Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies.

Aims We investigated the relationship between clinically assessed left ventricular ejection fraction (LVEF) and survival in a large, heterogeneous clinical cohort. Methods and results Physician-reported LVEF on 403 977 echocardiograms from 203 135 patients were linked to all-cause mortality using electronic health records (1998–2018) from US regional healthcare system. Cox proportional hazards regression was used for analyses while adjusting for many patient characteristics including age, sex, and relevant comorbidities. A dataset including 45 531 echocardiograms and 35 976 patients from New Zealand was used to provide independent validation of analyses. During follow-up of the US cohort, 46 258 (23%) patients who had undergone 108 578 (27%) echocardiograms died. Overall, adjusted hazard ratios (HR) for mortality showed a u-shaped relationship for LVEF with a nadir of risk at an LVEF of 60–65%, a HR of 1.71 [95% confidence interval (CI) 1.64–1.77] when ≥70% and a HR of 1.73 (95% CI 1.66–1.80) at LVEF of 35–40%. Similar relationships with a nadir at 60–65% were observed in the validation dataset as well as for each age group and both sexes. The results were similar after further adjustments for conditions associated with an elevated LVEF, including mitral regurgitation, increased wall thickness, and anaemia and when restricted to patients reported to have heart failure at the time of the echocardiogram. Conclusion Deviation of LVEF from 60% to 65% is associated with poorer survival regardless of age, sex, or other relevant comorbidities such as heart failure. These results may herald the recognition of a new phenotype characterized by supra-normal LVEF.

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Importance Atrial fibrillation (AF) is the most common arrhythmia affecting 1% of the population. Young individuals with AF have a strong genetic association with the disease, but the mechanisms remain incompletely understood. Objective To perform large-scale, deep-coverage whole genome sequencing to identify genetic variants related to AF. Design, Setting, Participants The National Heart Lung and Blood Institute’s Trans-Omics for Precision Medicine Program includes longitudinal and cohort studies that underwent high depth, whole genome sequencing between 2014 and 2017 in 18,526 individuals from the U.S., Mexico, Puerto-Rico, Costa-Rica, Barbados, and Samoa. This case-control study included 2,781 patients with early onset AF from 9 studies and identified 4,959 controls of European ancestry from the remaining participants. Results were replicated in the UK Biobank and the MyCode Study consisting of 346,546 and 42,782 participants, respectively. Exposures Loss-of-function (LOF) variants in genes at AF loci and common genetic variation across the whole genome. Main Outcomes and Measures Early-onset AF defined as AF onset < 66 years of age. Due to multiple testing, the significance threshold for the rare variant analysis was P=4.55 X 10−3. Results Among 2,781 early-onset AF cases, 72.1% were male, and the mean age of AF onset was 48.7±10.2 years. Samples underwent whole genome sequencing at a mean depth of 37.8 fold and mean genome coverage of 99.1%. At least one LOF variant in TTN, the gene encoding the sarcomeric protein titin, was present in 2.1% of cases compared with 1.1% in controls. The proportion of individuals with early-onset AF who carried a LOF variant in TTN increased with an earlier age of AF onset (P value for trend 4.92×10−4) and 6.5% of individuals with AF onset prior to age 30 carried a TTN LOF variant (odds ratio = 5.94; 95% CI, 2.64–13.35; P=1.65×10−5). The association between TTN LOF variants and AF was replicated in an independent 1,582 early-onset AF cases and 41,200 controls (odds ratio = 2.16; 95% CI, 1.19–3.92; P=0.01). Conclusions and Relevance In a case control study, there was a statistically significant association between a LOF variant in the gene TTN and early-onset AF, with the variant present in a small percentage of cases. Further research is required to understand whether this is a causal relationship.

Background: Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We hypothesized that a deep neural network could predict new-onset AF from the resting 12-lead ECG and that this prediction may help identify those at risk of AF-related stroke. Methods: We used 1.6 M resting 12-lead digital ECG traces from 430 000 patients collected from 1984 to 2019. Deep neural networks were trained to predict new-onset AF (within 1 year) in patients without a history of AF. Performance was evaluated using areas under the receiver operating characteristic curve and precision-recall curve. We performed an incidence-free survival analysis for a period of 30 years following the ECG stratified by model predictions. To simulate real-world deployment, we trained a separate model using all ECGs before 2010 and evaluated model performance on a test set of ECGs from 2010 through 2014 that were linked to our stroke registry. We identified the patients at risk for AF-related stroke among those predicted to be high risk for AF by the model at different prediction thresholds. Results: The area under the receiver operating characteristic curve and area under the precision-recall curve were 0.85 and 0.22, respectively, for predicting new-onset AF within 1 year of an ECG. The hazard ratio for the predicted high- versus low-risk groups over a 30-year span was 7.2 (95% CI, 6.9–7.6). In a simulated deployment scenario, the model predicted new-onset AF at 1 year with a sensitivity of 69% and specificity of 81%. The number needed to screen to find 1 new case of AF was 9. This model predicted patients at high risk for new-onset AF in 62% of all patients who experienced an AF-related stroke within 3 years of the index ECG. Conclusions: Deep learning can predict new-onset AF from the 12-lead ECG in patients with no previous history of AF. This prediction may help identify patients at risk for AF-related strokes.