Identification of causal genes and their shared pathways is essential for understanding the biology underpinning disease susceptibility and for guiding therapeutic intervention in type 1 diabetes (T1D). Approximately 13% of the human reference genome has regions of low mapability and is enriched with structural variants (SVs), such as inversions, insertions and deletions. Work from developmental disorders has demonstrated that SV can modulate the regulatory landscape in the human genome with consequences for gene expression and disease pathogenesis. The role of SVs in common diseases such as T1D is a relatively underexplored area of research. We will utilise the latest methods and technologies (e.g Bionano Irys and 10x linked-reads) to generate high resolution phased haplotype maps that are inclusive of both SVs and single nucleotide polymorphisms. We will integrate these novel datasets with the largest genome wide association study in T1D to date, RNA gene expression, methylation state, 3D chromatin conformation maps and chromatin phenotypes –to better define the T1D causal genes.
There will be opportunity to learn and develop skills in human genetics. In a collaborative project, combining expertise from groups in the WHG and the WIMM, the successful applicant will have the opportunity to develop a key set of skills, combining state of the art wet-lab methods with computational/bioinformatics and statistics.
Project reference number: 880
|Professor John A Todd FRS FMedSci||Wellcome Trust Centre for Human Genetics||Oxford University, Henry Wellcome Building of Genomic Medicine||GBRemail@example.com|
|Professor Linda Wicker||Wellcome Trust Centre for Human Genetics||Oxford University,||firstname.lastname@example.org|
|Dr Antony Cutler||NDM, WHG||University of Oxford||GBRemail@example.com|
|Mr David Flores||NDM, WHG||University of Oxford||GBRfirstname.lastname@example.org|
Genome-wide association studies are transformative in revealing the polygenetic basis of common diseases, with autoimmune diseases leading the charge. Although the field is just over 10 years old, advances in understanding the underlying mechanistic pathways of these conditions, which result from a dense multifactorial blend of genetic, developmental and environmental factors, have already been informative, including insights into therapeutic possibilities. Nevertheless, the challenge of identifying the actual causal genes and pathways and their biological effects on altering disease risk remains for many identified susceptibility regions. It is this fundamental knowledge that will underpin the revolution in patient stratification, the discovery of therapeutic targets and clinical trial design in the next 20 years. Here we outline recent advances in analytical and phenotyping approaches and the emergence of large cohorts with standardized gene-expression data and other phenotypic data that are fueling a bounty of discovery and improved understanding of human physiology. Hide abstract
BACKGROUND: Autoimmune disease-associated variants are preferentially found in regulatory regions in immune cells, particularly CD4 T cells. Linking such regulatory regions to gene promoters in disease-relevant cell contexts facilitates identification of candidate disease genes. RESULTS: Within 4 h, activation of CD4 T cells invokes changes in histone modifications and enhancer RNA transcription that correspond to altered expression of the interacting genes identified by promoter capture Hi-C. By integrating promoter capture Hi-C data with genetic associations for five autoimmune diseases, we prioritised 245 candidate genes with a median distance from peak signal to prioritised gene of 153 kb. Just under half (108/245) prioritised genes related to activation-sensitive interactions. This included IL2RA, where allele-specific expression analyses were consistent with its interaction-mediated regulation, illustrating the utility of the approach. CONCLUSIONS: Our systematic experimental framework offers an alternative approach to candidate causal gene identification for variants with cell state-specific functional effects, with achievable sample sizes. Hide abstract