regions where the topology-based probability of pathogenicity is suboptimal.

Cardiac ryanodine receptor (RYR2) and Kir2.1 (KCNJ2) mutations are a cause of catecholaminergic polymorphic ventricular tachycardia (CPVT), a lethal cardiac channelopathy. Here, we describe mutations in RYR2 and KCNJ2 in patients referred for FAMILION CPVT genetic testing. Sequence analysis of 38

INTRODUCTION: Although dominantly inherited diseases are defined as being caused by a single mutant gene copy, other factors can affect disease expression. Here we examined the relationship between mutation count and age at genetic testing across five autosomal dominant cardiac diseases. METHODS: We examined data for 2596 probands referred for genetic testing for arrhythmogenic right ventricular cardiomyopathy (ARVC), Brugada syndrome (BrS), catecholaminergic polymorphic ventricular tachycardia (CPVT), hypertrophic cardiomyopathy (HCM), or long QT syndrome (LQTS). Age at testing was used as a clinical correlate of age of onset. Mutations were categorized as either class I (definite or probable pathogenic) or class II (variant of uncertain significance). RESULTS: Among mutation-positive patients, 12% had two mutations and 1% had three; these two percentages were similar across all diseases, although the overall positive rate varied considerably. In all five diseases, age at testing was lower for positive than negative probands, and even lower for those with two mutations. In three out of the four diseases where at least one patient had three mutations, those patients were even younger. While differing by disease, the average age drop from 0 mutations to 1 was 3.5 years (p=0.00013) and from 1 mutation to 2 was 5.2 years (p=0.012). The patterns held when restricting to either class I or II mutations. CONCLUSIONS: This study found a remarkable 12% multiple-hit rate across five so-called autosomal dominant diseases and identified a correlation between mutation count and age at testing. This correlation may have multiple explanations: First, each mutation may contribute to disease severity or onset. Second, “more mutations” may reflect a higher probability that at least one is deleterious and that the patient indeed has the disease. Overall, the results point to a complexity that must continue to be explored as science and medicine grapple with the genetics of disease.

INTRODUCTION: CAV3 T78M has been reported to be a proarrhythmic mutation associated with Long QT Syndrome (LQTS) and other diseases. The purpose of this study was to determine if CAV3 T78M is a pat...

This study is the first to comprehensively evaluate genetic variation in healthy controls for the ARVC susceptibility genes. Radical mutations are high-probability ARVC-associated mutations, whereas rare missense mutations should be interpreted in the context of race and ethnicity, mutation location, and sequence conservation.