Phenotypic Refinement of Heart Failure in a National Biobank Facilitates Genetic Discovery

Abstract

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 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 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=4158) and echocardiographic data (n=30 201).

Results: We identified 7382 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, ie, coronary artery disease (CDKN2B-AS1 and MAP3K7CL) and atrial fibrillation (PITX2). Refining the HF phenotype yielded a subset of 2038 NICM cases. In contrast to all-cause HF, genetic analysis of NICM revealed suggestive loci that have been implicated in dilated cardiomyopathy (BAG3, CLCNKA-ZBTB17). Dilated cardiomyopathy 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. In addition, analyses of loss-of-function variants implicated BAG3 as a disease susceptibility gene for NICM (loss-of-function variant carrier frequency=0.01%; odds ratio,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 independently of clinical HF risk factors and that are associated with subclinical left ventricular dysfunction.

Keywords: cardiomyopathies; cardiomyopathy, dilated; genome-wide association study; heart failure.

Figure 1.. Epidemiological overlap between heart failure phenotypes and prominent risk factors in UK Biobank.

The overlap between all-cause heart failure, nonischemic cardiomyopathy, coronary artery disease, and atrial fibrillation cases are displayed among 488,010 study participants in the UK Biobank. Case counts represent the sum total of disease at baseline and incident cases.

Figure 2.. Manhattan plots of primary genome-wide association discovery analysis in UK Biobank for (a) all-cause heart failure and (b) nonischemic cardiomyopathy.

Logistic regression was used to test the association of allelic dosages for all variants with MAF > 1% with both all-cause heart failure and nonischemic cardiomyopathy, adjusting for age at baseline, sex, genotyping chip, and the first 10 principal components of ancestry. Lines are drawn at 1×10⁻⁶ and 5×10⁻⁸ to denote suggestive and genome-wide significant associations, respectively. Loci demonstrating P-value < 1×10⁻⁶ are highlighted in blue and the nearest genes are labeled.

Figure 3.. Association of suggestive all-cause heart failure and nonischemic cardiomyopathy variants adjusted for known heart failure risk factors.

Logistic regression was used to test the association of lead variants identified at suggestive loci (P < 1×10⁻⁶) for either all-cause heart failure or nonischemic cardiomyopathy against both endpoints adjusted for baseline atrial fibrillation, baseline coronary artery disease, and baseline hypertension. Nonischemic cardiomyopathy testing was not adjusted for coronary artery disease as coronary artery disease was an exclusion criteria. All analyses were additionally adjusted for age at baseline, sex, genotyping array, and the first 10 principal components of ancestry. Circle size denotes P-value and shading represents the odds ratio for a 1-allele increase of the all-cause heart failure/nonischemic cardiomyopathy risk allele. Abbreviations: HF=heart failure; NICM=nonischemic cardiomyopathy; AF=atrial fibrillation; CAD=coronary artery disease; HTN=hypertension.

Figure 4.. Association of suggestive all-cause heart failure and nonischemic cardiomyopathy variants with selected cardiac MRI traits of left ventricular structure and function in UK Biobank.

Linear regression was used to test the association of suggestive signals for all-cause heart failure and nonischemic cardiomyopathy variants with measured cardiac MRI traits in up to 4,158 individuals free of clinical heart failure in the UK Biobank. Testing was performed using allelic dosages, adjusting for age at baseline, sex, genotyping chip, and the first 10 principal components of ancestry. Results are displayed for (a) rs2234962 near BAG3 and (b) rs10927875 near ZBTB17 against three selected cardiac MRI traits as no other variants had associations reaching statistical significance. Points represent the effect in SD units of each respective cardiac MRI trait and error bars denote 95% confidence intervals. Significant associations passing Bonferroni significance (P < 0.05 / 42 = 1.19×10⁻³) are denoted with a star (*). Abbreviations: NICM=nonischemic cardiomyopathy; β=effect per NICM risk allele in SD units of the cardiac MRI trait; SD=standard deviation.