Next steps in the identification of gene targets for type 1 diabetes

Abstract

The purpose of this review is to provide a view of the future of genomics and other omics approaches in defining the genetic contribution to all stages of risk of type 1 diabetes and the functional impact and clinical implementations of the associated variants. From the recognition nearly 50 years ago that genetics (in the form of HLA) distinguishes risk of type 1 diabetes from type 2 diabetes, advances in technology and sample acquisition through collaboration have identified over 60 loci harbouring SNPs associated with type 1 diabetes risk. Coupled with HLA region genes, these variants account for the majority of the genetic risk (~50% of the total risk); however, relatively few variants are located in coding regions of genes exerting a predicted protein change. The vast majority of genetic risk in type 1 diabetes appears to be attributed to regions of the genome involved in gene regulation, but the target effectors of those genetic variants are not readily identifiable. Although past genetic studies clearly implicated immune-relevant cell types involved in risk, the target organ (the beta cell) was left untouched. Through emergent technologies, using combinations of genetics, gene expression, epigenetics, chromosome conformation and gene editing, novel landscapes of how SNPs regulate genes have emerged. Furthermore, both the immune system and the beta cell and their biological pathways have been implicated in a context-specific manner. The use of variants from immune and beta cell studies distinguish type 1 diabetes from type 2 diabetes and, when they are combined in a genetic risk score, open new avenues for prediction and treatment.

De‐coding genetic risk variants in type 1 diabetes

“…Although the majority of T1D‐associated variants reside in non‐coding regions, 7 there is considerable interest in the few known coding variants due to the possibility that these may not only impact gene expression in the pancreas and/or immune system (Figure 1), but also, potentially induce loss‐ or gain‐of‐function effects. Onengut‐Gumuscu et al .…”

“…5 More recently, with the extension of T1D GWAS subject cohorts from solely European ancestry to include individuals of African and East Asian descent, as well as admixed populations, 36 additional variants with genome-wide significance for T1D were reported, with differential accessibility enriched primarily in effector CD4 + T cells (Teff). 6 Although the majority of T1D-associated variants reside in non-coding regions, 7 there is considerable interest in the few known coding variants due to the possibility that these may not only impact gene expression in the pancreas and/or immune system Depicted are the consensus normalized expression (NX) values summarized from HPA, Genotype-Tissue Expression (GTEx), and Functional Annotation of Mammalian Genomes 5 (FANTOM5) transcriptomics datasets. 90 (b) Heatmap of gene expression within immune cell subsets.…”

Partners in Crime: Beta-Cells and Autoimmune Responses Complicit in Type 1 Diabetes Pathogenesis

“…Surprisingly, the Wellcome Trust Case Control Consortium (WTCCC) established GWAS found relatively few novel risk loci for T1D. It was not until the T1D Genetics Consortium (T1DGC) conducted a meta-analysis that approximately 41 distinct susceptibility loci were identified (165). Fine mapping of these loci using ImmunoChIP established credible sets of single nucleotide polymorphisms (SNPs), most of which are found in non-coding DNA regulatory regions, including tissue-specific enhancers (166).…”

The β-Cell Genomic Landscape in T1D: Implications for Disease Pathogenesis

“…This is an example of an allele-specific mechanism that helps to capture the complexity of T1D-associated genetic risk [31]. In contrast, long VNTR alleles (class III) seem to confer protection against T1D, as they were shown to promote higher levels of insulin transcription in the thymus during the induction of central immune tolerance [23,32,33].…”

“…CTLA4 is a known extracellular receptor that is a negative regulator of cytotoxic CD8 + T cell immune responses [34][35][36]. Additional loci initially associated to T1D prior to application of GWAS include protein tyrosine phosphatase, non-receptor type 22 (PTPN22) gene [37,38], interleukin 2 receptor alpha (IL2RA) [39,40], ubiquitin-associated and SH3 domain-containing protein A (UBASH3A) [41] and interferoninduced helicase c domain-containing protein 1 (IFIH1) [33,42].…”