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Type 1 Diabetes – untangling ethnic differences using a multimorbid co-localisation approach

Project Description

Type 1 diabetes (T1D) is an autoimmune disease in which autoreactive T cells destroy the insulin-producing pancreatic beta cells leading to absolute insulin deficiency. Every individual has an underlying genetic susceptibility for T1D. This genetic risk is hypothesized to be realized through a precipitating event in those individuals who go on to develop T1D. Population level data has been used to identify ~60 risk regions in our genetic code that are associated with the risk of developing T1D. The data have led to advances in polygenic risk score development that have seen the integration of low-frequency and low-odds ratio single nucleotide polymorphisms (SNPs) into genetic risk scores (GRS) to improve the prediction of islet autoantibodies and diabetes development, and identify young adults at risk of severe insulin deficiency. One bottle neck in understanding and utilizing this genetic information is the lack of understanding of the biological effects of genetic variation.

We aim to create a process that will enable the information on an individual’s genetic variation to transition quickly from the research arena to clinical utility. To do this we will use computational methods to integrate information on the 3-dimensional structure of DNA to understand the tissue specific burden of genetic changes that predispose an individual to developing T1D. We will then use longitudinal cohorts to predict how SNPs work together and in which tissues they have the greatest effect. Calculating the tissue specific burden of these changes will enable us to predict risk and to develop novel therapeutic approaches to treat T1D.

This project will look at tissue specific burdens of disease and extend a machine learning approach that integrates information on genetic variation, genome structure, and gene expression. The project will be supervised by Associate Professor Justin O’Sullivan and Dr William Schierding within a very productive and supportive international research team. You will use computational techniques to integrate new and existing spatial and epigenetic data from cross-sectional and longitudinal cohort studies to understand the link between the genotype and T1D in children.

Funding Notes

Requests for further information and applications for positions should be sent to . Applications should include a full CV, an academic transcript, and a cover letter outlining your interests in relation to our research. Applications will close on the 15th April 2020. Interviews will be held in the last week of April 2020 with an aim of the position starting in July 2020.

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