Depression is a complex and pervasive disorder, with significant social and economic burden. Crucially, depression rates appear to be rising around the world. Currently, our understanding of depression (and therefore our ability to accurately treat or prevent it) is limited by several interrelated challenges, including 1) heterogeneity in how identify and diagnose depression in people; 2) understanding of the timing and course of depression and how it changes over time and 3) what risk factors predict depression, it’s different sub-types and it’s course over time. There is no one size fits all approach to depression and innovative, multidisciplinary research is urgently needed to make significant progress in terms of stratification, understanding of risk factors and better overall prediction.This PhD will use powerful real-world data to identify sub-types of depression, and examine how a combination of genetic, biomarker and environmental data could underpin these sub-types of depression. The data included are UK Biobank (UKB, n=500K), Generation Scotland (GS, n=20K) and the Avon Longitudinal Study of Parents and Children (ALSPAC; n=30K). More datasets will be available as the PhD progresses.
This PhD will use a combination of approaches from different disciplines, including: epidemiology, statistics, social science, bioinformatics, genetics, public health and computational psychology. The student will gain experience of working with innovative methods including machine learning, deep learning and varying prediction analyses, alongside genetics, MRI imaging and longitudinal modelling to identify the most robust markers and best combinations of markers for different depression sub-types.
Extensive bespoke training will be offered across a variety of essential skills and across institutions with opportunities for international lab visits. This project provides an exciting opportunity to work on exciting real-world datasets, which will provide the student with a unique set of interdisciplinary and translational skills that are transferable across a number of disciplines.
