Background
In the clinic, finding the precise molecular cause of a patient's Parkinsonism can be a gruelling diagnostic odyssey. While most Parkinson’s cases are driven by a complex mix of genetics and environment, a subset of patients—typically those with an early onset or a strong family history—develop the condition due to rare, highly penetrant genetic variants. Pinpointing the responsible mutation is critical: it ends the diagnostic search, provides clarity for genetic counselling, and can even unlock access to highly targeted clinical trials. Yet, even after standard genetic testing, many families are left without answers.
The clinical picture becomes even more complicated with Parkinson’s-plus conditions (atypical parkinsonian disorders), such as dementia with Lewy bodies, progressive supranuclear palsy, and multiple system atrophy. These conditions share overlapping symptoms with Parkinson's but are notoriously difficult to diagnose early on, and their rare genetic drivers remain poorly understood.
To tackle this, your project will leverage the operational scale of two major national initiatives. Through MonoPDAus, we are sequencing and clinically assessing Australians with early-onset or familial Parkinson’s. Through AusPD+, we extend this effort to atypical parkinsonian disorders. Comparing these conditions side by side presents a significant strategic opportunity to map shared biological pathways and uncover distinct genetic blueprints across the broader spectrum of neurodegeneration.
Aim
Your primary objective is to identify, prioritise, and characterise the rare genomic variants driving familial Parkinson’s and Parkinson’s-plus conditions, and map exactly how these genetic signatures dictate a patient's clinical presentation.
Potential research questions include:
- What percentage of our cohort carries pathogenic (or potentially disease-causing) variants in known neurodegeneration genes?
- Can we deploy advanced WGS pipelines to discover completely novel genes or variants in families that remain genetically unresolved?
- Beyond simple point mutations, what role do complex structural variants, copy-number variants (CNVs), repeat expansions, and mitochondrial variations play in these diseases?
- Are there distinct rare-variant burdens or biological pathways shared between classic Parkinson’s disease and atypical Parkinson's-plus phenotypes?
- How do these genetic discoveries correlate with clinical realities, such as age at onset, motor/non-motor symptoms, and overall disease trajectory?
Note: The final scope of your thesis is flexible. You might choose to focus exclusively on MonoPDAus, AusPD+, a specific class of genomic variation, or run a high-level comparative analysis across both cohorts.
Approach
Day-to-day, you will be operating at the intersection of whole-genome bioinformatics and statistical genetics. Rather than just running standard tools, you will be deeply involved in the logistics of the analytical pipeline: from sequence quality control and variant annotation to sophisticated phenotype-driven prioritisation.
Depending on your specific research questions, you will execute rare-variant burden tests, structural/CNV analyses, repeat-expansion profiling, and family-based segregation analyses. You will validate your candidate findings against large-scale external reference datasets and global resources such as GP2.
To achieve this, you will build robust, reproducible computational workflows in R and/or Python, utilising Linux-based high-performance computing (HPC) environments. Importantly, you won't be working in a silo. You will collaborate closely with clinical experts, learning how to accurately interpret complex movement-disorder phenotypes and navigate the crucial distinction between a "research finding" and a "clinically reportable result."
This project requires a highly analytical student with a background in genetics, genomics, bioinformatics, computer science, data science, or a related biomedical field. Prior experience working with sequencing data and programming is a major advantage. More than anything, we are looking for a candidate with strong quantitative abilities, a strategic mindset, and a genuine drive to master whole-genome analysis