A single DNA letter change can raise or lower a person’s risk of developing type 2 diabetes, but finding that precise change among millions of possibilities is like searching for a typo in a library of books. The problem is that genetic studies routinely flag broad stretches of DNA linked to disease, but rarely pinpoint the exact causal variant. Without that precision, researchers cannot understand the biological mechanism—how a specific sequence difference alters insulin production or fat-cell function—nor translate that knowledge into clinical tools. This project aims to solve that by combining massive sequencing datasets from European and multiethnic populations with tissue-specific functional data from human pancreatic islets and fat. If successful, the work will produce a catalogue of causal variants for type 2 diabetes, each tied to a molecular or physiological pathway. That could eventually support clinical decision-making—for example, using a patient’s genetic profile to predict disease progression or response to treatment. The research is fundamentally curiosity-driven, but the fine-grained variant maps it generates are a necessary prerequisite for any future diagnostic or therapeutic application.
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Sequence-based discovery in human genetics will, it is expected, define the relationship between sequence variation and risk of common diseases such as type 2 diabetes (T2D), illuminate the biology of disease and advance translational objectives. However, substantial challenges attend each step of this process: sequence- or genotype-derived association signals must first be converted into causal alleles; those alleles need to be functionally-characterised; and the biological insights gleaned tra nsmuted into clinically-valuable endpoints. The research which forms the basis of this Senior Investigator proposal will seek to address these challenges. It builds on the track record of the applicant and the unparalleled amounts of data being generated by the applicant within several internationally-leading, collaborative genetic and genomic studies (including the Trust- and NIH-funded GoT2D project). Specific goals include: - identification and characterisation ('fine-mapping') of causal alleles at known and novel T2D-risk loci through deep genotyping, whole genome and targeted resequencing, and imputation applied to massive European-descent and multiethnic data sets; - integration of genetic data with biological annotations from generic (e.g. ENCODE) and focused, tissue-specific, analyses (e.g. within human islets and fat) to define both proximal (molecular) and downstream (physiological) mechanisms of disease pathogenesis; and to explore these processes further throug h (collaborative) functional analyses in human subjects and model (animal, cellular) systems; - fine-grained evaluation of genotype-phenotype relationships at selected loci of interest, using targeted sequencing within multiethnic samples to generate comprehensive inventories of variation, in part to define the extent to which sequence data can support clinical decision-making in T2D.
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