We are pleased to be offering a PhD studentship funded by the NIHR Barts Biomedical Research Centre - a partnership between Barts Health NHS Trust and Queen Mary University London funded by the National Institute for Health Research. The studentship is open to Home/EU applicants.
The William Harvey Research Institute, the UK’s largest academic pharmacological research institute has a long history of training talented scientists for careers in cardiovascular genetics and translational research. Our NIHR Barts Biomedical Research Centre is running a world-leading program in elucidating the molecular basis of cardiometabolic traits including heart disease and use this knowledge to improve disease prediction and prevention. We are seeking an outstanding graduate with a strong interest in statistical genetics and machine learning approaches to work on ‘Investigating the genetic interplay between adult height, adiposity and cardio-metabolic traits’
Background: Evidence from observational studies suggests that height is associated with different disease outcomes, including Coronary Artery Disease (CAD). Recent work shows that the association between height and CAD seems to be mediated by lung function (Marouli et al). It is well recognized that when two clinical variables are correlated in the population, this does not imply that one causes the other. Defining causal factors in human disease is challenging but naturally occurring genetic variation and pharmacologic intervention studies represent two suitable approaches. In situations where randomized trials are inappropriate or impossible, Mendelian Randomisation (MR) provides a good alternative to study the causal relationship between a trait and a disease outcome. Apart from height, other adiposity measures including body mass index and waist to hip ration have been shown to be associated with different outcomes, but the causal and mediatory relations of these observations has not been yet elucidated. There is still a component of anthropometric trait heritability that is missing, and current efforts under the international GIANT (Genetic Investigation of ANthropometric Traits) consortium involving data from circa 2 million individuals are addressing this gap.
PhD Project: The principal aim of this project is to use state-of-the-art analytical approaches to understand the complex relationships between anthropometric traits and adiposity on the risk of cardio-metabolic outcomes. The project will investigate in addition to CAD, a number of other cardiometabolic diseases (CMD) as well as risk factors such as lipid levels, blood pressure, lung function, glycaemic and metabolism related traits. Additional risk factors will include lifestyle and socio-economic status variables.
Training will involve the analysis of large biomedical datasets and interpretation of genome-wide association studies, Mendelian Randomisation (MR), causal inference and mediation approaches. The student will have the opportunity to interact with the GIANT consortium analysis group and the MR/PheWAS/Pleiotropy working group co-led by Dr Marouli. Different MR methods will be tested to establish causality between anthropometric traits and CMD outcomes. As pleiotropy is a critical factor when studying the molecular basis of health and disease, the student will investigate the extent of correlated genetic effects. Mediation modeling will follow to identify the factors that have a causal effect on the risk of cardio-metabolic disease. This work can be extended to ancestries other than Europeans by accessing the GIANT data from East- and South-Asians, African Americans and Hispanics. Furthermore, the project will access data from the East London Genes and Health (ELG&H) study, a large community-based cohort of Pakistani and Bangladeshi heritage volunteers.
Through this project the student will be offered an excellent training in computational biology and statistical genetics by harnessing “big data”. The student will have hands-on experience of several world-leading datasets and will be part of a supportive, dynamic and successful team of researchers. The student will receive mentoring and will develop a unique set of skills incorporating genetics, genomics and skills in large-scale data analysis, which are relevant to clinical research. This studentship will equip the successful applicant with the necessary skills to bridge the gap between data analysis and clinical applications along with experience with world-leading data-sets. As well as the details described above, students will have access to high-quality training in scientific and generic skills, as well as access to a wide-range of seminars and training opportunities organised by Queen Mary University of London. The student will be encouraged to develop management and leadership skills through engagement in scientific discussions within the LSI Genomics community, reporting scientific results in meetings and manuscript preparation. At the end of the PhD they will have an excellent basis for a career combining data analysis and biostatistics with clinically relevant research.
Primary Supervisor: Professor Panos Deloukas
Secondary Supervisor: Dr Eirini Marouli
The successful applicant will hold, or expect, a first or an upper second class honours degree in the fields of statistics, computer science and/or bioinformatics. Skills in machine learning will be a plus. Applications are particularly welcome from individuals with a relevant research Masters degree.
Applicants for whom English is not a first language will also require a minimum IELTS score of 6.5 or equivalent, unless their undergraduate degree was studied in and awarded by an English speaking country. For more information on acceptable English language qualifications please click here.
You will be based in the attractive Charterhouse Square campus in the City of London with access to exceptional scientific and recreational facilities. Funding is for 3 years and includes:
• A tax free annual stipend of £17,000 p/a
• Project consumables
• Tuition Fees (up to the Home/EU rate only)
To apply please complete the online PhD application form providing the following documents:
• Your CV
• Statement of purpose
• Details of 2 academic referees.
• Copy of your transcript(s), including a breakdown of marks
• Copy of your passport
• If applicable, proof of English proficiency
Please ensure you provide all supporting documents, as we are unable to consider incomplete applications.
For informal inquiries please contact Dr Eirini Marouli [email protected]
or Prof Deloukas [email protected]
Please indicate on your application form under the Funding section (page 6) that you are applying for funding under ‘WHRI/BRC’.
The closing date for applications is 12th of August 2019.