Integration of genomics and transcriptomics predicts diabetic retinopathy susceptibility genes
- Ann and Robert H Lurie Children's Hospital of Chicago,, United States
- NeoGenomics Laboratories, United States
- University Of Illinois at Chicago, United States
- The University of Chicago, United States
- Johns Hopkins University, United States
- National Institutes of Health, United States
- The George Washington University, United States
- Northwestern University Feinberg School of Medicine, United States
Abstract
We determined differential gene expression in response to high glucose in lymphoblastoid cell lines derived from matched individuals with type 1 diabetes with and without retinopathy. Those genes exhibiting the largest difference in glucose response were assessed for association to diabetic retinopathy in a genome-wide association study meta-analysis. Expression Quantitative Trait Loci (eQTLs) of the glucose response genes were tested for association with diabetic retinopathy. We detected an enrichment of the eQTLs from the glucose response genes among small association p-values and identified FLCN as a susceptibility gene for diabetic retinopathy. Expression of FLCN in response to glucose was greater in individuals with diabetic retinopathy. Independent cohorts of individuals with diabetes revealed an association of FLCN eQTLs to diabetic retinopathy. Mendelian randomization confirmed a direct positive effect of increased FLCN expression on retinopathy. Integrating genetic association with gene expression implicated FLCN as a disease gene for diabetic retinopathy.
Data availability
Source files and code for all the figures and tables have been provided, except for drawings, flowcharts and histopathology findings. We have also included links and references where appropriate.Figure 3 source data 5 and 6 are available on Dryad at https://doi.org/10.5061/dryad.zkh18938jAdditional data files can be found here: microarray expression data at Gene Expression Omnibus (GEO) under accession code GSE146615 and diabetic retinopathy GWAS data at UKBB archive (https://oxfile.ox.ac.uk/oxfile/work/extBox?id=825146B4380F72048D).
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Figure 3 additional source data filesDryad Digital Repository, doi:10.5061/dryad.zkh18938j.
Article and author information
Author details
Funding
National Eye Institute (R01EY023644)
- Michael A Grassi
National Eye Institute (ZIAEY000546)
- Anand Swaroop
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- David E James, The University of Sydney, Australia
Version history
- Received: June 13, 2020
- Accepted: November 6, 2020
- Accepted Manuscript published: November 9, 2020 (version 1)
- Version of Record published: December 10, 2020 (version 2)
Copyright
© 2020, Skol et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Integration of genomics and transcriptomics predicts diabetic retinopathy susceptibility genes
eLife 9:e59980.
https://doi.org/10.7554/eLife.59980
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