A multi-omic investigation of Major Depressive Disorder and differential antidepressant response

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Informatics


This project aims to disentangle the biological pathways underlying Major Depressive Disorder (MDD) and differential anti-depressant response through the analysis of multi-omics data. Major Depressive Disorder (MDD) is a complex condition, primarily characterised by a persistent low mood, and is currently one of the leading causes of disability worldwide. Conventional antidepressants, which are widely prescribed, are inherently limited as approximately 40% of those diagnosed with MDD do not respond to antidepressant therapy. The development of these antidepressants was based off serendipitous observations of anti-depressant effects, rather than a hypothesis-driven drug design. MDD is currently diagnosed based on self-disclosed symptoms and observed signs and is understood to identify a syndrome that groups together clinically heterogeneous individuals with differing aetiologies. An increased mechanistic understanding of MDD could enable progress in the identification of more efficacious drug targets, novel biomarkers for patient stratification for existing therapies, and biologically founded subtypes of MDD. Genetic studies of MDD have identified 100s of genetic risk variants of small effect sizes, converging on pathways of altered gene and protein tissue expression in the disorder. Therefore, the integration of multi-omics data may enable the identification of intermediate mechanisms and a means of identifying the biological basis of MDD as well as the mode of action and response to antidepressants. This project will explore the antidepressant response phenotype, using available electronic health record data in Generation Scotland. The antidepressant response phenotype will be associated with other phenotypic measures in a phenome-wide association study (pheWAS), and genotype data in a genome-wide association study (GWAS). Linking the genetic contributions of MDD and antidepressant response to functional biological pathways will be through multi-omics analysis, integrating polygenic risk scores (PRSs) with epigenetic methylation data, and proteomic data, which are both available in Generation Scotland. Furthermore, a multi-omic analysis will be performed on the MDD phenotype itself, using the most recently released GWAS from the Psychiatric Genomics Consortium.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S02431X/1 01/04/2019 30/09/2027
2422814 Studentship EP/S02431X/1 01/09/2020 31/08/2024 Ella Eve Davyson