The Frequency, Penetrance and Variable Expressivity of Dilated Cardiomyopathy-Associated Putative Pathogenic Gene Variants in UK Biobank Participants

Background: There is a paucity of data regarding the phenotype of dilated cardiomyopathy (DCM) gene variants in the general population. We aimed to determine the frequency and penetrance of DCM-associated putative pathogenic gene variants in a general, adult population, with a focus on the expression of clinical and subclinical phenotype, including structural, functional and arrhythmic disease features. Methods: UK Biobank participants who had undergone whole exome sequencing (WES), ECG and cardiovascular magnetic resonance (CMR) imaging were selected for study. Three different variant calling strategies (one primary and two secondary) were used to identify subjects with putative pathogenic variants in 44 DCM genes. The observed phenotype was graded to either 1) DCM (clinical or CMR diagnosis); 2) early DCM features, including arrhythmia and/or conduction disease, isolated ventricular dilation, and hypokinetic non-dilated cardiomyopathy; or 3) phenotype-negative. Results: Among 18,665 individuals included in the study, 1,463 (7.8%) subjects possessed ≥1 putative pathogenic variant in 44 DCM genes by the main variant calling strategy. A clinical diagnosis of DCM was present in 0.34% and early DCM features in 5.7% of individuals with putative pathogenic variants. ECG and CMR analysis revealed evidence of subclinical DCM in an additional 1.6% and early DCM features in 15.9% of individuals with putative pathogenic variants. Arrhythmias and/or conduction disease (15.2%) were the most common early DCM features, followed by hypokinetic non-dilated cardiomyopathy (4%). The combined clinical/subclinical penetrance was ≤30% with all three variant filtering strategies. Clinical DCM was slightly more prevalent among participants with putative pathogenic variants in definitive/strong evidence genes, as compared to those with variants in moderate/limited evidence genes. Conclusions: In the UK Biobank, approximately 1/6 of adults with putative pathogenic variants in DCM genes exhibited a subclinical phenotype based on ECG and/or CMR, most commonly manifesting with arrhythmias in the absence of substantial ventricular dilation/dysfunction.

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Use of SNP chips to detect rare pathogenic variants: retrospective, population based diagnostic evaluation

BMJ ◽

10.1136/bmj.n214 ◽

2021 ◽

pp. n214

Author(s):

Weedon MN ◽

Jackson L ◽

Harrison JW ◽

Ruth KS ◽

Tyrrell J ◽

...

Keyword(s):

Positive Predictive Value ◽

Predictive Value ◽

Population Based ◽

Genome Project ◽

Personal Genome ◽

Uk Biobank ◽

Sequencing Data ◽

Snp Chip ◽

Pathogenic Variants ◽

The Uk

Abstract Objective To determine whether the sensitivity and specificity of SNP chips are adequate for detecting rare pathogenic variants in a clinically unselected population. Design Retrospective, population based diagnostic evaluation. Participants 49 908 people recruited to the UK Biobank with SNP chip and next generation sequencing data, and an additional 21 people who purchased consumer genetic tests and shared their data online via the Personal Genome Project. Main outcome measures Genotyping (that is, identification of the correct DNA base at a specific genomic location) using SNP chips versus sequencing, with results split by frequency of that genotype in the population. Rare pathogenic variants in the BRCA1 and BRCA2 genes were selected as an exemplar for detailed analysis of clinically actionable variants in the UK Biobank, and BRCA related cancers (breast, ovarian, prostate, and pancreatic) were assessed in participants through use of cancer registry data. Results Overall, genotyping using SNP chips performed well compared with sequencing; sensitivity, specificity, positive predictive value, and negative predictive value were all above 99% for 108 574 common variants directly genotyped on the SNP chips and sequenced in the UK Biobank. However, the likelihood of a true positive result decreased dramatically with decreasing variant frequency; for variants that are very rare in the population, with a frequency below 0.001% in UK Biobank, the positive predictive value was very low and only 16% of 4757 heterozygous genotypes from the SNP chips were confirmed with sequencing data. Results were similar for SNP chip data from the Personal Genome Project, and 20/21 individuals analysed had at least one false positive rare pathogenic variant that had been incorrectly genotyped. For pathogenic variants in the BRCA1 and BRCA2 genes, which are individually very rare, the overall performance metrics for the SNP chips versus sequencing in the UK Biobank were: sensitivity 34.6%, specificity 98.3%, positive predictive value 4.2%, and negative predictive value 99.9%. Rates of BRCA related cancers in UK Biobank participants with a positive SNP chip result were similar to those for age matched controls (odds ratio 1.31, 95% confidence interval 0.99 to 1.71) because the vast majority of variants were false positives, whereas sequence positive participants had a significantly increased risk (odds ratio 4.05, 2.72 to 6.03). Conclusions SNP chips are extremely unreliable for genotyping very rare pathogenic variants and should not be used to guide health decisions without validation.

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Prevalence and cardiometabolic correlates of ketohexokinase gene variants among UK Biobank participants

PLoS ONE ◽

10.1371/journal.pone.0247683 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0247683

Author(s):

Joseph A. Johnston ◽

David R. Nelson ◽

Pallav Bhatnagar ◽

Sarah E. Curtis ◽

Yu Chen ◽

...

Keyword(s):

Autosomal Recessive ◽

Compound Heterozygous ◽

Gene Variants ◽

Uk Biobank ◽

Loss Of Function ◽

Recessive Condition ◽

Clinical Consequences ◽

Autosomal Recessive Condition ◽

Clinically Significant ◽

The Uk

Essential fructosuria (EF) is a benign, asymptomatic, autosomal recessive condition caused by loss-of-function variants in the ketohexokinase gene and characterized by intermittent appearance of fructose in the urine. Despite a basic understanding of the genetic and molecular basis of EF, relatively little is known about the long-term clinical consequences of ketohexokinase gene variants. We examined the frequency of ketohexokinase variants in the UK Biobank sample and compared the cardiometabolic profiles of groups of individuals with and without these variants alone or in combination. Study cohorts consisted of groups of participants defined based on the presence of one or more of the five ketohexokinase gene variants tested for in the Affymetrix assays used by the UK Biobank. The rs2304681:G>A (p.Val49Ile) variant was present on more than one-third (36.8%) of chromosomes; other variant alleles were rare (<1%). No participants with the compound heterozygous genotype present in subjects exhibiting the EF phenotype in the literature (Gly40Arg/Ala43Thr) were identified. The rs2304681:G>A (p.Val49Ile), rs41288797 (p.Val188Met), and rs114353144 (p.Val264Ile) variants were more common in white versus non-white participants. Otherwise, few statistically or clinically significant differences were observed after adjustment for multiple comparisons. These findings reinforce the current understanding of EF as a rare, benign, autosomal recessive condition.

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Abstract 13296: Risk Loci of Hypertrophic Cardiomyopathy Identified via the UK Biobank

Circulation ◽

10.1161/circ.142.suppl_3.13296 ◽

2020 ◽

Vol 142 (Suppl_3) ◽

Author(s):

Alex Gyftopoulos ◽

Yi-Ju Chen ◽

Libin Wang ◽

Charles H Williams ◽

Young Wook Chun ◽

...

Keyword(s):

Hypertrophic Cardiomyopathy ◽

Large Population ◽

Phenotypic Expression ◽

Coiled Coil ◽

Intraflagellar Transport ◽

Genetic Profile ◽

Uk Biobank ◽

Disease Manifestation ◽

Pathogenic Variants ◽

The Uk

Introduction: Hypertrophic cardiomyopathy (HCM) is the most commonly inherited cardiac disease affecting 1:500 to 1:200 individuals worldwide. HCM has a heterogeneous genetic profile and phenotypic expression. More than 1400 known pathogenic variants have been identified in 11 sarcomere genes. In about 40% of HCM patients, the genetic cause may not be identified. The same mutation may lead to different phenotypes and severity in different individuals. Identification of novel HCM genes and modifiers will expand our understanding of the signaling pathways that are responsible for phenotypic expression of HCM. Methods: The UK Biobank comprises clinical and genetic data for greater than 500,000 individuals. We used OASIS, an information system for analyzing, searching, and visualizing associations between phenotype and genotype data to analyze this data. We compared control individuals to HCM individuals identified by ICD-10 code (I42.1 and I42.2) in a 20-to-1 fashion. Related individuals and those with confounding diagnoses were excluded. Results: The analysis was performed with Plink’s GLM option, and we identified 84 variants with a minor allele frequency of 0.5% or greater in 65 genes associated with HCM with a p < 1x10 -6 , including 4 with p < 5x10 -8 . The identified genes encode lncRNAs, miRNAs, and membrane proteins. Variants with high significance were identified in the genes encoding putative ciliary components DNAL4 (dynein axonemal light chain 4; p = 2.9x10 -8 ), MYO1D (unconventional myosin 1D; p = 3.1x10 -8 ), ITFAP (intraflagellar transport associated protein; p = 9.5x10 -8 ), CABCOCO1 (ciliary associated calcium biding coiled-coil 1; p = 3.7x 10 -7 ), EVL (Enah-Vasp-like; p = 4.4x 10 -7 ) and IFT122 (intraflagellar transport 122; p = 8.0 x10 -7 ). Conclusion: While none of these have previously associated with HCM, our findings suggest ciliary structure and function may play a role in disease manifestation. Our method is unique by pooling individuals in a large population set to identify potential causative or contributing mutations. Bioinformatic tools, such as OASIS, allow for the identification of previously unrecognized variants that may play a role in the development of HCM. This approach has identified numerous novel genes as possible risk loci.

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Rare Genetic Variation Underlying Human Diseases and Traits: Results from 200,000 Individuals in the UK Biobank

10.1101/2020.11.29.402495 ◽

2020 ◽

Author(s):

Sean J. Jurgens ◽

Seung Hoan Choi ◽

Valerie N. Morrill ◽

Mark Chaffin ◽

James P. Pirruccello ◽

...

Keyword(s):

Cardiovascular Disease ◽

Genetic Variation ◽

Exome Sequencing ◽

Human Diseases ◽

Disease Genes ◽

Uk Biobank ◽

Novel Genes ◽

Pathogenic Variants ◽

Rare Genetic Variation ◽

The Uk

AbstractBackgroundMany human diseases are known to have a genetic contribution. While genome-wide studies have identified many disease-associated loci, it remains challenging to elucidate causal genes. In contrast, exome sequencing provides an opportunity to identify new disease genes and large-effect variants of clinical relevance. We therefore sought to determine the contribution of rare genetic variation in a curated set of human diseases and traits using a unique resource of 200,000 individuals with exome sequencing data from the UK Biobank.Methods and ResultsWe included 199,832 participants with a mean age of 68 at follow-up. Exome-wide gene-based tests were performed for 64 diseases and 23 quantitative traits using a mixed-effects model, testing rare loss-of-function and damaging missense variants. We identified 51 known and 23 novel associations with 26 diseases and traits at a false-discovery-rate of 1%. There was a striking risk associated with many Mendelian disease genes including: MYPBC3 with over a 100-fold increased odds of hypertrophic cardiomyopathy, PKD1 with a greater than 25-fold increased odds of chronic kidney disease, and BRCA2, BRCA1, ATM and PALB2 with 3 to 10-fold increased odds of breast cancer. Notable novel findings included an association between GIGYF1 and type 2 diabetes (OR 5.6, P=5.35×10−8), elevated blood glucose, and lower insulin-like-growth-factor-1 levels. Rare variants in CCAR2 were also associated with diabetes risk (OR 13, P=8.5×10−8), while COL9A3 was associated with cataract (OR 3.4, P=6.7×10−8). Notable associations for blood lipids and hypercholesterolemia included NR1H3, RRBP1, GIGYF1, SCGN, APH1A, PDE3B and ANGPTL8. A number of novel genes were associated with height, including DTL, PIEZO1, SCUBE3, PAPPA and ADAMTS6, while BSN was associated with body-mass-index. We further assessed putatively pathogenic variants in known Mendelian cardiovascular disease genes and found that between 1.3 and 2.3% of the population carried likely pathogenic variants in known cardiomyopathy, arrhythmia or hypercholesterolemia genes.ConclusionsLarge-scale population sequencing identifies known and novel genes harboring high-impact variation for human traits and diseases. A number of novel findings, including GIGYF1,represent interesting potential therapeutic targets. Exome sequencing at scale can identify a meaningful proportion of the population that carries a pathogenic variant underlying cardiovascular disease.

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Assessing the analytical validity of SNP-chips for detecting very rare pathogenic variants: implications for direct-to-consumer genetic testing

10.1101/696799 ◽

2019 ◽

Cited By ~ 14

Author(s):

Michael N Weedon ◽

Leigh Jackson ◽

James W Harrison ◽

Kate S Ruth ◽

Jessica Tyrrell ◽

...

Keyword(s):

Genetic Testing ◽

Genetic Variants ◽

Rare Variants ◽

Uk Biobank ◽

Sequencing Data ◽

Snp Chip ◽

Direct To Consumer ◽

Pathogenic Variants ◽

Variant Frequency ◽

The Uk

ABSTRACTObjectivesTo determine the analytical validity of SNP-chips for genotyping very rare genetic variants.DesignRetrospective study using data from two publicly available resources, the UK Biobank and the Personal Genome Project.SettingResearch biobanks and direct-to-consumer genetic testing in the UK and USA.Participants49,908 individuals recruited to UK Biobank, and 21 individuals who purchased consumer genetic tests and shared their data online via the Personal Genomes Project.Main outcome measuresWe assessed the analytical validity of genotypes from SNP-chips (index test) with sequencing data (reference standard). We evaluated the genotyping accuracy of the SNP-chips and split the results by variant frequency. We went on to select rare pathogenic variants in the BRCA1 and BRCA2 genes as an exemplar for detailed analysis of clinically-actionable variants in UK Biobank, and assessed BRCA-related cancers (breast, ovarian, prostate and pancreatic) in participants using cancer registry data.ResultsSNP-chip genotype accuracy is high overall; sensitivity, specificity and precision are all >99% for 108,574 common variants directly genotyped by the UK Biobank SNP-chips. However, the likelihood of a true positive result reduces dramatically with decreasing variant frequency; for variants with a frequency <0.001% in UK Biobank the precision is very low and only 16% of 4,711 variants from the SNP-chips confirm with sequencing data. Results are similar for SNP-chip data from the Personal Genomes Project, and 20/21 individuals have at least one rare pathogenic variant that has been incorrectly genotyped. For pathogenic variants in the BRCA1 and BRCA2 genes, the overall performance metrics of the SNP-chips in UK Biobank are sensitivity 34.6%, specificity 98.3% and precision 4.2%. Rates of BRCA-related cancers in individuals in UK Biobank with a positive SNP-chip result are similar to age-matched controls (OR 1.28, P=0.07, 95% CI: 0.98 to 1.67), while sequence-positive individuals have a significantly increased risk (OR 3.73, P=3.5×10−12, 95% CI: 2.57 to 5.40).ConclusionSNP-chips are extremely unreliable for genotyping very rare pathogenic variants and should not be used to guide health decisions without validation.SUMMARY BOXSection 1: What is already known on this topicSNP-chips are an accurate and affordable method for genotyping common genetic variants across the genome. They are often used by direct-to-consumer (DTC) genetic testing companies and research studies, but there several case reports suggesting they perform poorly for genotyping rare genetic variants when compared with sequencing.Section 2: What this study addsOur study confirms that SNP-chips are highly inaccurate for genotyping rare, clinically-actionable variants. Using large-scale SNP-chip and sequencing data from UK Biobank, we show that SNP-chips have a very low precision of <16% for detecting very rare variants (i.e. the majority of variants with population frequency of <0.001% are false positives). We observed a similar performance in a small sample of raw SNP-chip data from DTC genetic tests. Very rare variants assayed using SNP-chips should not be used to guide health decisions without validation.

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Gene-based association analysis identifies 190 genes affecting neuroticism

Scientific Reports ◽

10.1038/s41598-021-82123-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Nadezhda M. Belonogova ◽

Irina V. Zorkoltseva ◽

Yakov A. Tsepilov ◽

Tatiana I. Axenovich

Keyword(s):

Association Analysis ◽

Bioinformatics Analysis ◽

Specific Protein ◽

Gene Variants ◽

Summary Statistics ◽

Uk Biobank ◽

Protein Coding ◽

Pathogenic Variants ◽

Genome Wide ◽

Coding Variants

AbstractNeuroticism is a personality trait, which is an important risk factor for psychiatric disorders. Recent genome-wide studies reported about 600 genes potentially influencing neuroticism. Little is known about the mechanisms of their action. Here, we aimed to conduct a more detailed analysis of genes that can regulate the level of neuroticism. Using UK Biobank-based GWAS summary statistics, we performed a gene-based association analysis using four sets of within-gene variants, each set possessing specific protein-coding properties. To guard against the influence of strong GWAS signals outside the gene, we used a specially designed procedure called “polygene pruning”. As a result, we identified 190 genes associated with neuroticism due to the effect of within-gene variants rather than strong GWAS signals outside the gene. Thirty eight of these genes are new. Within all genes identified, we distinguished two slightly overlapping groups obtained from using protein-coding and non-coding variants. Many genes in the former group included potentially pathogenic variants. For some genes in the latter group, we found evidence of pleiotropy with gene expression. Using a bioinformatics analysis, we prioritized the neuroticism genes and showed that the genes that contribute to neuroticism through their within-gene variants are the most appropriate candidate genes.

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COVID-19 and associations with frailty and multimorbidity: a prospective analysis of UK Biobank participants

10.1101/2020.06.09.20126292 ◽

2020 ◽

Cited By ~ 1

Author(s):

S J Woolford ◽

S D’Angelo ◽

E M Curtis ◽

C M Parsons ◽

K A Ward ◽

...

Keyword(s):

Prognostic Value ◽

Walking Speed ◽

Prospective Analysis ◽

Test Results ◽

Uk Biobank ◽

The Past ◽

Negative Results ◽

Slow Walking ◽

The Uk ◽

Slow Walking Speed

AbstractBackgroundFrailty and multimorbidity have been suggested as risk factors for severe COVID-19 disease.AimsWe investigated whether frailty and multimorbidity were associated with risk of hospitalisation with COVID-19 in the UK Biobank.Methods502,640 participants aged 40-69 years at baseline (54-79 years at COVID-19 testing) were recruited across UK 2006-10. A modified assessment of frailty using Fried’s classification was generated from baseline data. COVID-19 test results (England) were available 16/03/2020-01/06/2020, mostly taken in hospital settings. Logistic regression was used to discern associations between frailty, multimorbidity and COVID-19 diagnoses, adjusting for sex, age, BMI, ethnicity, education, smoking and number of comorbidity groupings, comparing COVID-19 positive, COVID-19 negative and non-tested groups.Results4,510 participants were tested for COVID-19 (positive=1,326, negative=3,184). 497,996 participants were not tested. Compared to the non-tested group, after adjustment, COVID-19 positive participants were more likely to be frail (OR=1.3 [95% CI=1.1, 1.7]), report slow walking speed (OR=1.3 [1.1, 1.6]), report two or more falls in the past year (OR=1.3 [1.0, 1.5]) and be multimorbid (≥4 comorbidity groupings vs 0-1: OR=1.9 [1.5, 2.3]). However, similar strength of associations were apparent when comparing COVID-19 negative and non-tested groups. Furthermore, frailty and multimorbidity were not associated with COVID-19 diagnoses, when comparing COIVD-19 positive and COVID-19 negative participants.Discussion and conclusionsFrailty and multimorbidity do not appear to aid risk stratification, in terms of a positive versus negative results of COVID-19 testing. Investigation of the prognostic value of these markers for adverse clinical sequelae following COVID-19 disease is urgently needed.

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Resilience to dominant genetic disease in the healthy elderly

10.1101/19006932 ◽

2019 ◽

Author(s):

Paul Lacaze ◽

Robert Sebra ◽

Moeen Riaz ◽

Jane Tiller ◽

Jerico Revote ◽

...

Keyword(s):

Genetic Disease ◽

The Elderly ◽

Prior History ◽

Disease Genes ◽

Uk Biobank ◽

Healthy Elderly ◽

Pathogenic Variants ◽

High Genetic Risk ◽

Dominant Disease ◽

The Uk

ABSTRACTHere we describe genomic screening of the healthy elderly to identify those resilient to adult-onset genetic disease, despite being at exceptionally high genetic risk. We sequenced 13,131 individuals aged 70 or older (mean age 75 years) from the ASPirin in Reducing Events in the Elderly (ASPREE) trial. Participants had no prior history of cardiovascular disease, life-threatening cancer, persistent physical disability or dementia. We compared the prevalence of pathogenic variants in medically actionable autosomal dominant disease genes with that from the UK Biobank population, and assessed their clinical impact using personal medical history and adjudicated study outcomes during 4.5 years of follow-up. The frequency of pathogenic variants was less than reported among the younger UK Biobank population, suggesting these variants confer a survival disadvantage during the middle years of life. Yet we identified 141 individuals with pathogenic variants free of any associated disease up to average age 79.5 years. Further study of these elderly resilient individuals might help uncover genetic mechanisms that protect against the development of disease.

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Exploring polygenic-environment and residual-environment interactions for depressive symptoms within the UK Biobank

10.1101/2021.08.02.21261499 ◽

2021 ◽

Author(s):

Alexandra Claire Gillett ◽

Bradley Jermy ◽

S. Hong Lee ◽

Oliver Pain ◽

David M Howard ◽

...

Keyword(s):

Depressive Symptoms ◽

A Priori ◽

Reaction Norm ◽

Risk Scores ◽

Environment Interaction ◽

Uk Biobank ◽

Negative Results ◽

Residual Variation ◽

A Genome ◽

The Uk

Substantial advances have been made in identifying genetic contributions to depression, but little is known about how the effect of genes can be modulated by the environment, creating a gene-environment interaction. Using multivariate reaction norm models (MRNMs) within the UK Biobank (N=61294-91644), we investigate whether the polygenic and residual variation of depressive symptoms are modulated by 25 a-priori selected covariate traits: 12 environmental variables, 5 biomarkers and polygenic risk scores for 8 mental health disorders. MRNMs provide unbiased polygenic-covariate interaction estimates for a quantitative trait by controlling for outcome-covariate correlations and residual-covariate interactions. Of the 25 selected covariates, 11 significantly modulate depressive symptoms, but no single interaction explains a large proportion of phenotypic variation. Results are dominated by residual-covariate interactions, suggesting that covariate traits (including neuroticism, childhood trauma and BMI) typically interact with unmodelled variables, rather than a genome-wide polygenic score, to influence depressive symptoms. Only average sleep duration has a polygenic-covariate interaction explaining a demonstrably non-zero proportion of the variability in depressive symptoms. This effect is small, accounting for only 1.22% (95% CI [0.54,1.89]) of variation. The presence of an interaction highlights a specific focus for intervention, but the negative results here indicate a limited contribution from polygenic-environment interactions.

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Implications of Genetic Testing in Dilated Cardiomyopathy

Circulation Genomic and Precision Medicine ◽

10.1161/circgen.120.003031 ◽

2020 ◽

Vol 13 (5) ◽

pp. 476-487

Author(s):

Job A.J. Verdonschot ◽

Mark R. Hazebroek ◽

Ingrid P.C. Krapels ◽

Michiel T.H.M. Henkens ◽

Anne Raafs ◽

...

Keyword(s):

Atrial Fibrillation ◽

Risk Factor ◽

Ventricular Tachycardia ◽

Genetic Testing ◽

Dilated Cardiomyopathy ◽

Atrioventricular Block ◽

Gene Variants ◽

Gene Variant ◽

Nonsustained Ventricular Tachycardia ◽

Pathogenic Gene

Background: Genetic analysis is a first-tier test in dilated cardiomyopathy (DCM). Electrical phenotypes are common in genetic DCM, but their exact contribution to the clinical course and outcome is unknown. We determined the prevalence of pathogenic gene variants in a large unselected DCM population and determined the role of electrical phenotypes in association with outcome. Methods: This study included 689 patients with DCM from the Maastricht Cardiomyopathy Registry, undergoing genetic evaluation using a 48 cardiomyopathy-associated gene-panel, echocardiography, endomyocardial biopsies, and Holter monitoring. Upon detection of a pathogenic variant in a patient with DCM, familial segregation was performed. Outcome was defined as cardiovascular death, heart transplantation, heart failure hospitalization, and/or occurrence of life-threatening arrhythmias. Results: A (likely) pathogenic gene variant was found in 19% of patients, varying from 36% in familial to 13% in nonfamilial DCM. Family segregation analysis showed familial disease in 46% of patients with DCM who were initially deemed nonfamilial by history. Overall, 18% of patients with a nongenetic risk factor had a pathogenic gene variant. Almost all pathogenic gene variants occurred in just 12 genes previously shown to have robust disease association with DCM. Genetic DCM was independently associated with electrical phenotypes such as atrial fibrillation, nonsustained ventricular tachycardia, and atrioventricular block and inversely correlated with the presence of a left bundle branch block ( P <0.01). After a median follow-up of 4 years, event-free survival was reduced in genetic versus patients with nongenetic DCM ( P =0.01). This effect on outcome was mediated by the associated electrical phenotypes of genetic DCM ( P <0.001). Conclusions: One in 5 patients with an established nongenetic risk factor or a nonfamilial disease still carries a pathogenic gene variant. Genetic DCM is characterized by a profile of electrical phenotypes (atrial fibrillation, nonsustained ventricular tachycardia, and atrioventricular block), which carries increased risk for adverse outcomes. Based on these findings, we envisage a broader role for genetic testing in DCM.

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