Genetic analyses of ventricular depolarisation and repolarisation and prediction of cardiovascular risk.

Lead Research Organisation: Queen Mary, University of London
Department Name: William Harvey Research Institute

Abstract

Abnormal electrical conduction through the heart is associated with dangerous heart rhythms that can cause sudden death. At present, the exact causes of these abnormal heart rhythms are unknown, however research has shown that genetics plays a significant role in an individual's risk profile. Genes are made of DNA, and they provide instructions to make proteins which influence growth and development of all cells in the body. Small changes in the make up of a gene can alter its function or change the way it provides information to the body. It is becoming increasingly understood that a person's risk of sudden death is due to many small variations in genes combined rather than one single change.
Electrocardiograms (ECGs) are a non-invasive method of recording electrical activity within the heart. Different time points on the ECG refer to when the bottom chambers of the heart (ventricles) contract to pump blood around the body, or relax to their resting state. The QRS interval on an ECG is related to ventricle contraction, while the JT interval is specific for when the ventricles return to their resting state. The QT interval refers to the period from the beginning of the QRS interval to the end of the JT interval. Another important marker which is calculated from many leads on an ECG is spatial QRS-T angle. This represents electrical conduction in the heart in a three dimensional manner during ventricle contraction and relaxation and could represent different biological processes compared with other ECG parameters. Changes in the duration of these ECG parameters are associated with the development of abnormal heart rhythms and sudden cardiac death.
Previous smaller studies have identified genetic changes (variants) influencing the duration of QT, JT and QRS intervals however a large proportion of the genetic contribution to these ECG markers remains unexplained. This is likely due to the size of previous studies and lack of power to detect rare variants. We will conduct the largest study to date, for QT, JT and QRS intervals which will have greater power to detect variants that are as yet unidentified including less common or rare variants which may have a greater effect on the duration of these ECG traits.
We will also perform a study to determine the genetic contribution to spatial QRST angle, which has never been studied before. As it offers a global assessment of cardiac ventricular conduction compared with other ECG traits, we anticipate we will identify new pathways and biological mechanisms for the generation of abnormal heart rhythms. Significant genetic variants identified from these studies will be extensively investigated using publically available datasets to map variants to pathways in cardiac function and arrhythmia generation. These analyses will help to improve our understanding of the role of these genetic variants in causing abnormal heart rhythms and could give insights into how to prevent or treat them in the future. We will also test for association between genetic variants and clinical outcomes including hospital admissions for abnormal heart rhythms, changes in heart chamber dimensions on scans and the risk of heart attacks or death.
At present, current markers for predicting abnormal heart rhythms and sudden death are not specific or sensitive enough to be used to test the general population. This research is designed to identify new genetic contributions to abnormal heart rhythms in order to improve risk prediction for sudden cardiac death and other adverse cardiac events. It will help identify people in the general population who would benefit from early treatment or monitoring to prevent disease. The results will aid physician decision making and help us understand what influences the health of the general population and their risk of significant cardiac disease.

Technical Summary

Genome wide association studies (GWAS) for QT, JT and QRS interval have provided new information on the underlying mechanisms behind ventricular depolarisation and repolarisation. However, the heritability of these traits is not fully explained, and further investigation is required into the utility of adopting common genetic variants for cardiac risk prediction in the clinical setting. The aims of this project are to identify new variants to utilise in multi-biomarker risk prediction for adverse cardiac events.

Methodology
We will perform the largest meta-analysis of GWAS to date, of QT, JT and QRS intervals in ~300,000 individuals using 1000 genomes and HRC imputation in collaboration with the CHARGE EKG consortium. We will also derive spatial QRS-T angle from 12 lead resting ECGs acquired from UK Biobank, and perform the first ever heritability study and GWAS on this trait in ~100,000 individuals using BOLT-RMM and BOLT-LMM software respectively. Identified variants will be extensively annotated using bioinformatics methods and publically available datasets, which will provide insight into their putative function in electrophysiological pathways and identify candidate genes. Using identified variants, using genetic risk scores, we will test for association with cardiovascular outcomes including left ventricular measurements on cardiac MRI, hospital admissions for tachyarrhythmia and mortality.

Scientific and medical opportunities
This project will contribute to our understanding of the pathophysiology of arrhythmia generation and sudden cardiac death. It may identify new targets for medical therapy by novel drug development or existing drug re-profiling. The data will enable Mendelian Randomisation studies to test the effects of specific drug interventions on clinical outcomes. Finally through inclusion of new biomarkers and subsequent risk score development, risk prediction for adverse cardiovascular outcomes in the general population could be improved.

Planned Impact

Currently the genomics industry is focused on sequencing and with costs continuing to fall this will facilitate the clinical application of genomic medicine. Through the production of risk scores using genomic variants, the industry will benefit from utilising sequencing techniques for application in clinical medicine. This will influence the genomic strategy at a government and private sector level, and will increase the output from an industry which is already significantly contribution to the UK economy. With the rapidly expanding genomic industry, development of skills in bioinformatics and genomics is necessary to meet its growth. This project will offer the fellow intensive training in this field including statistical genetics, bioinformatics tools and the use of genomics in translational medicine.

New candidate genes and pathways will facilitate further academic and clinical research into their functional role in arrhythmia. The pharmaceutical industry will benefit from the identification of new pathways or clarification of the roles of others in arrhythmogenesis, to use as targets for novel drug therapy or existing drug re-profiling; a cost and time effective method of drug development. Our research will also enable Mendelian Randomisation studies to test the effects of specific drug interventions on clinical outcomes as undertaken by the pharmcogenetics group under Prof. Hingorani at UCL. This represents a highly cost effective strategy to plan and execute clinical trials at a population level utilising genomic data. By stratifying specific groups according to their genomic profile, patients most likely to benefit form an intervention can be identified and targeted for therapy. Such an approach has already been used in coronary artery disease and type 2 diabetes. This will facilitate the de-risking of extremely expensive clinical trials.

Currently, general population screening for sudden cardiac death is not recommended due to concerns regarding the sensitivity and specificity of current risk prediction models and the potential cost implications; as recently debated in parliment. It was however agreed that there is an economic and social benefit to improving models to be able to implement general screening in the UK. By improving models through our research with the addition of new bio-risk markers, including low cost electrocardiographic genetic traits, risk prediction will be improved and affordable, supporting policy making for sudden cardiac death screening in the general population. Risk calculators could be incorporated into apps or websites facilitating use by clinicans as seen with current hypertrophic cardiomyopathy sudden cardiac death risk tools (http://www.doc2do.com/hcm/webHCM.html). By supporting clinical decision making, unnecessary device implantations could be prevented, avoiding adverse consequences from procedural complications or inappropriate shock therapy. By improving policy and incorporation into NICE guidelines, the quality of life of those at risk will be improved by ensuring those who need therapy receive it.

Importantly, this research could reduce mortality from sudden cardiac death. By reducing morbidity and mortality from arrhythmias, the nation's health and wealth would improve and positively affect the economy by increasing the number of people available for work as lethal arrhythmias often strike otherwise well individuals with preserved heart function. This impact following completion of our planned research, could be realised in the medium term. Arrhythmia and sudden cardiac death is a significant cause of morbidity and mortality internationally and thus there is a significantly wider impact of our research, potentially affecting policy globally. Charities and public health organisations including the British Heart Foundation and Arrhythmia Alliance will be able to use our findings to facilitate education and prevention strategies amongst the public and clinicians.

People

ORCID iD

 
Title ECG phenotypes in UK Biobank 
Description Use of ECG signal processing algorithms in Matlab to derive ECG parameters from raw ECG data obtained from participants of the UK Biobank study ECG parameters derived will at the end of the project, be returned to UK Biobank for other researchers to utilise (as with genotype-phenotype association results) 
Type Of Material Data analysis technique 
Year Produced 2018 
Provided To Others? No  
Impact ECG parameters derived will at the end of the project, be returned to UK Biobank for other researchers to utilise (as with genotype-phenotype association results) 
 
Description CHARGE EKG Consortium 
Organisation Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE)
Country Global 
Sector Academic/University 
PI Contribution Development of analysis plan for largest ever GWAS of QT and JT interval. This is due for submission imminently to consortia members to move the project to the next stage.
Collaborator Contribution n/a
Impact No outputs thus far
Start Year 2017
 
Description Attendance at China Kadoorie Biobank Symposium 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Postgraduate students
Results and Impact Attendance at China Kadoorie Biobank symposium to discuss current and future potential work.
Year(s) Of Engagement Activity 2018
 
Description Electrogenomics group 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Study participants or study members
Results and Impact Presentation on the pilot study I completed, performing a GWAS on QT interval change on exercise. No significant variants were identified in this small pilot group however important learning points were identified which has assisted the group in performing a GWAS on the entire cohort. This includes how we were performing the signal processing, phenotype quality control and ajustment for confounding variables.
Year(s) Of Engagement Activity 2017