Functional insights into regulatory non-coding genetic variation associated with type 2 diabetes risk – NEXT-T2D

Type 2 diabetes (T2D) is a multifactorial polygenic disease characterised by the dysfunction of pancreatic islet cells in secreting insulin in response to the body’s needs. Although genome wide association studies (GWAS) have been successful in identifying > 300 loci associated with T2D risk, the majority of loci lie in non-coding regions, thus, the mechanistic and functional insights into how these regulatory non-coding variants impact disease pathogenesis remains elusive. We and other have demonstrated that combining transcriptomics and genotyping to identify expression quantitative trait loci (eQTLs), is a powerful approach and an important stepping stone in establishing the true genes targeted by T2D GWAS loci. In NEXT-T2D, we propose a systematic approach to first identify eQTLs, with a focus on T2D GWAS loci and related traits, and then to functionally characterise and validate these regions in relevant models. For this study, we will use the largest cohort of metabolically profiled living donors, which has several advantages over cadaveric organ donor islets (i.e., donors have not undergone stressful environment prior to islet procurement and RNA extracted using less stressful methods). We aim to identify eQTLs in > 400 living donors by combining genotyping from blood and transcriptomics using total RNA sequencing in pancreatic islets obtained using laser capture microdissection (Task 1). We will perform a loss-of-function screen (siRNA) on the identified target genes to assess their downstream effects on insulin secretion in the human pancreatic beta cell line EndoC-BH1 (Task 2). Then, we will directly explore the effect of the T2D GWAS regulatory non-coding regions in our best candidates by deleting them using Crispr-Cas9 in a physiologically-relevant in vitro model of human islets (Task 3). We believe that this comprehensive post-GWAS functional study is necessary for the identification of novel key target genes for T2D and beyond for precision diabetic medicine.