PhD project but may also be considered for Honours project. A background (or strong interest) in genetics, epidemiology, data science, statistics or bioinformatics is preferred. Previous research experience coding and analysing data (genetic, clinical or other kind) using R/Python is also desirable.
Background: Parkinson’s disease (PD) affects >100,000 Australians, and the number of patients worldwide was estimated to be at least 6.1 million in 2016. In Australia, PD is the second most common neurological disorder, the second most common cause of dementia, and the fastest growing neurodegenerative disease, with approximately 32 newly diagnosed patients every day. PD is a progressive condition, typically defined by its motor features, which are also accompanied by a range of non-motor symptoms. The onset, intensity and progression of these clinical features varies greatly from patient to patient and the underlying mechanisms of such heterogeneity are largely unknown, although it is increasingly evident that genetics play an important role.
Aim: We have collected self-reported measures on a range of sociodemographic, clinical and lifestyle variables from individuals with PD from all over Australia. The aim of the study is to advance knowledge about the environmental and genetic factors that contribute to differences in Parkinson’s disease risk, clinical heterogeneity, treatment response and progression.
Approach: The student will conduct statistical and computational data analyses aimed at identifying risk and protective factors (including genetic biomarkers) for Parkinson’s disease and its associated variables.
Outcome: The characterization of risk and protective factors may provide important insights into the pathological mechanisms of PD development throughout the lifespan, which is the first step toward developing preventative and therapeutic interventions, including disease-modifying therapies.
Suitable background: A background (or strong interest) in genetics, epidemiology, data science, statistics or bioinformatics is preferred. Previous research experience coding and analysing data (genetic, clinical or other kind) using R/Python is also desirable.