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CURRENT RESEARCH PROJECTS

Project

Developing a genetic test to predict who are most at risk of developing complications of diabetes (both type 1 and type 2)

Project

Developing a genetic test to predict who are most at risk of developing complications of diabetes (both type 1 and type 2)

Project Outline

Our research aims to understand diabetes and its complications in order to develop new treatments. We aim for scientific excellence with the goal of developing clinically relevant outcomes to treat diabetes and its complications. Our area of expertise is in understanding the genetic basis of complex genetic diseases. In the next year, we will focus on developing a genetic test to predict who are most at risk of developing complications of diabetes (both type 1 and type 2).
Genetics of Type 1 Diabetes
In order to understand why some people develop Type 1 Diabetes (T1D), and to develop targeted preventative treatments, genetic risk factors must first be identified. We have established the Australian Childhood Diabetes DNA Repository (ACDDR) and are part of the Environmental Determinants of Diabetes (ENDIA) Study. We are looking into identifying ‘genetic risk signatures’ of Type 1 Diabetes and the environmental factors that interact with these genetic pathways to cause disease.

Diabetes complications
Due to the rising health costs associated with complications due to diabetes, the Centre is now focussing its research on diabetic nephropathy which results in kidney failure and imposes a huge cost to the Australian health system.

CURRENT STUDENT PROJECTS

Student Project

Genetic analyses of type 1 diabetes

Student Project

Genetic analyses of type 1 diabetes

Project Outline

Type 1 diabetes (T1D) is an autoimmune disease in which the insulin-producing beta cells are destroyed. It usually has an onset in childhood, but people at any age may be affected. T1D is increasing in frequency, and Australia has one of the highest rates in the world.
Over the last decade, we have been part of the worldwide Type 1 Diabetes Genetics Consortium, dedicated to identifying the genetic risk factors for T1D. For the Consortium, we conducted the world’s largest family-based genetic study for any disease. The Consortium identified over 50 genes that contribute to T1D susceptibility.
We have applied advanced genetic analysis methods to identify how these genes interact and affect an individual’s risk of disease. We found there are six main types of T1D, and these differ in many clinical characteristics.

In this project, these subtypes will be investigated in more detail, with the help of people with T1D attending the Diabetes Clinic at Sir Charles Gairdner Hospital. In collaboration with other endocrinologists around Australia, we will also investigate other aspects of T1D, including gene-environment interactions, and develop diagnostic tests to predict children at high risk.

This project provides an opportunity to work with a group that has a strong track record in analyses of the genetics of T1D and other complex genetic diseases and in systems genetics, and to develop links with diabetes experts around Australia and internationally.

Contact
Professor Grant Morahan – [email protected]

Chief supervisor
Professor Grant Morahan

Other supervisor
Dr Joey Kay (Director, Dept of Endocrinology and Diabetes, Sir Charles Gairdner Hospital)

Project suitable for
PhD

Essential qualifications
B. Sc (Hons.) or MBBS. Familiarity with genetic concepts and terms would be an advantage.

Start date
Anytime

Student Project

Identifying who is most likely to die from heart disease

Student Project

Identifying who is most likely to die from heart disease

Project Outline

Heart disease is a leading cause of death worldwide. We have used our world-leading genetic methods to develop a diagnostic test that can identify people at highest risk of early death from heart disease. This test is based on sophisticated genetic signatures that can distinguish high and low risk groups.
Working with collaborators in the Eastern states, as well as China, India and Europe, this project will further develop and refine the test. The project will involve analyses of genome-wide genetic data from thousands of people enrolled in cohorts for study of genetic epidemiology. We will apply machine-learning methods to optimize prediction of people at highest risk of developing heart attacks or stroke. Other diseases can also be investigated using the same methodology.

This project provides an opportunity to work with a group that has a strong track record in analyses of complex genetic diseases and in systems genetics, and to build skills in IT, especially in machine learning techniques and parallel computing.

Contact
Professor Grant Morahan – [email protected]

Chief supervisor
Professor Grant Morahan

Other supervisor
Prof Peter Thompson

Project suitable for
PhD

Essential qualifications
Advanced mathematical and computer science skills. Familiarity with genetic concepts and terms would be an advantage. B. Sc Hons.

Start date
Anytime

Student Project

Identifying genetic signatures of high risk cancer

Student Project

Identifying genetic signatures of high risk cancer

Project Outline

Cancer is a leading cause of death worldwide. We have used our world-leading genetic methods to develop a diagnostic test that can identify people at highest risk of early death from melanoma. This test is based on sophisticated genetic signatures that can distinguish high and low risk groups.
This project will apply the same methods to identifying patients at highest risk for other cancers, particularly breast cancer and colorectal cancer. The project will involve analyses of genome-wide genetic data from thousands of patients enrolled in cohorts for study of genetic epidemiology of cancer. We will apply machine-learning methods to optimize prediction of people at highest risk of early death from cancer.
This project provides an opportunity to work with a group that has a strong track record in analyses of complex genetic diseases and in systems genetics, and to build skills in IT, especially in machine learning techniques and parallel computing.

Chief supervisor
Professor Grant Morahan

Other supervisor
Dr Andy Redfern

Project suitable for
PhD

Essential qualifications
Advanced mathematical and computer science skills. Familiarity with genetic concepts and terms. B. Sc Hons.

Start date
Anytime