Genome-wide association studies (GWAS) have identified numerous common genetic variants associated with cardiovascular traits and diseases. These studies have increased our understanding of the genetic architecture of cardiac diseases and have facilitated the identification of genetic risk factors in patients. Furthermore, they have spurred the development of novel effective therapies by targeting the causal disease pathways. Despite the demonstrated clinical utility of GWAS, the mechanism of action of many of these variants remains unstudied, and this has hindered the full potential of GWAS. Various high-throughput screening and machine-learning technologies have been developed to assist with predicting and prioritizing pathogenic variants for experimental validation. These technologies can potentially unravel novel pathways in disease pathogenesis and accelerate the development of new therapies. In this review, we provide an overview of the various GWAS performed in heart disease and describe the various methods employed to prioritize disease-relevant variants from these studies, including bioinformatic and experimental approaches. We highlight relevant examples that have applied these tools to identify the specific variants in each identified locus and how some of these variants have spurred novel therapies. Finally, we discuss the outstanding challenges facing research in this field and how they can be addressed.