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The electrocardiogram (ECG) is one of the most useful non-invasive diagnostic tests for a wide array of cardiac disorders. Traditional approaches to analyzing ECGs focus on individual segments. Here, we performed comprehensive deep phenotyping of 77,190 ECGs in the UK Biobank across the complete cycle of cardiac conduction, resulting in 500 spatial-temporal datapoints, across 10 million genetic variants. In addition to characterizing polygenic risk scores for the traditional ECG segments, we identified over 300 genetic loci that are statistically associated with the high-dimensional representation of the ECG. We established the genetic ECG signature for dilated cardiomyopathy, associated the BAG3, HSPB7/CLCNKA, PRKCA, TMEM43, and OBSCN loci with disease risk and confirmed this association in an independent cohort. In total, our work demonstrates that a high-dimensional analysis of the entire ECG provides unique opportunities for studying cardiac biology and disease and furthering drug development. A record of this paper's transparent peer review process is included in the Supplemental Information.