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.

心电图 (ECG) 是对各种心脏疾病最有用的非侵入性诊断测试之一。分析心电图的传统方法侧重于各个部分。在这里，我们对英国生物库中的 77,190 份心电图进行了全面的深度表型分析，涵盖了心脏传导的完整周期，产生了 500 个时空数据点，涉及 1000 万个遗传变异。除了表征传统心电图片段的多基因风险评分外，我们还确定了 300 多个与心电图的高维表示在统计上相关的基因位点。我们建立了扩张型心肌病的遗传心电图特征，与BAG3、HSPB7/CLCNKA、PRKCA、TMEM43和OBSCN位点具有疾病风险，并在一个独立队列中证实了这种关联。总之，我们的工作表明，对整个心电图的高维分析为研究心脏生物学和疾病以及促进药物开发提供了独特的机会。

本文的透明同行评审过程记录包含在补充信息中。