The genetic architecture of heart rate and the electrocardiogram at rest and during exercise

Heart rhythm abnormalities (beating too fast or too slow) affect more than 2 million people per year in the UK, and the consequences are a significant clinical burden. At the most malignant end of the spectrum this can result in people dying suddenly but other patterns of heart rhythm abnormality are also a leading and underestimated cause of stroke. There are data indicating genes are important, but our knowledge is limited. Heart rhythm abnormalities can be detected as changes in electrical signals, and these are identified using an electrocardiogram (ECG), a test that shows the hearts electrical activity as line tracings on a piece of paper. Prior work has indicated that measures taken from the ECG, for example heart rate, during and after exercise are strongly associated with the risk of sudden death. We propose to study the effects of exercise on heart rhythm, and will determine the genes influencing this response, as we believe exercise will amplify inherited differences. UK Biobank is a national resource for scientific research, and it has ~ 95,000 individuals who have had an exercise ECG. In a pilot study we have derived a series of measures from the ECG taken at rest, during exercise and after exercise using custom-made methods from 85 individuals. The results indicate that we can successfully extract the exercise ECG measures we are interested in. We now wish to expand the study to everyone who has had an exercise ECG, and perform tests to assess if exercise ECG measures are inherited, and further genetic studies to discover new genes. The results from this study will provide important information on genes influencing our response to exercise and recovery that will provide new insights into mechanisms, and the project also has the potential to find new genes that we can target to develop new therapies.

Rate and autonomic dependent changes in cardiac electrophysiology are fundamental in the genesis of many cardiac arrhythmias, including atrial and ventricular fibrillation. Genetic factors are known to be important, however we have limited knowledge of their identity. We wish to study the effects of exercise on cardiac excitability and establish the genetic architecture of dynamic electrophysiological parameters apparent on exercise. The results will reveal genes influencing our cardiovascular response to exercise and recovery. This knowledge will provide new insights into the molecular mechanisms of arrhythmias, and has the potential to identify drug targets for arrhythmia, and may also permit risk stratification for patients.

Hypothesis: There is a specific set of genetic loci that determine human cardiac electrophysiological parameters influenced by exercise.

Aims:
1.To derive resting and exercise ECG traits using bespoke algorithms.
2.To perform heritability analysis of dynamic electrophysiological parameters.
3.To perform GWAS to further understand the genetic architecture of resting and dynamic ECG traits.

Methodology: UK Biobank has exercise ECG data acquired from ~ 95,000 individuals who have corresponding genomic data, this offers a unique opportunity to test this hypothesis. In a pilot study, we have successfully extracted a range of dynamic ECG parameters from data on 89 individuals using bespoke algorithms. This project will expand analysis to all exercise ECGs enabling heritability analyses for ECG dynamic phenotypes for the first time. This will be complemented with existing work, particularly regarding resting heart rate data from genetic consortia (where we co-lead analyses).

Academia:
We expect our project to have a strong direct impact in the academic community in near and short term. Our work will generate new scientific knowledge about the genetic determinants of heart rate and dynamic electrophysiological parameters, and therefore basic scientists, geneticists and researchers in electrophysiology and cardiology will all benefit from the research outputs. We have an agreement with UK Biobank to deposit results of the ECG analysis, including heart rate profile and dynamics of the most important cardiac intervals during exercise. This will be valuable information for anyone working in cardiac epidemiology that has expertise to develop improved strategies to combat and predict sudden cardiac death. Our detailed analysis of the pattern of response of heart rate and cardiac activation/repolarization to exercise is expected to be of interest for researchers in cardiology. This analysis performed at large scale on this unique data-set will help to define the normal and abnormal dynamic response to exercise and this will be of interest to clinicians. Over the three-years, we will improve existing software for ECG analysis and develop new statistical techniques related to big-data analysis. This work will be of also be of great value for researchers working in biomedical engineering.


Commercial, policy makers and regulators:
The results from this project will not be clinically or commercially relevant in the near or short term. However, the methods and results from the research could help in the predicting sudden cardiac death based on dynamic electrophysiological markers, and may identify new therapeutic targets. We would be in a position to follow this up as we have established collaborations with both academia and industry. We do not expect any major impact of our research findings on clinical practice in the near or short term, however with the big data-sets we are analyzing and the rich phenotypic dataset we have access to in UK Biobank it maybe that the genetic modifiers we identify will be useful for diagnosis and treating disease earlier and more efficaciously than is currently possible.