Background: Quantitative phenotypes such as blood pressure (BP), electrocardiogram (ECG) and cardiovascular magnetic resonance (CMR) measurements play a critical role in the management of numerous cardiovascular diseases. We aim to provide an overview of the genetic basis of these endophenotypes.
Methods: The complexity and sophistication of phenotype derivation ranges from a well-calibrated BP monitor to convolutional neural networks for CMR image annotation. The genetics of multiple BP traits including systolic, diastolic and pulse pressure have been studied in large meta-analyses. Several studies have reported the genetic basis of ECG-derived traits, such as heart rate, QT interval, or T-wave morphology parameters. As for the CMR phenotypes, the first large-scale genome-wide association studies (GWASs) of left and right ventricular (LV and RV) measurements, including volume, mass, ejection fraction, have been conducted in the UK Biobank cohort.
Results: The largest study to-date of BP traits (N > 1 million) discovered 901 genetic loci harbouring known and novel BP-regulating genes, several of which are linked to existing drugs, that can be repurposed for BP treatment. Regarding the ECG indices, 437 independent signals have been reported for resting heart rate (N ~ 500,000), mainly modulating the autonomic nervous system, as well as up to 60 loci for QT or T-wave morphology traits (N ~50,000). The LV GWASs (N ~ 17,000) discovered 14 loci harbouring genes regulating the cardiac developmental pathways. The LV polygenic risk scores were predictive of heart failure events. The RV GWASs discovered 11 loci enriched with genes involved in sarcomere organisation and myofibril assembly.
Conclusion: Large-scale genetic analyses of quantitative cardiovascular traits have yielded hundreds of susceptibility loci, candidate genes and key biological pathways, which not only improve our understanding of their genetic architecture but also shed lights on potential novel therapeutic targets and inform the development of personalised risk stratification strategies.