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Interplay between lifestyle factors and genetic factors modulating coagulation in risk of myocardial infarction


   Biosciences

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  Dr R Pazoki  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

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.


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