Risk Prediction in AMD - Can We Do Better?

Paul Baird, PhD The University of Melbourne

Co-Principal Investigators

Adam Kowalczyk, PhD
Alice Pebay, PhD


This proposal brings together different areas of medicine and biology and applies advances in high throughput computing and big data analysis to aid our understanding and advancement of treatments for the eye disease of age-related macular degeneration; particularly the dry form of disease where there is currently no treatment. It will identify genes that interact with each other as well as with other factors known to be involved in increased risk of AMD such as age, sex of an individual and smoking. The statistical findings will be verified through modelling in human cells derived from AMD patients to identify how these genes influence disease. This work will have a profound impact on how we think of both advanced types (the dry and wet types) of AMD and provide targets for future development of therapies that will improve patient’s lives with this disease.

Project Details

This proposal brings together experts in computational, genetics and regenerative medicine to unravel how genes, environment and other factors “talk to, or interact with each other” to influence progression of AMD as well as how these different factors influence AMD disease subtypes.

We will use novel computational programs that we have developed to analyse data from 40,000 patient samples collected through the International AMD Genomics Consortium - the world’s largest AMD genome wide association study. Our first aim will identify a compact set of gene variants that represents the most likely combination of genes predicting AMD. We will also identify how different combinations of genes predict the dry and wet forms of AMD. Once these gene risk combinations have been identified we will assess how other factors such as age and smoking impact on these gene combinations. In the second aim, we will use regenerative medicine techniques to test the function of the gene combinations that we have identified. This will be undertaken by using gene editing techniques in stem cell models that use retinal cells similar to those involved in AMD.

This suite of experiments will provide a powerful pipeline to better understand what causes AMD and in particular what gene combinations are involved in either the dry or wet form of AMD. This will have important implications for development of future novel treatments.