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Geographic distribution and phenotypic associations of Type 1 Diabetes risk variants (Dan Crouch)
People in different geographic regions vary in both their genetic makeup and in the environments to which they are exposed. Finding areas in which number of people who have Type 1 Diabetes (T1D) is higher or lower than their genetics would suggest implies the presence of environmental factors in that region that are either causing or protecting against T1D. Bacterial or viral infections are strong candidates. The UK Biobank is a collection of half a million people from across the UK that is ideal for this purpose. In addition to geographic area, detailed medical and lifestyle information is available for large numbers of participants. We are analysing the UK Biobank to understand how genetic and environmental risk factors predispose to T1D.

Genetic analyses in T1D (Dan Crouch, David Flores and Jia-Yuan Zhang)
This project aims to further characterise the genetic susceptibility to T1D by analysing larger datasets, which increases power to identify previously undiscovered genetic regions associated with the disease. Of the ~60 regions associated with the disease currently, we know the causal variants in a small number. Often T1D variants lie in large haplotypes which are rarely broken up in the European population and therefore identification of the causal variant amongst all the SNPs contained in these large haplotypes is troublesome. Using larger datasets allows for better resolution but also using individuals of different ancestry helps to fine-map the more likely causal variants, which we will attempt to do using a cohort of African-American individuals and Finnish individuals to combine with cases and controls from the UK and central Europe.
Identification of T1D endotypes has become a research question of interest, which may help in personalising treatment and/or identifying patients most likely to benefit from clinical trials. T1D progresses faster in individuals diagnosed very young, so we will be examining any genetic differences between T1D cases diagnosed very young compared to those diagnosed later in life.
Finally, each susceptibility region will be acting in one or more cell/tissue types. By examining functional data from various cell types, particularly ATAC-seq data, we can overlay our most likely causal SNPs from each region and see if they lie in open chromatin in various cell types. This will help guide the appropriate cell type for each susceptibility region. In particular, we are interested in regions that are likely acting in islets. In those regions, we will see if there is genetic susceptibility to type 2 diabetes, where a shared underlying mechanism may be increasing risk to type 1 and type 2 diabetes.

Single-cell analysis (Dominik Trzupek, Jia-Yuan Zhang and Ricardo Ferreira)
To complement our GWAS work at the level of large cohorts such as the UK Biobank, we also carry out analysis at a very different scale with the aim of understanding the different cell types with a role in T1D. Populations of cells, such as regulatory T cells, can exhibit a great deal of heterogeneity in their molecular properties and function, making it important to perform analysis at the level of individual cells. Recent technological advances now allow features such as gene/protein expression and chromatin accessibility to be measured across thousands of cells from a sample at single-cell resolution. This opens up exciting possibilities such as identifying or characterising rare cell types and discovering which cell subtypes are expanded or altered in a situation such as disease.
We have generated single-cell datasets from a number of human donors including healthy individuals and those with autoimmune disease, with a particular focus on the CD4+ T cell population. Furthermore, we have an ongoing focus on understanding the mechanism of action of ultra-low dose IL-2 to promote better regulatory T cell function in type 1 diabetes. Applying these latest technologies to unique clinical samples of patients treated with exogenous IL-2 will inform on how the immune system is modulated by immunotherapy and the putative clinical benefits of this dosing regimen.
Analysis of these data will allow us to improve our understanding of the heterogeneity in this complex group of cells. We apply a range of computational methods and are interested in approaches that will enable us to investigate how gene regulation is linked to heterogeneity in cellular function.
Further details about the 'Single-cell characterisation of human Treg heterogeneity' project can be found on the Research Projects page.