PhD Studentship: Using Molecular and Clinical Data to Predict Outcomes to Treatments for Depression. MRC GW4 BioMed DTP PhD Studentship 2024/25 Entry, PhD in Psychology

Project Title: 

Using molecular and clinical data to predict outcomes to treatments for depression. MRC GW4 BioMed DTP PhD studentship 2024/25 Entry, PhD in Psychology

The GW4 BioMed2 MRC DTP is offering up to 22 funded studentships across a range of biomedical disciplines, with a start date of October 2024.

These four-year studentships provide funding for fees & stipend at the rate set by the UK Research Councils, as well as other research training & support costs, and are available to UK and International students.

About the GW4 BioMed2 Doctoral Training Partnership

The partnership brings together the Universities of Bath, Bristol, Cardiff (lead) and Exeter to develop the next generation of biomedical researchers. Students will have access to the combined research strengths, training expertise & resources of the four research-intensive universities, with opportunities to participate in interdisciplinary & 'team science'. The DTP already has over 90 studentships over 6 cohorts in its first phase, along with 38 students over 2 cohorts in its second phase.

The 80 projects available for application, are aligned to the following themes;

• Infection, Immunity, Antimicrobial Resistance & Repair
• Neuroscience & Mental Health
• Population Health Sciences

Applications open on 4nd September 2023 and close at 5.00pm on 1st November 2023.

Studentships will be 4 years full time. Part-time study is also available.

Project Information

Research Theme: Neuroscience & Mental Health

Summary:

Up to 50% of people with depression do not benefit from the pharmacological & psychosocial treatments initially prescribed. This often results in the need to switch treatments several times before finding the optimal therapy. In this project you will develop and compare markers for antidepressant treatment efficacy focussing on integrating clinical, demographic, genetic and epigenetic characteristics using machine learning across studies and outcomes.

Description:

People with depressive symptoms often need to switch antidepressant medications several times & combine those treatments with psychological therapies because of lack of effectiveness. The evidence so far on the link between patient’s characteristics (e.g. sociodemographic or genetic) & response to treatment is not sufficient to be usefully implemented clinically. The project aims at exploring potential biomarkers for antidepressant treatment efficacy using machine learning to ultimately better inform clinicians and people with depression on the choice of therapy.

The project will focus on DNA methylation in peripheral blood initially as it is both under the influence of genetic predispositions & environmental factors, while being stable and easily measurable. To achieve a large-scale study, we will use DNA methylation data from a variety of existing human studies across population studies and clinical trials. DNA methylation data obtained via microarrays will be integrated with other clinical, sociodemographic and genetic characteristics using a range of statistical and computational methods to predict antidepressants efficacy across studies & outcomes. Over the course of the studentship and depending on the interests of the student, they will have the opportunity to:

1. Identify patterns in DNA methylation that are linked to improvement in depressive symptoms in response to pharmacological treatments.
2. Develop & test a DNA methylation biomarker that predicts symptoms improvement in response to pharmacological treatments.
3. Combine the DNA methylation biomarker with other data (e.g. severity of symptoms) and compare its effectiveness in predicting reduced depressive symptoms.
4. Compare the predictive validity of biomarker across ages (e.g. childhood vs adulthood).
5. Develop and test a clinical marker to predict improvement in response to psychological therapies (e.g. CBT).
6. Investigate the acceptability & utility of predictive markers for mental health treatments from service providers and users via stakeholder meetings.

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