Suitable for Masters and PhD students. Some experience in biostatistics and data analysis is essential and a background in bioinformatics and epidemiology is highly desirable.
Cardiovascular disease is a leading cause of death and morbidity in Australia and internationally yet it is highly preventable. Risk prediction equations are used in primary care to estimate the combined influence of multiple cardiovascular risk factors to identify those most likely to have a future cardiovascular event. Polygenic risk scores, which combine information from millions of genetic variants, have been shown to predict cardiovascular disease events independent of clinical risk factors, and can predict risk before the onset of clinical risk factors making them especially useful for improving risk prediction in younger people where conventional prediction equations underperform.
To assess the utility of genetic information to improve cardiovascular disease risk prediction in Australia.
This project will involve the analysis of genetic and linked data from a cohort of ~16,000 Australians and contribution to the development of a microsimulation model to estimate population health and cost-effectiveness outcomes.