PhD students only. Suited to someone with an undergraduate or Master’s degree in genetic epidemiology, epidemiology, statistics or bioinformatics. Experience in the analysis/ manipulation of large datasets and a good knowledge of computing is desirable. Experience in ophthalmic genetics advantageous but not essential. Nonstatistical applicants must be able to demonstrate some knowledge of statistics. For statistical applicants, some knowledge of genetics is desirable.
Glaucoma is the leading cause of irreversible blindness worldwide. While there is no cure once visual loss occurs, progressive visual loss and blindness can usually be prevented by timely treatment. This means early detection is vital. Unlike many other common complex diseases, the heritability of glaucoma is very high (70%) and traditional epidemiology studies have not identified any means by which risk can be decreased (e.g. via modifiable risk factors). The major role of genetic factors in glaucoma make understanding the molecular mechanisms fundamental to improve screening and develop better therapies. Although we have developed genetics-based risk prediction tools for glaucoma, we have shown there is scope to improve them.
To develop improved risk prediction tools for glaucoma based on genetic data. To translate these genetic findings into improved screening for the disease. To integrate genetics-based prediction approaches with methods harnessing artificial intelligence. The project may also consider gene-mapping and prediction analysis for other eye diseases.
We already have custody of very large-scale genetic data sets (genome wide association studies, exome/genome sequencing), with further data collection nearing completion. The student will employ a range of statistical genetic approaches to interrogate these data and to determine the genes and pathways underlying glaucoma and use these in prediction models.