Genetic Architecture of Heart Rate in Diverse Global Populations

Background: Heart rate (HR) is a heritable trait influenced by genetic and environmental factors, with established associations to cardiovascular disease (CVD). While previous genome-wide association studies (GWAS) have identified nearly 500 genome-wide significant loci, these studies have been predominantly conducted in populations of European ancestry, limiting our understanding of HR genetics across diverse populations. This study is the largest and most ancestrally diverse GWAS meta-analysis of HR to date, encompassing global populations to better characterize shared and population-specific genetic determinants of HR regulation. Methods: Summary statistics were obtained from the UK Biobank (n = 405,308), Million Veteran Program (n = 608,816), Taiwan Precision Medicine Initiative (n = 141,961), Korean Genome and Epidemiology Study (n = 65,812), Genes and Health (n = 39,134), and All of Us Research Program (n = 209,869). Fixed-effects inverse-variance weighted trans-ancestry and population-specific meta-analyses were performed using METAL. Independent loci were defined based on a physical distance of 500 kb spanning genome-wide significance (GWS; p < 5x10-8). To prioritize effector genes, 99% Bayesian credible sets were computed at each locus and annotated using Fine-Mapped Locus Assessment Model of Effector Genes (FLAMES), combining SNP-to-gene evidence with Multi-marker Analysis of GenoMic Annotation (MAGMA) Z-scores and Polygenic Priority Scores. Results: In 1.4 million individuals across six populations, we identified 603 unified GWS loci. Population-specific analyses revealed 634 significant loci in the European population, 98 in the African population, 60 in the East Asian population, 41 in the Admixed American population, and 17 in the South Asian population. The Middle Eastern population analysis yielded no GWS loci. FLAMES prioritized 428 unique effector genes, including 143 novel genes. Effector gene prioritization identified well-established cardiac genes across populations, including GJA1, HCN1, and SCN5A. Novel gene candidates include NYAP2, DCP1B, and EYA4. Conclusion: This multi-ancestry analysis reveals new insights into the genetic architecture underlying HR regulation and demonstrates the importance of diverse representation in genetics research. These findings establish a foundation for investigating genetic correlations between HR and complex diseases through phenome-wide association studies to identify pleiotropic effects and applying Mendelian Randomization to determine whether HR causally influences cardiovascular outcomes.