Genetic architecture of spatial electrical biomarkers for cardiac arrhythmia and relationship with cardiovascular disease

The 3-dimensional spatial and 2-dimensional frontal QRS-T angles are measures derived from the vectorcardiogram. They are independent risk predictors for arrhythmia, but the underlying biology is unknown. Using multi-ancestry genome-wide association studies we identify 61 (58 previously unreported) loci for the spatial QRS-T angle (N=118,780) and 11 for the frontal QRS-T angle (N=159,715). Seven out of the 61 spatial QRS-T angle loci have not been reported for other electrocardiographic measures. Enrichments are observed in pathways related to cardiac and vascular development, muscle contraction, and hypertrophy. Pairwise genome-wide association studies with classical ECG traits identify shared genetic influences with PR interval and QRS duration. Phenome-wide scanning indicate associations with atrial fibrillation, atrioventricular block and arterial embolism and genetically determined QRS-T angle measures are associated with fascicular and bundle branch block (and also atrioventricular block for the frontal QRS-T angle). We identify potential biology involved in the QRS-T angle and their genetic relationships with cardiovascular traits and diseases, may inform future research and risk prediction. The spatial and frontal QRS-T angles are electrocardiographic (ECG) predictors for arrhythmia. This work used genetic analyses to identify associated loci and pathways, and explore their relationships with other ECG traits and cardiovascular disease.

Automated summary

Using multi-ancestry genome-wide association studies, this research identified 61 loci related to spatial QRS-T angle and 11 loci related to frontal QRS-T angle. These loci also showed shared genetic influences with classical ECG traits and were associated with cardiovascular diseases. The findings provide insights into the biology of QRS-T angles and their relationships with cardiovascular traits and diseases, which can inform future research and risk prediction.