Statistical Genetics

The Statistical Genetics Laboratory studies the role that genetic variation plays in determining risk of disease and its risk factors. The laboratory develops and applies statistical genetic methods to gene mapping studies across a wide range of traits and diseases. One major focus is understanding genetic variation in various cancers. Cancers studied include melanoma, ovarian cancer and oesophageal cancer. This work will lead to better understanding of why particular individuals are affected by cancer or why they respond poorly to cancer treatment. Another major interest is ophthalmological genetics, with ongoing work to identify the specific genes involved in both eye disease and in underlying quantitative risk factors.

CURRENT RESEARCH

  • gene mapping studies on eye disease
  • translation of gene discoveries in eye disease using approaches such as genetic prediction
  • the modifiable risk factors that actually cause cancer
  • twin studies examining ophthalmogical traits
  • genetic factors underlying oesophageal cancer and its risk factors
  • response to chemotherapy in ovarian cancer
  • gene mapping studies in melanoma
  • statistical and computational methods in genetics

Staff

  • Alex Hewitt
  • Amanda Lim
  • Guiyan Ni
  • Dr Jue Sheng Ong
  • Mathias Seviiri
  • Associate Professor Matthew H. Law
  • Nathan Ingold
  • Ngoc Quynh Le
  • Regina Yu
  • Shanika Jayasinghe
  • Weixiong He
  • Wendy Yang
  • Xikun Han

Internal collaborators

External Collaborators

  • Professor Jamie Craig, Flinders University
  • Dr Kathryn Burdon, University of Tasmania
  • Dr Alex Hewitt, University of Melbourne
  • Professor David Mackey, Lions Eye Institute
  • Professor Janey Wiggs, Harvard Medical School
  • Professor Georgia Long & Professor Richard Scolyer, Melanoma Institute Australia
  • Dr Kevin Brown, National Cancer Institute Bethesda, USA
  • Professor Tim Bishop & Dr Mark Iles, University of Leeds
  • Professor Anna DeFazio, Westmead Hospital

We gratefully acknowledge support from the following organisations and funding bodies:

  • National Health and Medical Research Council
  • Australia Research Council
  • Cancer Australia
  • National Institute of Health
  • Perpetual Foundation
  • Worldwide Cancer Research

STUDENT PROJECTS

Genetics-based Approaches to Precision Medicine for Chronic or Noncommunicable Diseases

This project is suited to a PhD student with experience in genetic epidemiology, epidemiology, biostatistics or bioinformatics. Experience in the analysis and manipulation of large datasets and a good knowledge of computing is desirable. Experience in cancer genetics and clinical medicine are advantageous but not essential. Non-statistical applicants must be able to demonstrate some knowledge […]

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Bridging gaps on the genetics of age-related disorder among under-represented populations

This project is suitable for PhD students only. Some experience in biostatistics and data analysis is essential, and a background in statistics, engineering, health sciences, epidemiology, health economics, computer science and/or public health is recommended. Crucially, candidates must demonstrate adequate interpersonal skills, critical thinking and cultural competence to effectively engage with stakeholders from diverse backgrounds. […]

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Eye disease genetics

This project is suitable for Honours, Masters, MPhil, MD or PhD student. BACKGROUND 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, […]

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Genetics of skin cancer

The project is suited to a PhD student with experience in genetic epidemiology, epidemiology, biostatistics or bioinformatics. Experience in the analysis and manipulation of large datasets and a good knowledge of computing is desirable. Experience in cancer genetics is advantageous but not essential. Non-statistical applicants must be able to demonstrate some knowledge of statistics. For […]

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