Identification of determinants of sudden cardiac death and arrhythmias using population studies

Main Aim: To identify the genetic and clinical determinants and predictors of sudden cardiac death and arrhythmias

Sudden cardiac death is a major cause of deaths from cardiovascular origin. Indeed, up to half of the deaths from cardiovascular diseases are caused by sudden cardiac death. Prediction of sudden cardiac death is crucial in prevention of these deaths. Modifiable and non-modifiable risk factors are recognised for sudden cardiac death. Examples of modifiable risk factors includes hypertension, diabetes, and electrocardiographic abnormalities such as long QT syndrome. Factors such as problems in heart muscle or stenosis of aorta are not modifiable. Identification of novel determinants of sudden cardiac death is one step forward in prediction and possible insight into treatment options.

This PhD project aims to investigate genetic and clinical determinants and predictors of sudden cardiac death using statistical analysis methods. We will use data from the UK Biobank on 500,000 individuals. This project mainly involves data analysis and learning various skills in epidemiology and statistical analysis. Individuals with first degree at 2:1 or above with/without MSc degree or first degree at 2:2 with MSc degree at Merit or above in the fields related to data analysis such as data science, epidemiology, population genetics, statistics, or related disciplines and those who are experienced working with big data are encouraged to apply. You will learn techniques in analysis of data such as regression models, mendelian randomisation, polygenic models, risk prediction or machine learning. The duration of this PhD project is three years, and it will be supervised by Dr Raha Pazoki.

If you are interested to apply for this PhD project or if you prefer a one-year MPhil on a similar topic, please contact Dr Pazoki directly to get advice on the next steps.

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

1. Contact the supervisor by email or phone to discuss your interest and find out if you would be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)

Raha Pazoki - Raha Pazok MD PhD FHEA is a medical doctor and an epidemiologist. She studied Epidemiology at the Netherlands Institute for Health Sciences (NIHES) and in the University of Amsterdam. She worked with various cohort and case control studies such as the Arrhythmia Genetics in the Netherlands (AGNES), the Rotterdam Study, the Airwave Health Monitoring Study and the UK Bio bank. In 2016, she joined the Department of Epidemiology and Bio-statistics at Imperial College London as a Research Associate. In 2020, she started a Teaching & Research academic position at Brunel University London. Dr Pazoki specializes in the field of health data research, with a primary focus on the epidemiology of cardiometabolic diseases. She holds a particular interest in exploring causal inference and precision medicine by leveraging genomics and extensive health data sets with sample sizes exceeding 500,000 individuals. Her expertise spans various domains, including precision medicine, global health, interventions, and the application of artificial intelligence for predicting health outcomes. She harbors a keen interest in identification of the relationship between circulating molecules and biomarkers, nutrition, lifestyle choices, genetic factors, and their collective contribution to the modulation of health risk factors and outcomes. She was the first to identify 517 novel genetic loci associated with liver enzymes and the first to show the causal effect of liver dysfunction on cardiovascular diseases. In addition, she is the first to show the effect of the alcohol consumption WDPCP gene in lipid metabolism, and liver cirrhosis.   -