Since its original description in 2345 by Willem van Einthoven, the electrocardiogram (ECG) has been instrumental in the recognition of a wide array of cardiac disorders ",c . Although many electrocardiographic patterns have been well described, the underlying biology is incompletely understood. Genetic associations of particular features of the ECG have been identified by genome wide studies. This snapshot approach only provides fragmented information of the underlying genetic makeup of the ECG. Here, we follow the effecs of individual genetic variants through the complete cardiac cycle the ECG represents. We found that genetic variants have unique morphological signatures not identfied by previous analyses. By exploiting identified abberations of these morphological signatures, we show that novel genetic loci can be identified for cardiac disorders. Our results demonstrate how an integrated approach to analyse high-dimensional data can further our understanding of the ECG, adding to the earlier undertaken snapshot analyses of individual ECG components. We anticipate that our comprehensive resource will fuel in silico explorations of the biological mechanisms underlying cardiac traits and disorders represented on the ECG. For example, known disease causing variants can be used to identify novel morphological ECG signatures, which in turn can be utilized to prioritize genetic variants or genes for functional validation. Furthermore, the ECG plays a major role in the development of drugs, a genetic assessment of the entire ECG can drive such developments. d Main An enhanced understanding of the influence of genetic variants on the complete cardiac cycle represented by the ECG could generate new hypotheses on cardiac physiology, disease and effects of drugs. To better characterize the impact of genetic variants on the ECG, we obtained all ee,"fg d-lead ECGs of the UK Biobank that contained raw signal data necesserary for the analysis. After individual level and population level quality control to remove abnormal beats and ECGs d (Online Methods), id,egi individuals remained for the primary analyses. The primary ECG morphology phenotype was constructed by dividing one representation of the averaged cardiac cycle on the ECG into jgg temporal data points,as dictated by the data sampling frequency of the stored ECG recording. To study possible effects of heart rate, we performed additional, secondary, analyses in which we normalized that representation for the individual beat-to-beat variation (the RR-interval). To demonstrate that this approach also captures the classical ECG traits, we used previously described genetic variants in aggregate and isolation to visualize their morphological effect on the ECG k-"d . By plotting jgg association signals of each datapoint as -log"g P-values along the time axis of one beat ( Fig. 2 and Supplementary Data 2), we found that the polygenic risk score of PR-interval associated with a shift of the P-wave; the polygenetic risk score of QRS-duration associated with Q and S-wave durat...