Identifying individuals at high risk of Alzheimer’s disease

This project is suitable for Honours, Masters, MPhil, MD or PhD students. For those with experience in statistics and an interest in dementia, genetic epidemiology, bioinformatics and machine learning.

BACKGROUND

Dementia affects an estimated 353,800 Australians, with up to 80% being diagnosed with Alzheimer’s disease (AD). Newly developed anti-amyloid drugs are set to revolutionise the treatment of AD. These are likely to have the most impact at the earliest disease stages, therefore there is an urgent need for early-stage biomarkers which are affordable, accessible and scalable.

AIM

To generate predictive screening algorithms, opening up opportunities for simple, accurate and effective screening to identify early-stage AD.

APPROACH

The student will build on our current work in PISA (the Prospective Study of Aging, Genes, Brain and Behaviour) using datasets including genome-wide genetic SNP chip data, cognitive data, and blood-based biomarkers. Predictive algorithms will be developed using statistical and machine-learning approaches.

PROJECT POTENTIAL

The identification of individuals at the earliest stages of AD will provide the opportunity to allow the selection of individuals for early treatment strategies.