Grant: Fellowship - PA: Takeda Canada and CIHR Institute of Genetics Rare Diseases

Cardiac traits and diseases have a genetic substrate with overlapping mechanisms. Common genomic variation is identifiable by genome wide association studies (GWAS) and account for a part of the variability of these cardiovascular traits and diseases. Bioinformatics methods allow the integration of overlapping genetic risk among cardiovascular traits and diseases (using joint analysis) to increase the statistical power from the original trait-specific GWAS and thus increase the identification of disease related genetic loci. We aim to combine genetically correlated cardiovascular traits in a joint analysis using novel state-of-the-art bioinformatics methods, such as multi-trait analysis of GWAS (MTAG), to enhance novel loci discovery and relevant pathophysiological pathways, and to refine genome-based prognostic prediction in common cardiovascular diseases: atrial fibrillation (AF), coronary artery disease (CAD), and heart failure (HF). These approach may be specifically relevant in underrepresented subgroups such as ethnic minorities and women, where the gain in statistical power may be particularly useful.