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Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration

Nature Genetics volume 51, pages 606–610 (2019)Cite this article

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An Author Correction to this article was published on 08 May 2019

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Abstract

Genome-wide association studies (GWAS) have identified genetic variants at 34 loci contributing to age-related macular degeneration (AMD)1,2,3. We generated transcriptional profiles of postmortem retinas from 453 controls and cases at distinct stages of AMD and integrated retinal transcriptomes, covering 13,662 protein-coding and 1,462 noncoding genes, with genotypes at more than 9 million common SNPs for expression quantitative trait loci (eQTL) analysis of a tissue not included in Genotype-Tissue Expression (GTEx) and other large datasets4,5. Cis-eQTL analysis identified 10,474 genes under genetic regulation, including 4,541 eQTLs detected only in the retina. Integrated analysis of AMD-GWAS with eQTLs ascertained likely target genes at six reported loci. Using transcriptome-wide association analysis (TWAS), we identified three additional genes, RLBP1, HIC1 and PARP12, after Bonferroni correction. Our studies expand the genetic landscape of AMD and establish the Eye Genotype Expression (EyeGEx) database as a resource for post-GWAS interpretation of multifactorial ocular traits.

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Fig. 1: EyeGEx: retinal transcriptome and eQTL analyses.
Fig. 2: Genes and variants associated with AMD, on the basis of retina eQTL data (n = 406 retinas) and summary-level AMD-GWAS data (based on z scores of two-sided t tests on 33,976 individuals)3.

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Data availability

The sequencing data are available at Gene Expression Omnibus (GEO) under accession code GSE115828 and NEI Commons (see URLs). The GTEx data used here were obtained from the GTEx Portal on 26 March 2018 and/or dbGaP accession number phs000424.v7.p2.

Change history

  • 08 May 2019

    In the version of this article initially published, in Supplementary Data 5, the logFC, FC, P value and adjusted P value for advanced AMD versus control (DE 4/1) without age correction did not correspond to the correct gene IDs. The errors have been corrected in the HTML version of the article.

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Acknowledgements

The authors acknowledge B. Weber for providing liver eQTL data. We thank members of the laboratory of A.S., especially H. Yang, J. Bryan, A. Police Reddy, and F. Giuste for assistance, and the Lions Gift of Sight members for procuring human retina tissue. This work was supported by the Intramural Research Program of the National Eye Institute EY000450, EY000474, and EY000475 (to A.S.), NIH grants EY028554 and EY026012, The Lindsay Family Foundation, an anonymous benefactor, and the Minnesota Lions Vision Foundation (to D.A.F.), and the Johns Hopkins Bloomberg Distinguished Professorship Endowment (to N.C.). This study used the high-performance computational capabilities of the Biowulf Linux cluster (see URLs).

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Author notes

  1. These authors contributed equally: Rinki Ratnapriya, Olukayode A. Sosina, Margaret R. Starostik.

Authors and Affiliations

  1. Neurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD, USA

    Rinki Ratnapriya, Olukayode A. Sosina, Margaret R. Starostik, Madeline Kwicklis, Ashley Walton, Linn Gieser, Alexandra Pietraszkiewicz & Anand Swaroop

  2. Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA

    Olukayode A. Sosina & Nilanjan Chatterjee

  3. Department of Ophthalmology and Visual Neurosciences, University of Minnesota, Minneapolis, MN, USA

    Rebecca J. Kapphahn, Sandra R. Montezuma & Deborah A. Ferrington

  4. Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA

    Lars G. Fritsche & Gonçalo R. Abecasis

  5. Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Marios Arvanitis

  6. Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MD, USA

    Emily Y. Chew

  7. Departments of Biomedical Engineering and Computer Science, Johns Hopkins University, Baltimore, MD, USA

    Alexis Battle

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Contributions

Overall conceptualization: R.R. and A.S.; clinical and tissue resources: D.A.F., R.J.K., S.R.M., and E.Y.C.; transcriptome data: R.R., M.R.S., L.G., A.W., and A.P.; genotyping data: L.G.F. and G.R.A.; bioinformatic analysis: M.R.S., R.R., M.K., and A.W.; eQTL analysis: O.A.S., N.C., M.A., and A.B.; statistical supervision: N.C.; data curation: M.R.S.; writing original draft: R.R., M.R.S., O.A.S., M.K., N.C., and A.S.; writing, review, and editing: all authors; funding: D.A.F. and A.S.; supervision and project administration: A.S.

Corresponding authors

Correspondence to Deborah A. Ferrington, Nilanjan Chatterjee or Anand Swaroop.

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Competing interests

G.R.A. is now employed by Regeneron Pharmaceuticals. The other authors declare no competing interests.

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Supplementary information

Supplementary Text and Figures

Supplementary Note, Supplementary Figures 1–8 and Supplementary Table 1

Reporting Summary

Supplementary Data 1

Summary of retina donor characteristics (n = 453)

Supplementary Data 2

Summary of genes and pathways in the retina transcriptome (n = 105 MGS1 donor retinas)

Supplementary Data 3

Summary of eQTL analysis (n = 406)

Supplementary Data 4

Summary of transcriptome-wide association study

Supplementary Data 5

Summary of changes in gene expression and pathways in AMD

Supplementary Data 6

WGCNA module attributes and input literature genes

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Ratnapriya, R., Sosina, O.A., Starostik, M.R. et al. Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration. Nat Genet 51, 606–610 (2019). https://doi.org/10.1038/s41588-019-0351-9

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