Applications for our 2020/2021 recruitment round are open from 29th September 2019 until 5pm on Monday 25th November 2019.
Please find the details of the available projects for the Population Health theme outlined below. A full list of our available projects can be downloaded below.
Full project descriptions, including contact details for the lead supervisor, can be downloaded by clicking on the link in the project title.
Students can apply to the GW4 BioMed MRC DTP via this online survey by 5pm on 25th November 2019.
This project focusses on healthy mental ageing in the population and in individuals with schizophrenia. The student will investigate how brain age (derived from brain imaging scans) links to physical health; the genetic architecture of brain ageing; and how schizophrenia and modifiable lifestyle factors impact accelerated brain ageing.
Lead Supervisor: Dr Esther Walton
In randomised trials comparing two or more treatments, patients often change from the treatment they were randomised to receive during the trial. This project will explore how methods developed for quantifying the causal effects of exposures or treatments in non-randomised studies can be used and extended to disentangle the effects of each treatment in randomised trials.
Lead Supervisor: Dr Jonathan Bartlett
This study provides a unique opportunity for a student to use data from two clinical trials to determine how weight loss effects metabolic pathways in an intervention-specific manner. Students will investigate how metabolic signatures of weight change relate to downstream disease and the role of body mass index as a causal risk factor for disease.
Lead Supervisor: Professor Nicholas Timpson
Cohort studies are used to inform about clinical practice but face problems of missing data arising through non-response and dropout. Common solutions are usually not applicable when data are missing not at random (MNAR). We will develop Bayesian methods and instrumental variable methods to analyse data MNAR. Our findings will have a major impact since many researchers use resources such as UK Biobank.
Lead Supervisor: Dr Rachael Hughes
This is an exciting opportunity to explore statistical methods for triangulation: using and synthesising multiple studies strategically to answer a single question. The project will primarily use Bayesian synthesis methods, including incorporation of external information through prior distributions, applying them to real-life problems in epidemiology.
Lead Supervisor: Professor Julian Higgins
Fasting is now popular form of dieting. However, very little is known about its longer-term effects. One idea is that we learn from fasting. Specifically, it changes our perception of appetite and the ‘need’ to eat at regular times of day. This project will address this question and consider whether a single fasting episode might benefit healthy weight maintenance, both in children and their caregivers.
Lead Supervisor: Professor Jeff Brunstrom
Omic technologies have made it possible to systematically scan a broad range of biological variation to identify biomarkers. Recent evidence suggests that DNA methylation (DNAm) profiles in peripheral tissues can capture much of this variation. We propose to use multi-omic, multi-tissue datasets to generate DNAm models of biological variation in peripheral tissues that can be used to generate improved biomarkers of disease risk.
Lead Supervisor: Dr Matthew Suderman
Obesity and mental health problems are global issues, but the links between them are complex with sociocultural factors playing a key role. The student will train at 2 world leading centres, utilising global studies (e.g. UK Biobank) and genetic approaches to understand complex relationships between obesity, sociocultural factors and mental health.
Lead Supervisor: Dr Jess Tyrrell
DNA methylation is an accurate biomarker to lifestyle factors (smoking, alcohol use) and of chronological age and cell counts. Objective assessment of these risk factors can avoid biases with self-reported measures and may reveal disease mechanisms. This studentship will apply state-of the art genetic and genomic epidemiological approaches to address questions about the molecular mechanism underlying disease risk factors predicted with DNA methylation.
Lead Supervisor: Dr Josine Min
In this project the student will investigate whether multiple risk factors influence disease collectively or independently of one and other. Doing so for both early life and adult traits can discern whether the detrimental impact of childhood exposures can be alleviated through lifestyle changes. The findings will help elucidate causal pathways to improve healthcare.
Lead Supervisor: Dr Tom Richardson
This project will suit a candidate who wishes to undertake mixed methods training in both epidemiology and qualitative research, and has an interest in ADHD. You will study the impact in adulthood of long-term (>3 years) pharmaceutical treatment for ADHD in childhood. You will train in up to date epidemiological techniques, and thematic analysis.
Lead Supervisor: Dr Ginny Russell
People who inject drugs have increased risk of hepatitis C virus (HCV) and drug related death (DRD). In the UK, 85% of HCV is transmitted through injecting and DRD have increased 3-fold in 10 years. This PhD will develop mathematical models to determine the effect of interventions on HCV and DRD syndemics and find which combination has most impact.
Lead Supervisor: Professor Matthew Hickman
Diagnostic test accuracy reviews provide important information on the accuracy of tests but usually take a lot of time and money to complete. This PhD project will apply statistical methods to determine whether restricted searches and sample size thresholds can be applied in DTA reviews to optimise the review process and reduce resource requirements.
Lead Supervisor: Dr Penny Whiting
This is an exciting cross-disciplinary project to investigate the determinants of psychological traits and mental illness, to enable improved interventions to reduce risk. This will be approached in an innovative hypothesis-free manner – searching across a wide range of phenotypes to identify determinants of psychological / psychiatric outcomes.
Lead Supervisor: Dr Louise Millard
This project will examine the role of interactions between genes and early life health, economic and policy environments in shaping later life outcomes. By combining methods from genetics and social science, it goes beyond the old ‘nature vs. nurture’ debate and instead explores how the two interact in creating inequalities in health and disease.
Lead Supervisor: Professor Stephanie von Hinke
Medicines errors caused by poorly written professional guidance will be investigated by first statistically combining the results of past research & then developing an artificial intelligence algorithm to analyse a database of NHS error reports. There will be a placement involving medicines safety analysis in the NHS. The student will gain skills in artificial intelligence & statistics for large data sets, & knowledge of NHS medicines safety.
Lead Supervisor: Dr Matthew Jones
With an increasing number of tests available for many diseases, methods are needed to determine the ‘best’ test or combination of tests. Data on test accuracy are often available from multiple studies and statistically ‘pooled’ using meta-analysis. Working with case studies including tests for coeliac disease, this project will evaluate and develop advanced meta-analysis models and provide guidance to systematic reviewers on what data are required.
Lead Supervisor: Dr Hayley Jones