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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2018.00535

Type 1 diabetes mellitus-associated genetic variants contribute to overlapping immune regulatory networks

  • 1Liggins Institute, University of Auckland, New Zealand
  • 2Starship Children's Health, New Zealand

Type 1 diabetes (T1D) is a chronic metabolic disorder characterized by the autoimmune destruction of insulin-producing pancreatic islet beta cells in genetically predisposed individuals. Genome-wide association studies (GWAS) have identified over 60 risk regions across the human genome, marked by single nucleotide polymorphisms (SNPs), which confer genetic predisposition to T1D. There is increasing evidence that disease-associated SNPs can alter gene expression through spatial interactions that involve distal loci, in a tissue- and development-specific manner. Here, we used three-dimensional (3D) genome organization data to identify genes that physically co-localized with DNA regions that contained T1D-associated SNPs in the nucleus. Analysis of these SNP-gene pairs using the Genotype-Tissue Expression database identified a subset of SNPs that significantly affected gene expression. We identified 247 spatially regulated genes including HLA-DRB1, LAT, MICA, BTN3A2, CTLA4, CD226, NOTCH1, TRIM26, PTEN, TYK2, CTSH, and FLRT3, which exhibit tissue-specific effects in multiple tissues. We observed that the T1D-associated variants interconnect through networks that form part of the immune regulatory pathways, including immune-cell activation, cytokine signaling, and programmed cell death protein-1 (PD-1). Our results implicate T1D-associated variants in tissue and cell-type specific regulatory networks that contribute to pancreatic beta-cell inflammation and destruction, adaptive immune signaling, and immune-cell proliferation and activation. A number of other regulatory changes we identified are not typically considered to be central to the pathology of T1D. Collectively, our data represent a novel resource for the hypothesis-driven development of diagnostic, prognostic and therapeutic interventions in T1D.

Keywords: type 1 diabetes, Genome wide association studies (CWAS), Genetic Variation, genome organization, Expression QTL (eQTL), Autoimmunity

Received: 13 Aug 2018; Accepted: 22 Oct 2018.

Edited by:

Zhifu Sun, Mayo Clinic, United States

Reviewed by:

Haiquan Li, University of Arizona, United States
Nicholas Larson, Mayo Clinic, United States  

Copyright: © 2018 Nyaga, Vickers, Jefferies, Perry and O'Sullivan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Justin M. O'Sullivan, Liggins Institute, University of Auckland, Auckland, 1142, Auckland, New Zealand, justin.osullivan@auckland.ac.nz