The details of the available projects for the Population Health Sciences theme are outlined on this page. You can find other projects on the Infection, Immunity, Antimicrobial Resistance & Repair and Neuroscience & Mental Health pages. A full list of our available projects can be downloaded below.

GW4 BioMed2 MRC DTP – Full list of available projects 2023-24

For full project descriptions, including contact details for the lead supervisor, click the link on the project title.

Applications to the GW4 BioMed2 MRC DTP will be accepted via this online form until 5pm on Wednesday, 2nd November 2022. For guidance on the application criteria and decision timeline, please see the information here.


The natural environment and depression: triangulating the role of stress biomarkers across geographically diverse regions

Urbanicity is an important risk factor of mental illness. Furthermore, this link may be mediated by biological markers of stress including inflammation and epigenetic processes. However, the relationship between physical features of the living environment, biomarkers of stress and mental health in more geographically diverse regions (e.g. Australia, South America, Europe) remains unknown – a key limitation, which this project will address.

Lead Supervisor: Dr Esther Walton
Institution: Bath

Using multi-omics to improve prediction and early diagnosis of pregnancy complications

Pregnant women are among the most neglected populations in medical (and genomics) research. This PhD will aim to address this research gap by promoting innovation in risk stratification and early diagnosis of APOs. This is key to inform tailored antenatal care and reduce unnecessary interventions and costs to health services, leading to improvements in equitable and sustainable health. 

Lead Supervisor: Dr Maria Carolina Borges
Institution: Bristol

Using human genetics to identify novel biomarkers for enhanced prediction and early detection of cancer

The student will use a novel human genetics approach (“Reverse Mendelian randomization”) to identify plasma protein markers for early detection of cancer. Findings will be validated using direct protein measures in the UK Biobank study of 500,000 adults. The clinical application of these findings will be explored by integrating biomarkers into cancer prediction models with NHS data. These findings will guide and strengthen development of cancer prediction models.

Lead Supervisor: Dr Philip Haycock
Institution: Bristol

Estimating the global cancer burden due to low levels of physical activity

The student will estimate the overall global burden of cancer risk which is due to low levels of physical activity using a combination of: i) observational and Mendelian randomization analyses to estimate of the causal effect of physical activity on cancer risk, and ii) global cancer surveillance data and published data on physical activity rates to estimate population attributable risks.

Lead Supervisor: Prof Sarah Lewis
Institution: Bristol

Using machine learning to facilitate rapid and efficient responses to gastrointestinal disease outbreaks

Illness caused by the consumption of contaminated food is a major threat to public health and requires significant public resources to identify the source of infections. The successful candidate will work with the UK Health Security Agency to develop cutting-edge machine learning tools for the prediction of foodborne disease outbreaks. These tools will be used to support public health decision making and facilitate rapid response to future outbreaks.

Lead Supervisor: Prof Kristen Reyher
Institution: Bristol

Understanding psychiatric outcomes in children born with cleft lip and/or palate using genetics

Cleft of the lip and/or palate is a common birth defect which can affect appearance, speech, hearing, dentition and mental health. This PhD will investigate risk of adverse mental health outcomes in cleft and their genetic and non-genetic causes and provides the opportunity to develop into one of few experts globally with in-depth understanding across cleft, genetics, genetic epidemiology and psychiatry.

Lead Supervisor: Dr Evie Stergiakouli
Institution: Bristol

Post diagnostic risk stratification for screen detected prostate cancer using routinely collected clinical variables

To determine if measurements at diagnosis of screen-detected prostate cancer (age, serum prostate specific antigen (PSA) level and tumour characteristics) can help doctors predict individual long-term outcomes.  High-quality information will: i) enable better diagnostic/treatment decisions by men and doctors through greater understanding of prostate cancer outcomes after screening ii) could be used to update NHS post-diagnostic risk stratification recommendations.

Lead Supervisor: Dr Emma Turner
Institution: Bristol

Impacts of air pollution on neurodevelopment and neurodegeneration

There is increasing evidence that air pollution affects neurodevelopment and neurodegeneration. However, the mechanisms that mediate these relationships are unclear. This project will exploit large-scale data on air pollution and clinical and educational outcomes, and explore the role of genetic, epigenetic and brain imaging traits in this association. Results will provide new insights into how the environment shapes outcomes both early and later in life. 

Lead Supervisor: Prof Stephanie von Hinke
Institution: Bristol

Calcium signalling in In-Vitro Fertilization: developing a non-invasive diagnostic tool

In-Vitro Fertilization is the primary treatment of infertility, with ~2.5 million cycles performed annually. Success rates are, however, declining partly because of waiting 5-6 days to select the best embryo to transfer to the woman. A rapid (day 1) quantitative indicator of embryo viability, based on experimental data, will be developed through modelling calcium signalling in fertilising eggs and their associated movements and will be shared with IVF clinicians. PLEASE NOTE: This project is suitable for candidates with a strong quantitative background, with a degree in mathematics, physics, engineering or computer science.

Lead Supervisor: Dr Katerina Kaouri
Institution: Cardiff

Identifying targets to prevent early onset depression in the children of depressed parents 

One in four children and young people in the UK have a parent with a diagnosis of depression.  This project aims to identify the reasons why children with a depressed parent may develop depression themselves (inter-generational transmission).  It will use a combination of data sets to address this question (health record and detailed longitudinal data).  There will be opportunities to involve young people, third sector and government organisations. 

Lead Supervisor: Prof Frances Rice
Institution: Cardiff

Personalising Parkinson’s:  Identification brain-first versus body-first subtypes

Medical imaging and biopsy studies suggest that Parkinson’s disease starts at different places in the body: in some Parkinson’s patients, the brain is damaged (brain-first subtype) before the peripheral nervous system and in others, the opposite is seen (body-first subtype). To determine Parkinson’s body vs brain subtypes and their consequence, we will exploit big data in healthcare, genetics and imaging.

Lead Supervisor: Prof Caleb Webber
Institution: Cardiff

Using genetics to understand the links between pre-term birth and lung disease in childhood

Babies who are born prematurely are more likely to develop lung disease, but the underlying mechanisms are poorly understood. This project will combine world-class training in genetics, epidemiology and data science to test the hypothesis that genes and intrauterine exposures which lead to prematurity also lead to reduced future lung function.

Lead Supervisor: Dr Robin Beaumont
Institution: Exeter

The role of prenatal inflammation on neurodevelopmental disorders

Inflammation during pregnancy as a result of stress and infection is linked could increase the risk of autism and attention-deficit hyperactivity disorder in the child, potentially by influencing their brain development. In this project, the aim is to decipher this link by analysing large datasets from human population studies, including genetic and other molecular markers.   

Lead Supervisor: Dr Doretta Caramaschi
Institution: Exeter

Integrative analysis of whole genomes and transcriptomes from multiple cell types in rare disease patients

The use of Whole-Genome Sequencing has increased the diagnostic yield for rare diseases. However, even WGS fails to identify the genetic cause in about 50% of patients. To increase this yield, the NIHR National BioResource launched the RNA phenotyping initiative which adds RNA-seq and proteomic to WGS. The project aims to develop approaches for integrating these data to discover new causes of disease in a unique cohort of a thousand rare disease patients.

Lead Supervisor: Prof Mattia Frontini
Institution: Exeter

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