Project Description: 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 makes understanding the molecular mechanisms fundamental to improve screening and develop better therapies. Although we have discovered many specific genes influencing glaucoma, we have also shown that most have still to be found. Recent studies have suggested links between glaucoma and other eye diseases. Moreover, recent studies have begun to explore links with conditions such as dementia.
Aims: To identify more loci explaining why some people get glaucoma and some do not. To investigate how artificial intelligence based approaches to improve grading of optic nerve can improve our understanding of glaucoma and related traits. To translate genetic findings into improved screening and into better therapies. To examine the overlap between glaucoma and other traits such as dementia.
Approaches: We already have custody of very large scale genetic data sets (genome wide association studies, exome/genome sequencing), with further data collection underway. The student will employ a range of statistical genetic approaches to interrogate these data and to determine the genes and pathways underlying glaucoma and related traits.
Suitable background: The post is ideally suited to someone with an undergraduate or Masters 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. Non-statistical applicants must be able to demonstrate some knowledge of statistics. For statistical applicants, some knowledge of genetics is desirable.
You will need to enrol at a Queensland University.
Minimum academic qualifications:
Bachelor’s degree with Honours Class I (typically involving a substantial research project) or a Research Master’s degree or equivalent OR
A coursework Master’s degree with a research component of at least 25 % of an academic year and an overall grade point average equivalent to ≥5.5 on a 7-point scale.
If English is not your first language, you will also need to demonstrate proficiency in English. Your test results must not be older than 1/11/2020. Minimum requirements are:
IELTS (Academic module) Minimum overall score of 6.5 and score ≥6.0 in every sub-band; OR
iTOEFL Minimum overall score of 87 and 21 in written, 19 in reading, listening & speaking.
Please email your academic CV and transcripts to Prof MacGregor ([Email Address Removed]) and address your suitability for the project before 17 January 2021
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