Risk Prediction in AMD - Can We Do Better?
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.