Integration of genomics and transcriptomics predicts diabetic retinopathy susceptibility genes

  1. Andrew D Skol
  2. Segun C Jung
  3. Ana Marija Sokovic
  4. Siquan Chen
  5. Sarah Fazal
  6. Olukayode Sosina
  7. Poulami P Borkar
  8. Amy Lin
  9. Maria Sverdlov
  10. Dingcai Cao
  11. Anand Swaroop
  12. Ionut Bebu
  13. DCCT/ EDIC Study group
  14. Barbara E Stranger  Is a corresponding author
  15. Michael A Grassi  Is a corresponding author
  1. Ann and Robert H Lurie Children's Hospital of Chicago,, United States
  2. NeoGenomics Laboratories, United States
  3. University Of Illinois at Chicago, United States
  4. The University of Chicago, United States
  5. Johns Hopkins University, United States
  6. National Institutes of Health, United States
  7. The George Washington University, United States
  8. 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).

The following data sets were generated
    1. Skol A et al
    (2020) Figure 3 additional source data files
    Dryad Digital Repository, doi:10.5061/dryad.zkh18938j.
The following previously published data sets were used

Article and author information

Author details

  1. Andrew D Skol

    Department of Pathology and Laboratory Medicine, Ann and Robert H Lurie Children's Hospital of Chicago,, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Segun C Jung

    Research and Development, NeoGenomics Laboratories, Aliso Viejo, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Ana Marija Sokovic

    Ophthalmology and Visual Sciences, University Of Illinois at Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Siquan Chen

    Cellular Screening Center, The University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Sarah Fazal

    Cellular Screening Center, The University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Olukayode Sosina

    Department of Biostatistics, Johns Hopkins University, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Poulami P Borkar

    Ophthalmology and Visual Sciences, University Of Illinois at Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Amy Lin

    Ophthalmology and Visual Sciences, University Of Illinois at Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Maria Sverdlov

    Research Histology and Tissue Imaging Core, University Of Illinois at Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Dingcai Cao

    Ophthalmology and Visual Sciences, University Of Illinois at Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Anand Swaroop

    National Eye Institute, National Institutes of Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1975-1141
  12. Ionut Bebu

    Biostatistics Center, The George Washington University, Rockville, United States
    Competing interests
    The authors declare that no competing interests exist.
  13. DCCT/ EDIC Study group

  14. Barbara E Stranger

    Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, United States
    For correspondence
    barbara.stranger@northwestern.edu
    Competing interests
    The authors declare that no competing interests exist.
  15. Michael A Grassi

    Ophthalmology and Visual Sciences, University Of Illinois at Chicago, Chicago, United States
    For correspondence
    grassim@uic.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8467-3223

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

  1. David E James, The University of Sydney, Australia

Version history

  1. Received: June 13, 2020
  2. Accepted: November 6, 2020
  3. Accepted Manuscript published: November 9, 2020 (version 1)
  4. 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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  1. Andrew D Skol
  2. Segun C Jung
  3. Ana Marija Sokovic
  4. Siquan Chen
  5. Sarah Fazal
  6. Olukayode Sosina
  7. Poulami P Borkar
  8. Amy Lin
  9. Maria Sverdlov
  10. Dingcai Cao
  11. Anand Swaroop
  12. Ionut Bebu
  13. DCCT/ EDIC Study group
  14. Barbara E Stranger
  15. Michael A Grassi
(2020)
Integration of genomics and transcriptomics predicts diabetic retinopathy susceptibility genes
eLife 9:e59980.
https://doi.org/10.7554/eLife.59980

Share this article

https://doi.org/10.7554/eLife.59980

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