Diabetes currently affects 415 million people worldwide. In the UK, there will be 5M people with type 2 diabetes (T2D) by 2025, accounting for 1 in 30 prescriptions and £25 billion in annual NHS costs. Prevalence rates are even higher in many other parts of the world: in some societies, adults with normal glucose levels are in the minority.
The basis for these ethnic differences in T2D risk, as well as the notable variation in the clinical phenotype and disease progression (including the risk of complications) remains poorly understood. It is likely to involve a combination of differences in genetic predisposition, differences in environmental risk factors, and differences in the interplay between these (for example, through their joint impact on early life events).
The McCarthy group is uniquely poised to answer these questions. They lead global consortia undertaking the largest ever transethnic studies of the genetics of type 2 diabetes (involving ~1.2M individuals with GWAS, 70K with exome sequence, 40K+ with genome sequence), and of birthweight (~350K individuals in the latest analysis). They have access to large biobanks that are informative on a wide range of lifestyle factors: these include the 500K individuals in UK Biobank, as well as collaborative links to similar data sets of East Asian, Hispanic and African American origin.
This DPhil project will involve a range of complementary approaches to use these, and other multiethnic data sets to understand the relationships between ethnic diversity, genetic predisposition, exposure to modifiable environmental risk factors, and the impact of adverse early life events. Components of the project might include, for example
Precise project details will depend on the interests and skills of the student, and the status of this work as of October 2019. 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 and the Big Data Institute. Key collaborators will be Dr Anubha Mahajan and Prof Andrew Morris (the latter a visiting Professor in Oxford). 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, BDI and other collaborating groups. The student will also have the opportunity, through existing collaborations, to interact with other world-leading groups active in human genetics, large-scale epidemiology and the development and implementation of relevant statistical methodologies. 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, the generation of additional genomic of functional data, or the extension of findings to additional data sets (eg from other ethnic groups). This project provides an opportunity for a highly-motivated student with strong computational and analytical skills, and an interest in global aspects of human health and 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.
Project reference number: 939
|Professor Mark McCarthy||Wellcome Trust Centre for Human Genetics||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|
|Dr Anubha Mahajan||NDM||University of Oxford||GBRfirstname.lastname@example.org|
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
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
Birth weight (BW) has been shown to be influenced by both fetal and maternal factors and in observational studies is reproducibly associated with future risk of adult metabolic diseases including type 2 diabetes (T2D) and cardiovascular disease. These life-course associations have often been attributed to the impact of an adverse early life environment. Here, we performed a multi-ancestry genome-wide association study (GWAS) meta-analysis of BW in 153,781 individuals, identifying 60 loci where fetal genotype was associated with BW (P < 5 × 10). Overall, approximately 15% of variance in BW was captured by assays of fetal genetic variation. Using genetic association alone, we found strong inverse genetic correlations between BW and systolic blood pressure (R = -0.22, P = 5.5 × 10), T2D (R = -0.27, P = 1.1 × 10) and coronary artery disease (R = -0.30, P = 6.5 × 10). In addition, using large -cohort datasets, we demonstrated that genetic factors were the major contributor to the negative covariance between BW and future cardiometabolic risk. Pathway analyses indicated that the protein products of genes within BW-associated regions were enriched for diverse processes including insulin signalling, glucose homeostasis, glycogen biosynthesis and chromatin remodelling. There was also enrichment of associations with BW in known imprinted regions (P = 1.9 × 10). We demonstrate that life-course associations between early growth phenotypes and adult cardiometabolic disease are in part the result of shared genetic effects and identify some of the pathways through which these causal genetic effects are mediated. Hide abstract
To gain insight into potential regulatory mechanisms through which the effects of variants at four established type 2 diabetes (T2D) susceptibility loci (CDKAL1, CDKN2A-B, IGF2BP2 and KCNQ1) are mediated, we undertook transancestral fine-mapping in 22 086 cases and 42 539 controls of East Asian, European, South Asian, African American and Mexican American descent. Through high-density imputation and conditional analyses, we identified seven distinct association signals at these four loci, each with allelic effects on T2D susceptibility that were homogenous across ancestry groups. By leveraging differences in the structure of linkage disequilibrium between diverse populations, and increased sample size, we localised the variants most likely to drive each distinct association signal. We demonstrated that integration of these genetic fine-mapping data with genomic annotation can highlight potential causal regulatory elements in T2D-relevant tissues. These analyses provide insight into the mechanisms through which T2D association signals are mediated, and suggest future routes to understanding the biology of specific disease susceptibility loci. Hide abstract