Identifying neurobiological consequences of genetic risk for schizophrenia on traits associated with Major Depressive Disorder (MDD)

Background
Major depressive disorder (MDD) is a heritable, disabling psychiatric disorder, renowned for clinical and aetiological heterogeneity. This heterogeneity has severely hampered progress in the understanding of the disorder. MDD is characterised by low mood, cognitive deficits and altered personality traits, such as neuroticism. These traits have a polygenic architecture and importantly also differ in cases and controls in several other major psychiatric disorders such as schizophrenia (SCZ) and bipolar disorder (BD). The degree to which a genetic risk for SCZ and BD contribute to mood, cognitive deficits and personality in MDD is unclear, and may also mitigate some of the heterogeneity seen in MDD.

The current proposal is based on our previous findings in two important cohorts. Firstly, in the Lothian Birth Cohort (LBC) we demonstrated that polygenic risk for SCZ is associated with lower cognitive ability in healthy controls at age 70 years, and with greater cognitive decline (1). Secondly, in another large population-based sample (Generation Scotland; GS), we found that mechanisms underlying cognitive impairments in MDD, specifically relating to processing speed, are related to increased genetic loading for SCZ that are over and above the impact seen in healthy controls (2).

Aims
This proposal seeks to explore the neurobiological consequences of genetic loading for SCZ and its impact on cognition and other traits associated with MDD using the extensive imaging, phenotypic and genetic data available through UK Biobank, LBCs and STRADL (GS). This directly follows work we conducted previously linking polygenic risk scores with imaging markers of psychiatric disease (3), and from work in LBC and GS cohorts, whilst also maximizing potential by the additional use of UK Biobank. UK Biobank is a newly available major national health resource with the aim of improving prevention, diagnosis and treatment of a wide range of illnesses including depression, and includes data on 500,000 individuals. The use of such samples permits analysis of genetic neuroimaging in sample sizes not previously possible. It also permits the creative application of more sophisticated computerised modeling techniques (such as tests of mediation, prediction, and stratification) to combined multi-modal brain MRI, genetic and phenotypic data (4). Ultimately, this proposal offers the opportunity to stratify MDD based on defining subgroups of individuals with common mechanistic origins related to schizophrenia risk. The potential impact of this research could transform the understanding of MDD.

Training outcomes
Extensive training in MR image processing and sophisticated image analysis would be undertaken, including widely used software packages such as SPM, and FSL. It would also be expected that the PhD candidate would develop a thorough understanding of the modeling procedures offered by the statistical package ‘R’, as well as familiarisation with access procedures and use of these large cohorts. The candidate would also develop a sound understanding of procedures necessary for generating polygenic risk scores and of psychiatric genetics in general. Training outcomes would culminate in a sound understanding of techniques at the forefront of neuroimaging and psychiatric genetics.

Application
This MRC DTP programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

You can apply here via the University of Glasgow: http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/
Within the application, at the programme of study search field option, please select ‘MRC DTP in Precision Medicine’.

Please note that, in step 6 within the online application process, you are asked to detail supervisor/project title information. Please ensure that you clearly detail this information from the information provided within this abstract advert. Within the research area text box area, you can also add further details if necessary.

Please ensure that all of the following supporting documents are uploaded at point of application:
• CV/Resume
• Degree certificate (if you have graduated prior to 1 July 2016)
• Language test (if relevant)
• Passport
• Personal statement
• Reference 1 (should be from an academic who has a knowledge of your academic ability from your most recent study/programme)
• Reference 2 (should be from an academic who has a knowledge of your academic ability)
• Transcript

For more information about Precision Medicine at the University of Edinburgh, visit http://www.ed.ac.uk/medicine-vet-medicine/postgraduate/research-degrees/phds/precision-medicine