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  Integrated omics approach to psychiatric disorders


   Faculty of Life Sciences

  ,  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

In psychiatry, the selection of antipsychotics and antidepressants is led by a trial-and-error approach. The prescribing of these medications is complicated by sub-optimal efficacy and high rates of adverse drug reactions (ADRs). These both contribute to poor levels of adherence. Pharmacogenetic testing is a tool that could aid clinicians when selecting psychotropic medications, as part of a more personalized approach to prescribing with identified barriers and enablers to implementation (Jameson et al., 2021). This may improve the use of and adherence to these medications, hence the field of psychiatry is already moving towards personalised medicine as part of the NHS long term plan and Genomic Medicine Service. This project will develop a comprehensive research database of common psychotropic drugs and associated pharmacogenomic information integrated with dedicated Galaxy server (Afgan et al., 2021) to allow web-based analysis of collected data as well as machine learning model to analyse the interplay between effect/response to common psychotropic drugs and associated pharmacogenomic activity.

Objectives will include:

  1. The search for publicly available datasets, interrogation and robustness checking i.e.: the availability of raw data, the extent to which the experiment is described according to the MIAME and MINISEQE standards, statistical quality control of samples (1-9 months)
  2. Bioinformatics analysis of the ‘approved’ datasets (9-15 months)
  3. Development of machine learning model for association between psychotropic drugs and pharmacogenome. Review and interpretation of multi-omics patterns found in the context of antipsychotic and antidepressant treatment psychiatric diseases of and consideration of the value to the implementation of pharmacogenetic testing in Mental Health Care Settings (15-36 months)

Methodology

1. Public data extraction

  1. A range of genomic data repositories will be searched for data associated with antipsychotic and antidepressant treatment. We will extract the associated data, metadata and publication records where available
  2. The student will perform manual curation of the data to leave the records for which comprehensive metadata and raw data are available or can be obtained.
  3. The student will design the database for antipsychotic and antidepressant treatment. The database will feature standardized metadata with a hierarchical structure. We will connect database to dedicated Galaxy instance (1)

2. Pre-processing and individual data analysis

  1. The student will perform quality control, quality filtering, normalization and/or alignment of the data.
  2. The student will update the database records to include: the processed data, quality control information and bioinformatics procedure.
  3. If necessary, the student will work using R or/and Python programming languages to develop/modify tools required for analysis.

3.   Epigenetic biomarkers identification

  1. The student will develop machine learning model to analyse the interplay between effect/ response to common psychotropic drugs and pharmacogenomics data. The University of Bradford High Performance computing cluster will be used for this task
  2. The student will review research findings in the context of antipsychotic and antidepressant treatment psychiatric diseases of and consideration of the value to the implementation of pharmacogenetic and publish the results

How to apply

Formal applications can be submitted via the University of Bradford web site. Informal enquiries are also welcome.

Biological Sciences (4) Mathematics (25) Medicine (26) Nursing & Health (27) Psychology (31)

Funding Notes

This is a self-funded PhD project; applicants will be expected to pay their own fees or have a suitable source of third-party funding. A bench fee may also apply to this project, in addition to the tuition fees. UK students may be able to apply for a Doctoral Loan from Student Finance for financial support.

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