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.

PurposeGeisinger Health System (GHS) provides an ideal platform for Precision Medicine. Key elements are the integrated health system, stable patient population, and electronic health record (EHR) infrastructure. In 2007 Geisinger launched MyCode®, a system-wide biobanking program to link samples and EHR data for broad research use.MethodsPatient-centered input into MyCode® was obtained using participant focus groups. Participation in MyCode® is based on opt-in informed consent and allows recontact, which facilitates collection of data not in the EHR, and, since 2013, the return of clinically actionable results to participants. MyCode® leverages Geisinger’s technology and clinical infrastructure for participant tracking and sample collection.ResultsMyCode® has a consent rate of >85% with more than 90,000 participants currently, with ongoing enrollment of ~4,000 per month. MyCode® samples have been used to generate molecular data, including high-density genotype and exome sequence data. Genotype and EHR-derived phenotype data replicate previously reported genetic associations.ConclusionThe MyCode® project has created resources that enable a new model for translational research that is faster, more flexible, and more cost effective than traditional clinical research approaches. The new model is scalable, and will increase in value as these resources grow and are adopted across multiple research platforms.

Rationale Abdominal aortic aneurysm (AAA) is a complex disease with both genetic and environmental risk factors. Together, 6 previously identified risk loci only explain a small proportion of the heritability of AAA. Objective To identify additional AAA risk loci using data from all available genome-wide association studies (GWAS). Methods and Results Through a meta-analysis of 6 GWAS datasets and a validation study totalling 10,204 cases and 107,766 controls we identified 4 new AAA risk loci: 1q32.3 (SMYD2), 13q12.11 (LINC00540), 20q13.12 (near PCIF1/MMP9/ZNF335), and 21q22.2 (ERG). In various database searches we observed no new associations between the lead AAA SNPs and coronary artery disease, blood pressure, lipids or diabetes. Network analyses identified ERG, IL6R and LDLR as modifiers of MMP9, with a direct interaction between ERG and MMP9. Conclusions The 4 new risk loci for AAA appear to be specific for AAA compared with other cardiovascular diseases and related traits suggesting that traditional cardiovascular risk factor management may only have limited value in preventing the progression of aneurysmal disease.

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-Truncating variants in the Titin gene (TTNtvs) are common in individuals with idiopathic dilated cardiomyopathy (DCM). However, a comprehensive genomics-first evaluation of the impact of TTNtvs in different clinical contexts, and evaluation of modifiers such as genetic ancestry, has not been performed.