18 July 2022
About the research project
Multiple sclerosis (MS) is a devastating autoimmune neurodegenerative disease of the central nervous system, caused by an interplay between environmental and genetic factors. So far, genome-wide association studies (GWAS) for MS have identified >200 susceptibility regions. However, these susceptibility loci can only explain 38% of the heritability of MS while the remaining heritability has been left unexplained. A better understanding of the pathophysiology of MS will greatly improve preventive, diagnostic, therapeutic and reparative strategies.
To address this significant knowledge gap, this research project will focus on the integrated analysis of complementary data sets obtained from different parts of cell biology such as the genome, epigenome, transcriptome, proteome, metabolome and lipidome to improve understanding of disease pathophysiology. The PhD candidate will combine computational skills with statistical expertise to analysis large MS multi-omics datasets with extensive national and international collaborations. These large datasets include cross-sectional data (e.g. > 6000 whole exome sequencing data) as well as longitudinal follow-up data (e.g., the AusLong study which has followed 279 cases from their first clinical event suggestive of MS for up to 15 years, and has comprehensive data on standardized MS clinical outcomes, personal and lifestyle factors, environmental parameters, and significant omics data).
The PhD candidate will be supported by a strong research team that has already demonstrated significant knowledge impacts. For example, in a recent study published in Nature Communications, we established a pipeline that can integrate genomics and transcriptomic data to investigate tissue- and cell-type-specific enrichment of SNP heritability. Our study provided evidence supporting the pathogenic roles of some tissues and cells in contributing to MS development.
Primary SupervisorMeet Dr Yuan Zhou
Applicants will be considered for a Research Training Program (RTP) scholarship or Tasmania Graduate Research Scholarship (TGRS) which, if successful, provides:
- a living allowance stipend of $28,854 per annum (2022 rate, indexed annually) for 3.5 years
- a relocation allowance of up to $2,000
- a tuition fees offset covering the cost of tuition fees for up to four years (domestic applicants only)
If successful, international applicants will receive a University of Tasmania Fees Offset for up to four years.
As part of the application process you may indicate if you do not wish to be considered for scholarship funding.
Applicants should review the Higher Degree by Research minimum entry requirements.
The project is competitively assessed and awarded. Selection is based on academic merit and suitability to the project as determined by the College.
Additional essential selection criteria specific to this project:
- A degree in genetics, epidemiology, bioinformatics or other relevant field
- Experience in at least one programming language (e.g., R, Python and C++)
Additional desirable selection criteria specific to this project:
- Experience in analysing large multi-omics data (e.g., GWAS, WGS, scRNAseq and proteomics)
- Experience in statistical modelling (ideally in repeat measurement modelling and machine learning)
- Demonstrated skills working with Unix and high-performance computer platforms
There is a three-step application process:
- Select your project, and check you meet the eligibility and selection criteria;
- Contact the Primary Supervisor, Dr Yuan Zhou to discuss your suitability and the project's requirements; and
- Submit an application by the closing date listed above.
- Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
- As part of your application, you will be required to submit a covering letter, a CV including 2 x referees and your project research proposal.
Following the application closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email by the advertised outcome date.