Statistical methods for risk prediction of common diseases

This Earmarked Scholarship project is aligned with a recently awarded Category 1 research grant. It offers you the opportunity to work with leading researchers and contribute to large projects of national significance.

Supervisor – Dr Jian

Polygenic risk predictors based on a large number of genetic variants can identify a subgroup of individuals at high risk of developing a common disease, such as coronary artery disease, type 2 diabetes, or breast cancer. This risk stratification will greatly facilitate precision medicine through opportunities for early disease diagnosis, prevention and intervention. The overall aim of this project is to develop and implement optimised statistical methods and software to best predict an individual’s disease risk through the use of genetic and non-genetic data. Data available for the analysis include large-scale genetic data from genome-wide association studies, whole-genome sequence data, molecular quantitative phenotypes across tissues and cell types, functional annotations on genomic regions, and longitudinal health conditions and lifestyle phenotypes from biobanks.

The successful applicant will enrol through the Institute for Molecular Bioscience.

Preferred educational background

Applications will be judged on a competitive basis taking into account the applicant's previous academic record, publication record, honours and awards, and employment history.

A working knowledge of statistical genetics would be of benefit to someone working on this project.

A background or knowledge of computer programming is highly desirable.

*The successful candidate must commence by Research Quarter 4, 2022. You should apply at least 3 months prior to the research quarter commencement date. International applicants may need to apply much earlier for visa reasons.

Apply now