Genetic regulation of RNA splicing in human pancreatic islets
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
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 genetic variation has a widespread influence on 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.
Results: We examine splicing QTLs (sQTLs) in pancreatic islets from 399 human donors and observe that genetic variation has a widespread influence on 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.
Date Issued
2022-09-15
Date Acceptance
2022-08-23
Citation
Genome Biology, 2022, 23, pp.1-28
ISSN
1474-7596
Publisher
BMC
Start Page
1
End Page
28
Journal / Book Title
Genome Biology
Volume
23
Copyright Statement
© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits
use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original
author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third
party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the mate‑
rial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or
exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://
creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publi
cdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original
author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third
party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the mate‑
rial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or
exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://
creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publi
cdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
License URL
Sponsor
Wellcome Trust
Imperial College Healthcare NHS Trust- BRC Funding
Imperial College Healthcare NHS Trust- BRC Funding
Medical Research Council (MRC)
Medical Research Council (MRC)
Imperial College Healthcare NHS Trust- BRC Funding
Identifier
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02757-0
Grant Number
101033/C/13/Z
RDC03 79560
RDC03 79560
MR/L02036X/1
MR/L02036X/1
RDC03
Subjects
Beta cells
CTRB2
Diabetes pathophysiology
G-protein signaling
Pancreatic beta-cells
Pancreatic islets
Quantitative trait loci
RNA splicing
Senescence
TWAS
Type 1 diabetes
Type 2 diabetes
Diabetes Mellitus, Type 1
Diabetes Mellitus, Type 2
Exodeoxyribonucleases
Humans
Islets of Langerhans
Proinsulin
Protein Isoforms
RNA Splicing
RNA, Long Noncoding
T2DSystems Consortium
Islets of Langerhans
Humans
Diabetes Mellitus, Type 1
Diabetes Mellitus, Type 2
Proinsulin
Exodeoxyribonucleases
Protein Isoforms
RNA Splicing
RNA, Long Noncoding
05 Environmental Sciences
06 Biological Sciences
08 Information and Computing Sciences
Bioinformatics
Publication Status
Published
Date Publish Online
2022-09-15