Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Abstract

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.

Fig. 1. Study design and analysis workflow.

Overview of study design to identify and characterise heart failure-associated risk loci and for secondary cross-trait genome-wide analyses. GWAS, genome-wide association study; QTL, quantitative trait locus; MAGMA, Multi-marker Analysis of GenoMic Annotation; SNP, single-nucleotide polymorphism; mtCOJO, multi-trait-based conditional and joint analysis.

Fig. 2. Manhattan plot of genome-wide heart failure associations.

The x-axis represents the genome in physical order; the y-axis shows −log₁₀ P values for individual variant association with heart failure risk from the meta-analysis (n = 977,323). Suggestive associations at a significance level of P < 1 × 10⁻⁵ are indicated by the blue line, while genome-wide significance at P < 5 × 10⁻⁸ is indicated by the red line. Meta-analysis was performed using a fixed-effect inverse variance-weighted model. Independent genome-wide significant variants are annotated with the nearest gene(s).

Fig. 3. Associations of HF risk variants with traits relating to disease subtypes and risk factors.

This bubble plot shows associations between the identified HF loci and risk factors and quantitative imaging traits, using summary estimates from UK Biobank (DCM, dilated cardiomyopathy) and published GWAS summary statistics. Number in bracket represents sample size (for quantitative traits) or number of cases (for binary traits) used to derive the GWAS summary statistics. The size of the bubble represents the absolute Z-score for each trait, with the direction oriented towards the HF risk allele. Red/blue indicates a positive/negative cross-trait association (i.e., increase/decrease in disease risk or increase/decrease in continuous trait). We accounted for family-wise error rate at 0.05 by Bonferroni correction for the ten traits tested per HF locus (P < 4.5e-4); traits meeting this threshold of significance for association are indicated by dark colour shading. Agglomerative hierarchical clustering of variants was performed using the complete linkage method, based on Euclidian distance. Where a sentinel variant was not available for all traits, a common proxy was selected (bold text). For the LPA locus, associations for the more common of the two variants at this locus are shown. Bold text represents variants whose estimates are plotted, upon which we performed hierarchical agglomerative clustering using the complete linkage method based on Euclidian distance. FS, fractional shortening; LVD, left ventricular dimension; DCM, dilated cardiomyopathy; AF, atrial fibrillation; CAD, coronary artery disease; LDL-C, low-density lipoprotein cholesterol; T2D, type 2 diabetes; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure.

Fig. 4. Conditional Mendelian randomisation analyses of HF risk factors.

Forest plot of HF risk factors with significant causal effect HF risk estimated using Mendelian randomisation, implemented with GSMR. Diamonds represent the odds ratio and the error bars indicate the 95% confidence interval. The unadjusted estimates represent the risk of HF as estimated from the HF GWAS data, while the adjusted estimates represent risk of HF conditioned, using GWAS summary statistics for atrial fibrillation (adjusted for AF) or coronary artery disease (adjusted for CAD) estimated using the mtCOJO method. For binary traits (coronary artery disease, atrial fibrillation and type 2 diabetes), the MR estimates represent average causal effect per natural-log odds increase in the trait risk. For continuous traits, the MR estimates represent average causal effect per standard deviation increase in the reported unit of the trait. LDL, low-density lipoprotein; HDL, high-density lipoprotein; CAD, coronary artery disease; AF, atrial fibrillation.