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.
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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Enhancers and promoters are classically considered to be bound by a small set of transcription factors (TFs) in a sequence-specific manner. This assumption has come under increasing skepticism as the datasets of ChIP-seq assays of TFs have expanded. In particular, high-occupancy target (HOT) loci attract hundreds of TFs with often no detectable correlation between ChIP-seq peaks and DNA-binding motif presence. Here, we used a set of 1003 TF ChIP-seq datasets (HepG2, K562, H1) to analyze the patterns of ChIP-seq peak co-occurrence in combination with functional genomics datasets. We identified 43,891 HOT loci forming at the promoter (53%) and enhancer (47%) regions. HOT promoters regulate housekeeping genes, whereas HOT enhancers are involved in tissue-specific process regulation. HOT loci form the foundation of human super-enhancers and evolve under strong negative selection, with some of these loci being located in ultraconserved regions. Sequence-based classification analysis of HOT loci suggested that their formation is driven by the sequence features, and the density of mapped ChIP-seq peaks across TF-bound loci correlates with sequence features and the expression level of flanking genes. Based on the affinities to bind to promoters and enhancers we detected five distinct clusters of TFs that form the core of the HOT loci. We report an abundance of HOT loci in the human genome and a commitment of 51% of all TF ChIP-seq binding events to HOT locus formation thus challenging the classical model of enhancer activity and propose a model of HOT locus formation based on the existence of large transcriptional condensates.
