Interplay between lifestyle factors and genetic factors modulating coagulation in risk of myocardial infarction
Cardiovascular diseases including 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 and smoking. Sex, age, and genetic factors are among non-modifiable risk factors. Identification of risk factors is important in the prevention of cardiovascular disease. For instance, 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 the burden of cardiovascular diseases.
The most frequent form of cardiovascular disease is myocardial infarction that refers to an injury to the heart muscle due to blockage in coronary arteries that supply the heart with oxygen and nutrients. The main underlying mechanism for myocardial infarction is the formation of a blood clot inside coronary arteries on top of an atherosclerotic plaque. Coagulative factors are an important part of the process of clot formation. This PhD project aims to investigate the interactivity of genetic underpinning of coagulative factors and lifestyle behaviour in the occurrence of myocardial infarction.
We will use data from the UK Biobank on 500,000 individuals. This project involves 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 Genomics, Epidemiology, Population Genetics, Statistics, and Bioinformatics or related disciplines and those who are experienced working with big data are encouraged to apply. You will learn techniques such as analysis of interactions using regression models, mendelian randomisation, polygenic models, risk prediction and machine learning. This PhD project will be supervised by Dr Raha Pazoki.
Individuals with a 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 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 the 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:
- Contact the supervisor by email or phone to discuss your interest and find out if you woold 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.
- 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.
- Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.
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%.