Large-scale data integration to advance mechanistic inference and precision medicine in type 2 diabetes

Advances in understanding the genetic and genomic basis of complex diseases have had limited impact on the delivery of translational goals, including those concerning personalised management. Recently, we have shown that, by integrating information on quantitative trait associations and tissue-specific regulatory annotation, genetic variants influencing type 2 diabetes (T2D) predisposition can be characterised in terms of the pathophysiological processes through which they operate. The central hypothesis of this proposal is that this allows a deconstruction of T2D pathophysiology that addresses phenotypic and clinical heterogeneity, promotes mechanistic insights, and reveals novel translational opportunities. The approach begins with generation of “process-based” genetic risk scores that better capture patterns of individual T2D-predisposition and phenotype. I will refine these risk scores, more precisely characterise the cellular, molecular and physiological events they reflect, and describe their relationships to clinical outcomes. For multifactorial diseases, there are limits to the clinical prediction achievable through genetics alone: I will combine genetic risk scores with measures of individual external and internal environment, and with clinical and biomarker data, to generate “integrated risk profiles”.This approach aims to advance understanding of the pathophysiological basis of T2D and deliver precise, personalised information for key clinical outcomes including complication risk and therapeutic response.