Can the 3D genome predict type 1 diabetes onset?

Can the 3D genome predict type 1 diabetes onset?

Project details

Only 10% of individuals with a genetic predisposition to type 1 diabetes (T1D) will in fact develop T1D. Predicting this 10% prior to disease onset is an enduring clinical and scientific challenge. However, our exciting preliminary data suggests that 3D genome organisation within predisposed individuals’ immune cells may hold the key to predicting disease onset. 

A student with an interest in biology, programming and statistics would be able to work with genomic data sets to tackle biomedical problems centered around DNA structure and T1D. This could include: 

  • novel pipeline development 
  • analysis of the 3D structure through Hi-C techniques 
  • transcriptomics analysis 
  • epigenetics analysis 

The student would work closely with both our bioinformatics postdocs and our wet lab team. This project could include wet lab experience. 

 

About our research group

Every one of your nuclei contain 2 metres of DNA. We are fascinated by how this DNA is packed into the nucleus, and how this organisation changes throughout normal development and influences disease. Our team is small (lab head, 3x postdocs, 1x research assistant, 1x student) but enthusiastic and productive. Recently, we have published a number of high-impact publications (Nature Immunology, Nature Communications, among others). This success is due to our multidisciplinary team, containing expert molecular biologists and bioinformatians.  

After the graduation of our last student, we are looking for an enthusiastic bioinformatics student to join the team. If you are excited by using your bioinformatic skills to reveal fundamental biological insights or change the way human disease is understood, please get in touch! 

 

Email supervisors

 

Researchers:

Dr Tim Johanson
Dr
Tim
Johanson
Immunology division

Professor Gordon Smyth

Professor Gordon Smyth writing on a whiteboard
Professor
Gordon
Smyth
Joint Division Head
Dr Hannah Coughlan
Dr
Hannah
Coughlan
Bioinformatics division

Project Type: