Mapping environmental and genetic determinants of Parkinson’s disease risk and clinical heterogeneity

Parkinson’s disease doesn't develop in a vacuum; it is driven by a complex collision of genetic susceptibility, aging, lifestyle, and environmental exposures. While we know the environment plays a massive role, the existing literature is notoriously noisy. Studies consistently struggle with imprecise measurements, confounding variables, reverse causation, and the sheer logistical difficulty of tracking an individual's exposure history over time.

We are addressing these bottlenecks head-on through the Australian Parkinson’s Genetics Study (APGS). We have already collected extensive baseline and follow-up questionnaire data capturing the health, occupation, and lifestyle histories of people with and without Parkinson's across Australia.

Your project will take this resource to the next level by building a robust geospatial environmental data layer. Using advanced, privacy-preserving governance safeguards, you will link geocoded participant addresses with highly detailed spatial measures of their lived environments. To execute this, you will leverage the Climate, Urbanicity, Environment and Society framework—which standardises environmental data across domains like air pollution, climate, and socioeconomic conditions—and connect it to MyGeosome, a cutting-edge environmental exposome platform. Together, these tools will give you the operational capability to study environmental influences at a scale and resolution rarely seen in Parkinson’s research.

Your primary goal is to build and analyse an integrated environmental-genomic data engine for the APGS. Using this resource, you will decode how environmental exposures—and specifically, gene-environment interactions—drive both the risk of developing Parkinson’s and the heterogeneity of its clinical presentation.

Potential objectives include:

1. Building a reproducible, privacy-first data layer that successfully links APGS participants with harmonised spatial exposure maps.
2. Integrating these spatial metrics with the rich occupational and lifestyle data already captured in our APGS questionnaires.
3. Pinpointing the specific individual (or combined) environmental exposures that influence disease risk, age at onset, and distinct clinical features.
4. Testing whether a patient's genetic susceptibility modifies the impact of these environmental exposures (via gene-environment interactions and polygenic score-by-environment analyses).
5. Validating the robustness and potential causal relevance of your findings using complementary external cohorts.

Note: You will have substantial creative freedom to shape the specific exposure domains, clinical outcomes, and analytical methods that form the core of your thesis.

This project requires blending data engineering with advanced epidemiological and genetic modelling. On the logistical side, you will curate questionnaire data, execute spatial linkages, and harmonise massive exposure datasets. Analytically, your toolkit will range from spatial modelling and geographic information systems (GIS) to exposome-wide association analyses, causal-inference methods, and survival models.

You will need to think critically about the details: exposure timing, temporal resolution, handling missing data, and adjusting for population structure. To support this, you will build robust skills in R and/or Python, reproducible data pipelines, and high-performance computing (HPC). You won't be doing this in isolation—you will be guided by a multidisciplinary supervisory team spanning clinical neurology, health geography, environmental epidemiology, and statistical genetics.

This project is perfect for a strategic, analytically minded student with a background in epidemiology (especially spatial or environmental), data science, statistics, computer science, genetics, or biomedical science. While prior experience with programming or GIS is a major advantage, our interdisciplinary team is fully equipped to help a strong, quantitatively gifted candidate develop these complementary skills from the ground up.

The strategic impact of this project is twofold. First, you will be creating an enduring, high-value environmental data asset for the APGS. Second, by integrating environmental and genomic data, you will help answer one of the field's biggest questions: Why do people with the same genetic risk experience completely different clinical realities?

Ultimately, your findings could pinpoint modifiable risk factors, inform targeted prevention strategies, and identify vulnerable populations who require closer clinical monitoring. You can expect to drive multiple publishable outputs, ranging from high-impact methodological papers to substantive analyses of Parkinson's disease risk.