Regulatory networks for single nucleotide polymorphisms in type 1 diabetes mellitus

Abstract

Type 1 diabetes (T1D) is an autoimmune disorder characterised by hyperglycaemia resulting from the destruction of insulin-producing pancreatic beta cells by the body’s immune cells leading to loss of insulin production. Genetic factors play a significant role in disease susceptibility. Genome-wide association studies (GWAS) have identified over 60 susceptibility loci for T1D across the human genome. However, as >90% of the single nucleotide polymorphisms (SNPs) that mark these loci are mapped within non-coding regions of the genome, a knowledge gap exists that limits our understanding of how genetic variation contribute to the risk of T1D development. To address this challenge, I used a computational algorithm to assign 346 T1D-associated SNPs to the 996 genes that they control through tissue-specific spatiotemporal transcriptional regulation. Notably, I identified novel transcriptional regulation of transcriptional factors, including Tcf12, Ets2, and Foxp1 that are involved in the regulation of immune response. Moreover, I identified regulatory effects specific for SNPs associated with the differing age-at-onset of T1D across multiple immune cell types such as naïve and activated CD4+ and CD8+ T cells. A machine learning algorithm identified transcriptional changes in the lung as making the greatest single genetic contribution to the risk of conversion to the development of T1D. Further investigations revealed that risk alleles for T1D and type 2 diabetes (T2D) co-regulate genes involved in the regulation of pancreatic beta cell function and insulin signalling. Finally, I experimentally validated the allele-specific enhancer activity of SNPs associated with the early age of T1D onset on transcriptional control of key immune regulatory genes, including RBPJ, FOXP3, CTLA4, RP11-973H7.1, and HLA class I and II genes. Collectively, the work presented in this thesis contributes novel insights into the gene regulatory mechanisms that are associated with genetic risk for T1D. In so doing, it provides a valuable resource for the design of new and ongoing experimental studies on T1D.