Gene identification in type 1 diabetes

Project Overview

Background

Genetic identification of type 1 diabetes (T1D) genes and their pathways is essential for understanding the biology underpinning disease susceptibility. We are integrating the latest and emerging genetics and genomics data - genetic variation, RNA gene expression, methylation, transcription factor binding sites and chromatin phenotypes – to better define the T1D causal genes. For example, identification of contacts between promoter and enhancer sequences is providing major insight to causal gene identification (https://www.ncbi.nlm.nih.gov/pubmed/28983737;https://www.ncbi.nlm.nih.gov/pubmed/28870212).

Aims

1. Carry out genome-wide association analyses (GWAS) of T1D cases and controls.

2. Integrate gene expression and chromatin state data with GWAS results.

 

Training Opportunities

There will be opportunity to learn and develop skills in human genetics and its integration with genome structure and function, with a large computational / bioinformatics / statistical element.

Theme

Genetics & Genomics and Immunology & Infectious Disease

Admissions

Project reference number: 878

Funding and admissions information

Supervisors

Name Department Institution Country Email
Professor John A Todd FRS FMedSci Wellcome Trust Centre for Human Genetics Oxford University, Henry Wellcome Building of Genomic Medicine GBR john.todd@well.ox.ac.uk

Inshaw JRJ, Walker NM, Wallace C, Bottolo L, Todd JA. 2018. The chromosome 6q22.33 region is associated with age at diagnosis of type 1 diabetes and disease risk in those diagnosed under 5 years of age. Diabetologia, 61 (1), pp. 147-157. Read abstract | Read more

AIMS/HYPOTHESIS: The genetic risk of type 1 diabetes has been extensively studied. However, the genetic determinants of age at diagnosis (AAD) of type 1 diabetes remain relatively unexplained. Identification of AAD genes and pathways could provide insight into the earliest events in the disease process. METHODS: Using ImmunoChip data from 15,696 cases, we aimed to identify regions in the genome associated with AAD. RESULTS: Two regions were convincingly associated with AAD (p < 5 × 10-8): the MHC on 6p21, and 6q22.33. Fine-mapping of 6q22.33 identified two AAD-associated haplotypes in the region nearest to the genes encoding protein tyrosine phosphatase receptor kappa (PTPRK) and thymocyte-expressed molecule involved in selection (THEMIS). We examined the susceptibility to type 1 diabetes at these SNPs by performing a meta-analysis including 19,510 control participants. Although these SNPs were not associated with type 1 diabetes overall (p > 0.001), the SNP most associated with AAD, rs72975913, was associated with susceptibility to type 1 diabetes in those individuals diagnosed at less than 5 years old (p = 2.3 × 10-9). CONCLUSION/INTERPRETATION: PTPRK and its neighbour THEMIS are required for early development of the thymus, which we can assume influences the initiation of autoimmunity. Non-HLA genes may only be detectable as risk factors for the disease in individuals diagnosed under the age 5 years because, after that period of immune development, their role in disease susceptibility has become redundant. Hide abstract

Schofield EC, Carver T, Achuthan P, Freire-Pritchett P, Spivakov M, Todd JA, Burren OS. 2016. CHiCP: a web-based tool for the integrative and interactive visualization of promoter capture Hi-C datasets. Bioinformatics, 32 (16), pp. 2511-3. Read abstract | Read more

UNLABELLED: Promoter capture Hi-C (PCHi-C) allows the genome-wide interrogation of physical interactions between distal DNA regulatory elements and gene promoters in multiple tissue contexts. Visual integration of the resultant chromosome interaction maps with other sources of genomic annotations can provide insight into underlying regulatory mechanisms. We have developed Capture HiC Plotter (CHiCP), a web-based tool that allows interactive exploration of PCHi-C interaction maps and integration with both public and user-defined genomic datasets. AVAILABILITY AND IMPLEMENTATION: CHiCP is freely accessible from www.chicp.org and supports most major HTML5 compliant web browsers. Full source code and installation instructions are available from http://github.com/D-I-L/django-chicp CONTACT: ob219@cam.ac.uk. Hide abstract