Background: Novel therapeutic strategies need to be informed by a more complete understanding of the molecular and physiological basis of type 2 diabetes, designed to deliver validated interventional targets and biomarkers that can be used to measure disease risk, progression, and subtype. We are using human genetics and genomics to deliver this understanding. Through leadership of global consortia we have assembled massive genetic data sets informative for T2D status (1.2M samples with GWAS; 800K with exome array; 70K with exome sequence data; 40K (with more coming) with whole genome sequence). We have identified over 500 genetic loci influencing T2D and closely-related traits.
Most of these map to regulatory sequence, and many act through perturbation of pancreatic islet function. To define the molecular and cellular processes through which these risk variants operate, we have established the most detailed map to date of islet regulatory features incorporating RNA-Seq, ChiP-Seq, ATAC-Seq, whole genome bisulphite sequencing and Capture-C conformational capture. Through intersection of these data, we have identified many of the key regulatory networks influencing T2D predisposition and the genes and molecular networks through which they operate.
The research that we are proposing for this DPhil project will build upon this work and be based around one or more of the following approaches. Examples of possible components include
Precise project details will depend on the interests and skills of the student, and the status of this work as of October 2018. This work is funded by the Wellcome Trust and the US National Institutes of Health.
The DPhil would be based primarily at the Wellcome Trust Centre for Human Genetics but with strong interactions with colleagues at the Oxford Centre for Diabetes Endocrinology and Metabolism (Anna Gloyn, Fredrik Karpe) and the Weatherall Institute of Molecular Medicine (Doug Higgs, Jim Hughes). The student will receive training in diverse aspects of complex trait genetics, and will benefit from the strong computational and statistical focus of the WTCHG, and the expertise in molecular and cellular work at the WIMM and OCDEM. They will also have the opportunity, through existing collaborations, to interact with other leading groups in human genetics and statistical approaches. Through the strong network of diabetes collaborators in Oxford and beyond the student will be well-placed to further develop their understanding of related biology. The core of the project is computational and statistical and the student will deploy and develop their skills in the management of complex large biomedical and genomic data sets. Depending on interest and aptitude, the student will have the possibility to pursue follow-up of the findings that emerge in a variety of alternative directions, through a focus on the application of in silico methods (bioinformatics), the generation of additional genomic data, and/or empirical functional studies. This project provides an opportunity for a highly-motivated student with good computational and analytical skills, and an interest in human biology, to train in one of the internationally-leading centres at a uniquely-exciting time in the development of human genetics.
As well as the specific training detailed above, students will have access to a wide-range of seminars and training opportunities through the many research institutes and centres based in Oxford.
Generic skills training is offered through the Medical Sciences Division's Skills Training Programme. This programme offers a comprehensive range of courses covering many important areas of researcher development: knowledge and intellectual abilities, personal effectiveness, research governance and organisation, and engagement, influence and impact. Students are actively encouraged to take advantage of the training opportunities available to them.
Genes, Genetics, Epigenetics & Genomics and Molecular, Cell & Systems Biology
Project reference number: 938
|Prof Mark McCarthy||OCDEM||Oxford University, Oxford Centre for Diabetes, Endocrinology & Metabolism||GBRemail@example.com|
|Professor Andrew P Morris||Wellcome Trust Centre for Human Genetics||Oxford University, Henry Wellcome Building of Genomic Medicine||GBR||A.P.Morris@liverpool.ac.uk|
|Prof Anna L Gloyn||OCDEM||Oxford University, Oxford Centre for Diabetes, Endocrinology & Metabolism||GBRfirstname.lastname@example.org|
Type 2 diabetes affects over 300 million people, causing severe complications and premature death, yet the underlying molecular mechanisms are largely unknown. Pancreatic islet dysfunction is central in type 2 diabetes pathogenesis, and understanding islet genome regulation could therefore provide valuable mechanistic insights. We have now mapped and examined the function of human islet cis-regulatory networks. We identify genomic sequences that are targeted by islet transcription factors to drive islet-specific gene activity and show that most such sequences reside in clusters of enhancers that form physical three-dimensional chromatin domains. We find that sequence variants associated with type 2 diabetes and fasting glycemia are enriched in these clustered islet enhancers and identify trait-associated variants that disrupt DNA binding and islet enhancer activity. Our studies illustrate how islet transcription factors interact functionally with the epigenome and provide systematic evidence that the dysregulation of islet enhancers is relevant to the mechanisms underlying type 2 diabetes. Hide abstract
The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes. Hide abstract
We performed fine mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in or near KCNQ1. 'Credible sets' of the variants most likely to drive each distinct signal mapped predominantly to noncoding sequence, implying that association with T2D is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine mapping implicated rs10830963 as driving T2D association. We confirmed that the T2D risk allele for this SNP increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease. Hide abstract
To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS), including 26,488 cases and 83,964 controls of European, east Asian, south Asian and Mexican and Mexican American ancestry. We observed a significant excess in the directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals of association from the trans-ethnic meta-analysis in an additional 21,491 cases and 55,647 controls of European ancestry, we identified seven new T2D susceptibility loci. Furthermore, we observed considerable improvements in the fine-mapping resolution of common variant association signals at several T2D susceptibility loci. These observations highlight the benefits of trans-ethnic GWAS for the discovery and characterization of complex trait loci and emphasize an exciting opportunity to extend insight into the genetic architecture and pathogenesis of human diseases across populations of diverse ancestry. Hide abstract