Associate Professor Stuart MacGregor: Stuart.MacGregor@qimrberghofer.edu.au
Dr Matthew Law: Matthew.Law@qimrberghofer.edu.au
Suitable background: The post is ideally 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 cancer genetics and/or molecular biology 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.
Project Description: Genetics, together with sun exposure, play an important role in the development of skin cancers. Our lab studies both melanoma and keratinocyte cancers. Melanoma is responsible for >1,800 deaths a year in Australia. While keratinocytic cancers are rarely deadly, their high incidence still results ~600 deaths are year, and are the most expensive cancer in Australia (> $500 million p.a.).
Through large cohort studies based at QIMR Berghofer Medical Research Institute, including the Queensland Study of Melanoma: Environmental and Genetic Associations (Q-MEGA), the Queensland Twin Registry (QTwin), and the QSkin skin cancer study (N ~ 20,000), and from large international datasets (e.g. UK Biobank, N > 500,000) we have a large body of data linking genetics to skin biology. Through this we are able to assess the genetics of skin cancers, skin ageing, pigmentation, and mole count.
Aims: To use computational statistics approaches to identify risk factors for melanoma and keratinocytic cancer by leveraging related traits. To use this genetic information in risk prediction models and to identify factors important for outcome and prognosis.
Approaches: The project will focus on characterizing germline variation in skin cancer. Genome-wide data will be married with data on cancer susceptibility traits and survival. The overlap of these traits will be explored to identify new genetic risks common to all traits. Prediction models will be developed from this combined data, and calibrated against datasets in hand (e.g. Q-Skin) to determine their efficacy. Mendelian randomisation will be used to determine if potential risk factors associated with skin cancer are causal. Fine-mapping, bioinformatics, and post-GWAS approaches (e.g. gene-based tests) will be used to fully interpret identified genetic variants.
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