Journal
COMMUNICATIONS BIOLOGY
Volume 4, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s42003-021-02594-0
Keywords
-
Categories
Funding
- NeCTAR Research Cloud
- Wellcome Trust [076113, 085475]
- Common Fund of the Office of the Director of the National Institutes of Health, NCI
- Common Fund of the Office of the Director of the National Institutes of Health, NHGRI
- Common Fund of the Office of the Director of the National Institutes of Health, NHLBI
- Common Fund of the Office of the Director of the National Institutes of Health, NIDA
- Common Fund of the Office of the Director of the National Institutes of Health, NIMH
- Common Fund of the Office of the Director of the National Institutes of Health, NINDS
- NeSI's collaborator institutions
- Ministry of Business, Innovation & Employment's Research Infrastructure programme
- Sir Colin Giltrap Liggins Institute Scholarship
- Ministry of Business, Innovation and Employment of New Zealand [UOAX1611]
- National Collaborative Research Infrastructure Strategy
- New Zealand Ministry of Business, Innovation & Employment (MBIE) [UOAX1611] Funding Source: New Zealand Ministry of Business, Innovation & Employment (MBIE)
Ask authors/readers for more resources
A machine learning approach was developed to rank tissue-specific gene regulatory effects for T1D SNPs, identifying an eQTL associated with changes to AP4B1-AS1 transcript levels in lung tissue as making the largest gene regulatory contribution to T1D risk. The strongest tissue-specific eQTL effects were observed in lung tissue, potentially explaining associations between respiratory infections and risk of islet autoantibody seroconversion in young children.
Type 1 diabetes (T1D) etiology is complex. We developed a machine learning approach that ranked the tissue-specific transcription regulatory effects for T1D SNPs and estimated their relative contributions to conversion to T1D by integrating case and control genotypes (Wellcome Trust Case Control Consortium and UK Biobank) with tissue-specific expression quantitative trait loci (eQTL) data. Here we show an eQTL (rs6679677) associated with changes to AP4B1-AS1 transcript levels in lung tissue makes the largest gene regulatory contribution to the risk of T1D development. Luciferase reporter assays confirmed allele-specific enhancer activity for the rs6679677 tagged locus in lung epithelial cells (i.e. A549 cells; C > A reduces expression, p = 0.005). Our results identify tissue-specific eQTLs for SNPs associated with T1D. The strongest tissue-specific eQTL effects were in the lung and may help explain associations between respiratory infections and risk of islet autoantibody seroconversion in young children. Ho, Nyaga et al. develop a machine learning approach for ranking tissue-specific gene regulatory affects, used here for type 1 diabetes SNPs. They identify the lung as a site where these regulatory impacts can be most impactful, which may contribute to understanding the link between respiratory issues and risk of islet autoantibody seroconvernsion.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available