Integrating Capture-HiC with omic time course data to uncover the regulatory interactions modulated by genetic variation in disease

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Rheumatoid arthritis (RA) is one of the most common chronic inflammatory diseases. A number of genetic differences related to RA have been discovered through Genome Wide Association Studies (GWAS) where large genetic datasets are analysed to statistically associate genetic changes in the human population (usually differences at single nucleotides, called SNPs) with disease risk. However, to make progress it is now important to improve our understanding of how these disease-associated SNPs affect the functions of cells and tissues, so that we can better understand the disease mechanism and ultimately develop more effective medicines. A major obstacle to understanding the effect of these disease-associated SNPs is that many of them lie far from the main control regions of protein-coding genes (known as promoters) in terms of the linear DNA sequence of the genome. There is strong evidence that some of these SNPs lie in enhancer regions which are distal control regions of DNA that come into contact with promoters through looping of the DNA sequence, i.e. these enhancer regions appear distant in terms of DNA sequence but may be close in physical space. One of the applicants, Peter Fraser, is a leading expert in investigating how DNA folds and has developed novel experimental methods to allow this to be tested. This allows us to investigate how the DNA regions associated with RA interact and control genes in human cells. In this project we will combine this technique with measurements of gene activity and enhancer activity in human cells over time. We will look specifically at stimulated T-cells, cells which are involved in the immune system and are known to be important determinants of RA disease progression. We will use these data to build mathematical models describing how T-cells regulate gene expression through enhancer activity and enhancer-promoter interaction. These models will allow us to better understand how regulatory proteins, called transcription factors, bind to the DNA at enhancers and promoters to turn genes on or off. Finally, we will carry out experiments on T-cells derived from healthy human volunteers where genetic data (SNP calls) are already available to test whether the natural genetic variation we observe at SNPs identified through our analysis do have a strong effect on enhancer activity or enhancer-promoter interactions. This would then provide strong evidence for how these SNPs regulate specific genes and we will investigate the downstream cellular pathways to which these genes belong. The project brings a leading RA genetics group together with a leading molecular biology group and a leading mathematical modeller to work closely together and utilise the most up to date knowledge to gain insight into the function of genes that cause RA.