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Precision Medicine DTP - Identification of biomarkers of typical and atypical depression


Project Description

Background

Major depressive disorder (MDD) is a highly polygenic disorder with hundreds of common risk variants identified through genome-wide association studies of large samples. It has, however, been suggested that the small effect size of each variant is, in part, the result of phenotypic heterogeneity. Such heterogeneity may be the result of subgroups of depression with different underlying causal factors. Phenotypic correlations suggest that metabolic traits and weight gain/loss during a depressive episode may act as markers for typical and atypical subtypes of depression. Understanding the underlying differences between these subtypes could improve treatment selection and identify novel biological mechanisms underlying depression.

This project will examine genetic correlations of each subtype with psychiatric illnesses, metabolic and related traits. This will utilize the breadth of genotyping in UK Biobank and Generation Scotland population-based cohorts to select variables that discriminate between subtypes. Phenotypic prediction models will be developed on the subsets of UK Biobank participants with the appropriate measures (often less than the full cohort). Differences in prescriptions of common drugs for mental health and metabolic disorders will be assessed between subtypes.

Students are expected to have a BSc or MSc in a relevant field and an understanding of genetics and statistics. Training will be given in bioinformatics using both R and Python programming languages and machine-learning statistical approaches.

Training outcomes

Understanding of the strengths and limitations of genomic and phenotypic data. Training in programming languages: R and Python, data management best practice, statistical approaches such as mixed linear/logistic models and signature selection, how to test model robustness and assess results across models. Ability to critically assess information, write papers and present work at meetings are vital skills and will be supported throughout the PhD.

This MRC 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.

All applications should be made via the University of Edinburgh, irrespective of project location. For those applying to a University of Glasgow project, your application along with any supporting documents will be shared with University of Glasgow.

http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

Please note, you must apply to one of the projects and you must contact the primary supervisor prior to making your application. Additional information on the application process is available from the link above.

For more information about Precision Medicine visit:
http://www.ed.ac.uk/usher/precision-medicine

Funding Notes

Start: September 2020

Qualifications criteria: Applicants applying for a MRC DTP in Precision Medicine studentship must have obtained, or will soon obtain, a first or upper-second class UK honours degree or equivalent non-UK qualification, in an appropriate science/technology area.
Residence criteria: The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £15,009 (RCUK rate 2019/20) for UK and EU nationals that meet all required eligibility criteria.

Full eligibility details are available: View Website

Enquiries regarding programme:

References

1) Łojko D & Rybakowski JK. 2017. Atypical depression: current perspectives. Neuropsychiatr Dis Treat. 13:2447-2456. PMID: 29033570

2) Milaneschi Y et al. 2016. Polygenic dissection of major depression clinical heterogeneity. Mol Psychiatry. 21: 516-22. PMID 26122587

3) Brailean A et al. 2019. Characteristics, comorbidities, and correlates of atypical depression: evidence from the UK Biobank Mental Health Survey. Psychol Med. 2:1-10. doi: 10.1017/S0033291719001004. [Epub ahead of print]. PMID: 31044683

4) Wigmore EM et al. 2019. Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP. Pharmacogenomics J. [Epub ahead of print] PMID: 30700811

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