Computational drug repurposing for neuropsychiatric disease

Overview

While hundreds of robust genetic associations have been found for neuropsychiatric disease (such as schizophrenia, major depression, and anxiety) understanding the exact molecular mechanisms leading to disease onset and progression remains challenging. Inherited (i.e. genetic) risk factors for many neuropsychiatric diseases converge on genes that are co-ordinately expressed (co-expressed) in a disease-relevant tissue (e.g. brain). The study of how genetic risk factors affect co-expressed genes (i.e. gene co-expression analysis) has the potential to uncover new biological processes underlying disease onset. In turn, these processes may inform the identification of new drugs.

This project will involve the integration gene co-expression networks with large scale genetic and molecular datasets to interrogate the complex biological mechanisms causative of neuropsychiatric diseases. The overall research emphasis is on the identification and characterisation of genes and molecular mechanisms driving disease susceptibility, and the prioritisation of drug compounds that target those genes and mechanisms. This research will provide a list of clinically validated drugs with potentially lower development costs and shorter development timelines.

References

Gerring,Z.F. et al. (2019) A gene co-expression network-based analysis of multiple brain tissues reveals novel genes and molecular pathways underlying major depression. PLOS Genet., 15, e1008245.

Approaches, skills and techniques

This project is most suitable for students with a strong interest in quantitative statistical analysis. This project will have a strong computational component. Students will gain experience in high performance computing, programming, statistical genetics, and bioinformatics.

Outcomes

This project will validate the use of large scale genomic data for drug repositioning studies of complex neurodegenerative diseases. I expect the successful student will produce a first-authored publication at the conclusion of their honours year. The knowledge gained will help the broader research community to develop subsequent repositioning programs, promoting partnerships between industry and researchers, and accelerating drug discovery in Alzheimer’s disease and other forms of dementia.

Required skills and experience

No specific skills are required, but the student will need to have an interest in learning computational skills and programming languages (e.g., Python, R, Unix).

Keywords

• genetics
• bioinformatics
• statistical genetics
• neuropsychiatric
• gene expression
• drug repositioning
• network analysis
• data integration
• high performance computing

Contact

Contact Zachary Gerrin for more information.

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