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Health data analysis to identify the role of gene and environment in risk of cardiovascular diseases

Main Aim: To identify the role of environment and genes in risk of cardiovascular disease

Cardiovascular diseases including hypertension, myocardial infarction, heart failure, and stroke are the leading causes of mortality worldwide and are expected to keep rising. Modifiable and non-modifiable risk factors are recognised for cardiovascular diseases. Examples of modifiable risk factors include diet, sedentary lifestyle, and smoking. Sex, age, and genetic factors are among non-modifiable risk factors. Genetic factors can help us classify individuals in population into high risk and low risk groups. We can then target appropriate preventive strategies to these different groups accordingly and decrease burden of cardiovascular diseases.

This is an umbrella project for a series of PhD projects aiming to investigate the role of various genetic and environmental factors (e.g alcohol consumption, smoking, or environment affecting mental health) in cardiovascular diseases. We will use data from the UK Biobank on 500,000 individuals and analyse using statistical analysis methods and therefore you are required to have prior experience of working with a statistical software such as SPSS, SAS, STATA, or R. The projects involve working with human data and learning various skills in epidemiology and statistical analysis. Individuals with MSc degree in fields related to data analysis such as Data Science, Genomics, Epidemiology, Population Genetics, Statistics, and Bioinformatics or related disciplines and those who are experienced working with big data are encouraged to apply. Depending on the topic you choose, you will learn techniques such as mendelian randomisation, polygenic models, risk prediction and machine learning. These projects will be supervised by Dr Raha Pazoki. Please contact Dr Raha Pazoki for an informal discussion and instruction on how to move forward.

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: 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.   -