Genetic analyses across cardiovascular traits: leveraging genetic correlations to empower locus discovery and prediction in common cardiovascular diseases

Common genetic variation detected by genome-wide association studies (GWAS) partially explains variability in the spectrum of cardiac phenotypes. In this work, we explore genetic correlations among 58 cardiac-related traits/diseases, detecting novel ones. We subsequently employ multi-trait analysis of GWAS (MTAG), which meta-analyzes genetically correlated traits, to improve genomic loci discovery and prediction in atrial fibrillation (AF), coronary artery disease (CAD), and heart failure (HF). We identify 19 novel loci specific for AF, 131 for CAD, and 141 for HF. Polygenic scores (PGS) in 15,177 Canadian individuals show similar results when PGS are derived from conventional GWAS versus MTAG summary statistics, although MTAG-PGS improve prediction and discrimination of CAD in females [∆R2 1.735% (95% Confidence Interval (CI): 0.609–2.856); Net reclassification index 0.208 (95%CI: 0.139–0.277)]. This work describes new relevant genetic correlations among cardiac-related traits/diseases and supports MTAG to improve loci discovery in common cardiovascular diseases and potentially improve the prediction of CAD in females.