I am Olly Burren currently a PhD student within the prestigious Diabetes and Inflammation (DIL) Laboratory at the University of Cambridge. I am supervised by John Todd with statistical and computational co-supervision from Chris Wallace who is a Wellcome Trust senior research fellow in the Department of Medicine and a Programme leader at the MRC Biostatistics Unit (BSU) and Leonardo Bottolo, a reader in statistics for biomedicine in the Department of Medical Genetics. Concurrently I am Head of Genome Informatics at the DIL, and am lucky to manage a team of three very talented Bioinformaticians, who are responsible for ImmunoBase and T1DBase, two widely used and cited human autoimmune genetics databases.
The DIL is an incredibly stimulating multi-discipline organisation and as such I am extremely lucky to work with experts in encompassing Immunology, Experimental Medicine and Biostatistics. It gets better, however, as we are situated within the CIMR on the Cambridge Biomedical Campus, which itself is close to the MRC BSU, WTSI, EBI and Babraham Institute with whom I currently collaborate.
Our lab is particularly interested in understanding the cellular mechanisms through which autoimmunity is mediated, focusing on type 1 diabetes. This disease primarily singles out young people, and causes selective destruction of pancreatic islets cells, resulting in a lifelong dependency on exogenous insulin as well as some nasty co-morbidities. Worringly it’s prevelence is increasing. It’s a complex, polygenic disease which means that there is an underlying genetic component involving lots of genes that is also modulated by environmental triggers. Our lab seeks to understand both of these components in order to suggest both therapeutic avenues to treat existing type 1 diabetics, and primary prevention strategies, to reduce prevelence in those particularly at risk.
A technique called a genome wide association study (GWAS) pioneered at DIL in collboration with others, can give us a lot of information on the genetic differences that underlie a disease. However translating these into biological mechanisms has proved tricky as our knowledge of how DNA works within human cells is patchy. Fortunately the field of genomics which studies such things has some powerful tools and datasets that we can use. For me combining and aligning these datasets using the genetics presents some extremely interesting challenges.
Work I have carried out previously attempted to tag genes with disease based on proximity, however this is an oversimplification. At the Babraham Institute Peter Fraser’s group has been pioneering a technique called promoter capture Hi-C that gives us information about which bits of DNA bend and touch each other, even though they might be distant on a linear DNA molecule. With many others we have been working on methods to leverage these interactions in 17 primary human cell types with other genomic datasets to get a better understanding of Haematopoiesis in humans. I have been privileged to have the task to intergrate GWAS summary statistics to answer the following questions:-
- Are GWAS signals for traits enriched within interactions, and if so is this tissue specific ?
- Can we use this information to prioritise genes and GWAS signals for followup study ?