Causal gene identification
The unbiased approach of genotyping of common variants in genome wide association studies (GWAS) have been very informative in discovering the common variants and genomic regions associated with T1D. The association was further refined using the dense genotyping array, ImmunoChip, in regions defined by association in GWAS. Moving from credible single nucleotide polymorphisms (SNPs) to assigning candidacy to genes and defining common pathways associated with disease has been hampered by the fact that the majority of common variants associated with T1D are located in the non-coding part of the genome. Candidacy has, in part, been ascribed to genes being closest to the index SNP with strongest association in a region or to biologically plausible genes within a given window around the index SNP in a region. Genetically validated drug targets have a lower failure rate in pharmaceutical drug development pipelines and the need to identify causal genes and pathways involved in T1D is imperative to guide development of intervention strategies to delay decline in insulin production in recent onset T1D patients or to prevent progression to T1D in individuals with a high risk of developing T1D.
In recent years the implementation of novel technologies and methods has revolutionised our understanding of the grammar of the non-coding genome. Intersecting maps of regulatory elements such as enhancers, promoters and heterochromatin in a multitude of blood cell types and tissues with SNPs associated with T1D has enabled, to some extent, the identification of the tissue specificity of disease association. For example, we and others, have shown and enrichment of T1D associated SNPs in enhancer elements in lymphocytes, haematopoietic stem cells and the thymus. Linking regulatory elements that contain T1D associated variants to the genes that they control is a major challenge. Using promoter capture Hi-C, a method to map of long range interactions from the majority of protein coding gene promoters in the genome, in primary blood cell subsets we generated a data driven list of prioritised genes for many of the T1D associated regions. Despite this approach, many T1D regions remain without a data defined candidate.
The ultimate aim, where possible, is to assign cell specificity to T1D-associated SNPs using epigenetic datasets generated in our lab, by collaborators or where openly available. These analyses will guide high resolution mapping of each T1D associated region using state of the art chromatin capture methodology in collaboration with Jim Hughes and Doug Higgs (WIMM) to define candidacy of genes associated with T1D.
The genetics of Interleukin 2 Receptor Alpha (IL2RA)