Genetic regulation of RNA splicing in human pancreatic islets

Background: Non-coding genetic variants that influence gene transcription in pancreatic islets play a major role in the susceptibility to type 2 diabetes (T2D), and likely also contribute to type 1 diabetes (T1D) risk. For many loci, however, the mechanisms through which non-coding variants influence diabetes susceptibility are unknown. Results: We examine splicing QTLs (sQTLs) in pancreatic islets from 399 human donors and observe that common genetic variation has a widespread influence on the splicing of genes with established roles in islet biology and diabetes. In parallel, we profile expression QTLs (eQTLs) and use transcriptome-wide association as well as genetic co-localization studies to assign islet sQTLs or eQTLs to T2D and T1D susceptibility signals, many of which lack candidate effector genes. This analysis reveals biologically plausible mechanisms, including the association of T2D with an sQTL that creates a nonsense isoform in ERO1B, a regulator of ER-stress and proinsulin biosynthesis. The expanded list of T2D risk effector genes reveals overrepresented pathways, including regulators of G-protein-mediated cAMP production. The analysis of sQTLs also reveals candidate effector genes for T1D susceptibility such as DCLRE1B, a senescence regulator, and lncRNA MEG3. Conclusions: These data expose widespread effects of common genetic variants on RNA splicing in pancreatic islets. The results support a role for splicing variation in diabetes susceptibility, and offer a new set of genetic targets with potential therapeutic benefit.This research was supported by Ministerio de Ciencia e Innovación (BFU2014-54284-R, RTI2018-095666-B-I00), Medical Research Council (MR/L02036X/1), a Wellcome Trust Senior Investigator Award (WT101033), European Research Council Advanced Grant (789055), EU Horizon 2020 TDSystems (667191), ESPACE (874710), and Marie Sklodowska-Curie (643062, ZENCODE). S.B.G was supported by a Juan de la Cierva postdoctoral fellowship (MINECO; FJCI-2017-32090). M.C.A was supported by a Boehringer Ingelheim Fonds PhD fellowship. Work in CRG was supported by the CERCA Programme, Generalitat de Catalunya, Centro de Excelencia Severo Ochoa (CEX2020-001049), and support of the Spanish Ministry of Science and Innovation to the EMBL partnership. Work in Imperial College was supported by NIHR Imperial Biomedical Research Centre. M.I. was supported by a European Research Council consolidator award (101002275). D.J.M.C. and J.A.T. were supported by JDRF grants 9-2011-253, 5-SRA-2015-130-A-N, 4- SRA-2017-473-A-N, and Wellcome grants 091157/Z/10/Z and 107212/Z/15/Z, to the Diabetes and Inflammation Laboratory, Oxford, as well as the Oxford Biomedical Research Computing (BMRC) facility, a joint development between the Wellcome Centre for Human Genetics and the Big Data Institute supported by Health Data Research UK and NIHR Oxford Biomedical Research Centre, and Wellcome Trust Core Award grant 203141/Z/16/Z. D.M.J.C analysis with the UK Biobank Resource was conducted under Application 31295. A.L.G. is a Wellcome Senior Fellow in Basic Biomedical Science and was supported by the Wellcome Trust (095101, 200837, 106130, 203141), the NIDDK (U01DK105535 and UM1 DK126185), and the Oxford NIHR Biomedical Research Centre