Identifying causal risk factors for disease progression Research

The University of Bristol is offering a 3.5 year full time PhD in research around Population Health to start in 2019. This studentship is funded through GW4BioMed MRC Doctoral Training Partnership. It consists of full UK/EU tuition fees, as well as a Doctoral Stipend matching UK Research Council National Minimum (£14,777 p.a. for 2018/19, updated each year).
Additional research and training funding is available over the course of the programme. This will cover costs such as research consumables, courses, conferences and travel. Additional competitive funds are available for high-cost training/research.
The studentship is based at the Bristol Medical School. For further information please see the website below.
http://www.bristol.ac.uk/medical-school/
Many studies focus on risk factors for disease onset, however disease progression may be more important for treatment. This studentship provides cross-disciplinary training in state-of-the-art genetic epidemiological approaches (using large-scale datasets) and cutting-edge cell biology to address important questions underlying disease progression.
This studentship provides cross-disciplinary training in state-of-the-art genetic epidemiological approaches (under the supervision of Dr Lavinia Paternoster and Prof George Davey Smith at the Medical Research Council Integrative Epidemiology Unit, University of Bristol and Prof Tim Frayling at the University of Exeter Medical School) and cutting-edge cell biology (under the supervision of Professor Alastair Poole in the Faculty of Life Sciences, University of Bristol) to address important questions underlying progression of disease. The student will combine novel analyses of large-scale epidemiological datasets with detailed phenotypic cellular analyses. Background To date, the majority of studies to identify the causal risk factors for disease have focused on onset of disease. Whilst it has often been suggested that these will be informative for treatment, this is often not the case, and although such studies may be informative for prevention of disease, their utility in informing disease treatment is less clear. There is now growing interest in conducting genome-wide association studies (GWAS) and Mendelian Randomization (MR) analysis to identify genetic and causal modifiable factors which explicitly influence disease progression. Vascular disease (such as coronary artery disease and stroke) can occur as primary disease presentation, but can also develop as a secondary outcome in patients with non-vascular disease (such as Type 2 diabetes). Therefore, investigating the mechanisms by which this can occur is of great interest. Platelets play a key role in vasculature disease and so are a likely candidate for this process. The Aim and Approach The overall aim of this PhD is to apply (and potentially develop) novel methodology to identify causal risk factors for vasculature disease progression in patients with non-vasculature primary disease and determine the role of platelet function in this. The specific disease focus of this project would depend on the candidate's research interests, but could include e.g. type 2 diabetes or renal disease. Methods 1. GWAS will be conducted with disease progression outcomes, using available large-scale datasets (e.g. CHARGE consortium, UK Biobank, clinical trial data) dependent on the outcome of interest. There are several challenges with this type of analysis, including potential collider bias in case-only samples, which this studentship will seek to address. GWAS of potential risk factors may also be conducted or collated from existing published studies. 2. The GWAS results generated will be used to conduct MR analyses to investigate causal relationships between potential risk factors and disease progression. 3. Pathways that appear responsible for influencing disease progression will be interrogated functionally and biologically, using in vitro and in vivo approaches in cell and animal model systems.


Candidate requirements: Applications are welcome from high performing individuals across a wide range of disciplines closely related to natural sciences, biostatistics, genetics, bio-chemistry, mathematics and computer science who have, or are expected to obtain, a 2.i or higher degree. Applications are particularly welcome from individuals with a relevant research Masters degree.

How to apply: Please make an online application for this project here: https://www.findaphd.com/search/PhDDetails.aspx?CAID=2940&LID=287

Contact: Lavinia Paternoster [Email Address Removed]