Background: Heart failure (HF) is a morbid and heritable disorder for which the biological mechanisms are incompletely understood. We therefore examined genetic associations with HF in a large national biobank, and assessed whether refined phenotypic classification would facilitate genetic discovery. Methods: We defined all-cause HF among 488,010 participants from the UK Biobank and performed a genome-wide association analysis. We refined the HF phenotype by classifying individuals with left ventricular dysfunction and without coronary artery disease (CAD) as having nonischemic cardiomyopathy (NICM), and repeated a genetic association analysis. We then pursued replication of lead HF and NICM variants in independent cohorts, and performed adjusted association analyses to assess whether identified genetic associations were mediated through clinical HF risk factors. In addition, we tested rare, loss-of-function mutations in 24 known dilated cardiomyopathy (DCM) genes for association with HF and NICM. Finally, we examined associations between lead variants and left ventricular structure and function among individuals without HF using cardiac magnetic resonance imaging (n=4,158) and echocardiographic data (n=30,201). Results: We identified 7,382 participants with all-cause HF in the UK Biobank. Genome-wide association analysis of all-cause HF identified several suggestive loci (P < 1×10 −6), the majority linked to upstream HF risk factors, i.e. CAD (CDKN2B-AS1 and MAP3K7CL) and atrial fibrillation (PITX2). Refining the HF phenotype yielded a subset of 2,038 NICM cases. In contrast to all-cause HF, genetic analysis of NICM revealed suggestive loci that have been implicated in DCM (BAG3, CLCNKA-ZBTB17). DCM signals arising from our NICM analysis replicated in independent cohorts, persisted after HF risk factor adjustment, and were associated with indices of left ventricular dysfunction in individuals without clinical HF. Additionally, analyses of loss-offunction variants implicated BAG3 as a disease-susceptibility gene for NICM (loss-of-function variant carrier frequency=0.01%, OR=12.03, P=3.62×10 −5). Conclusions: We found several distinct genetic mechanisms of all-cause HF in a national biobank that reflect well-known HF risk factors. Phenotypic refinement to a NICM subtype appeared to facilitate the discovery of genetic signals that act independent of clinical HF risk factors, and which are associated with subclinical left ventricular dysfunction.

SUMMARY Objective Gestational diabetes (GDM) is characterized by maternal glucose intolerance that manifests during pregnancy. Because GDM resembles type 2 diabetes (T2DM), shared genetic predisposition is likely but has not been established. We tested the hypothesis that a genetic risk score (GRS) that included variants known to be associated with T2DM is associated with GDM. Study design We conducted a case-control study using the Vanderbilt Medical Center biobank (BioVU), and calculated a simple-count GRS using 34 variants previously associated with T2DM or fasting glucose in the general population, or with GDM or glucose intolerance in pregnancy. We assessed the association of the GRS with GDM adjusting for maternal age, parity, and body mass index (BMI) and calculated the area under the curve for the receiver-operating characteristic curve (c-statistic). Study population Among Caucasian women, we identified 458 cases of GDM and 1538 pregnant controls with normal glucose tolerance. Results Cases of GDM had a higher number of risk alleles compared to controls (38.9±4.0 vs. 37.4 ± 4.0 risk alleles, P=1.6x10−11). The GRS was significantly associated with GDM; the adjusted odds ratio associated with each additional risk allele was 1.10 (95% CI, 1.07-1.13, P=6x10−11). Clinical variables predicted the risk for GDM (c-statistic 0.67, 95% CI: 0.64 - 0.70), and adding the GRS modestly improved prediction (0.70, 95% CI: 0.67 - 0.73). Conclusions Among Caucasian women, a GRS that included common T2DM genetic risk variants was associated with increased risk of GDM but showed limited utility in the identification of GDM cases.

Greater genetic variability in an individual is protective against recessive disease. However, existing quantifications of autozygosity, such as runs of homozygosity (ROH), have proved highly sensitive to genotyping density and have yielded inconclusive results about the relationship of diversity and disease risk. Using genotyping data from three data sets with .43,000 subjects, we demonstrated that an alternative approach to quantifying genetic variability, the heterozygosity ratio, is a robust measure of diversity and is positively associated with the nondisease trait height and several disease phenotypes in subjects of European ancestry. The heterozygosity ratio is the number of heterozygous sites in an individual divided by the number of nonreference homozygous sites and is strongly affected by the degree of genetic admixture of the population and varies across human populations. Unlike quantifications of ROH, the heterozygosity ratio is not sensitive to the density of genotyping performed. Our results establish the heterozygosity ratio as a powerful new statistic for exploring the patterns and phenotypic effects of different levels of genetic variation in populations.

Background Anthracyclines are important chemotherapeutic agents, but their use is limited by cardiotoxicity. Candidate gene and genome-wide studies have identified putative risk loci for overt cardiotoxicity and heart failure, but there has been no comprehensive assessment of genomic variation influencing the intermediate phenotype of anthracycline-related changes in left ventricular (LV) function. The purpose of this study was to identify genetic factors influencing changes in LV function after anthracycline chemotherapy. Methods We conducted a genome-wide association study (GWAS) of change in LV function after anthracycline exposure in 385 subjects identified from BioVU, a resource linking DNA samples to de-identified electronic medical record data. Variants with p-values <1×10−5 were independently tested for replication in a cohort of 181 anthracycline-exposed subjects from a prospective clinical trial. Pathway analysis was performed to assess combined effects of multiple genetic variants. Results Both cohorts were middle-aged adults of predominantly European descent. Among 11 candidate loci identified in discovery GWAS, one single nucleotide polymorphism (SNP) near PR Domain Containing 2, With ZNF Domain (PRDM2), rs7542939, had a combined p-value of 6.5×10−7 in meta-analysis. Eighteen Kyoto Encyclopedia of Gene and Genomes (KEGG) pathways showed strong enrichment for variants associated with the primary outcome. Identified pathways related to DNA repair, cellular metabolism, and cardiac remodeling. Conclusions Using genome-wide association we identified a novel candidate susceptibility locus near PRDM2. Variation in genes belonging to pathways related to DNA repair, metabolism, and cardiac remodeling may influence changes in LV function after anthracycline exposure.

Background Transcriptomic studies have contributed to fundamental knowledge of myocardial remodeling in human heart failure (HF). However, the key HF genes reported are often inconsistent between studies, and systematic efforts to integrate evidence from multiple patient cohorts are lacking. Here, we aimed to provide a framework for comprehensive comparison and analysis of publicly available data sets resulting in an unbiased consensus transcriptional signature of human end‐stage HF. Methods and Results We curated and uniformly processed 16 public transcriptomic studies of left ventricular samples from 263 healthy and 653 failing human hearts. First, we evaluated the degree of consistency between studies by using linear classifiers and overrepresentation analysis. Then, we meta‐analyzed the deregulation of 14 041 genes to extract a consensus signature of HF. Finally, to functionally characterize this signature, we estimated the activities of 343 transcription factors, 14 signaling pathways, and 182 micro RNAs, as well as the enrichment of 5998 biological processes. Machine learning approaches revealed conserved disease patterns across all studies independent of technical differences. These consistent molecular changes were prioritized with a meta‐analysis, functionally characterized and validated on external data. We provide all results in a free public resource ( https://saezlab.shinyapps.io/reheat/ ) and exemplified usage by deciphering fetal gene reprogramming and tracing the potential myocardial origin of the plasma proteome markers in patients with HF. Conclusions Even though technical and sampling variability confound the identification of differentially expressed genes in individual studies, we demonstrated that coordinated molecular responses during end‐stage HF are conserved. The presented resource is crucial to complement findings in independent studies and decipher fundamental changes in failing myocardium.