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


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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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.

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Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.


  1. 1.

    Fritsche, L. G. et al. Age-related macular degeneration: genetics and biology coming together. Annu. Rev. Genomics Hum. Genet. 15, 151–171 (2014).

  2. 2.

    Grassmann, F., Ach, T., Brandl, C., Heid, I. M. & Weber, B. H. F. What does genetics tell us about age-related macular degeneration? Annu. Rev. Vis. Sci. 1, 73–96 (2015).

  3. 3.

    Fritsche, L. G. et al. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Nat. Genet. 48, 134–143 (2016).

  4. 4.

    Small, K. S. et al. Identification of an imprinted master trans regulator at the KLF14 locus related to multiple metabolic phenotypes. Nat. Genet. 43, 561–564 (2011).

  5. 5.

    Battle, A., Brown, C. D., Engelhardt, B. E. & Montgomery, S. B. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

  6. 6.

    Olsen, T. W. & Feng, X. The Minnesota Grading System of eye bank eyes for age-related macular degeneration. Invest. Ophthalmol. Vis. Sci. 45, 4484–4490 (2004).

  7. 7.

    Ferris, F. L. et al. A simplified severity scale for age-related macular degeneration: AREDS Report No. 18. Arch. Ophthalmol. 123, 1570–1574 (2005).

  8. 8.

    Pinelli, M. et al. An atlas of gene expression and gene co-regulation in the human retina. Nucleic Acids Res. 44, 5773–5784 (2016).

  9. 9.

    Hoang, Q. V., Linsenmeier, R. A., Chung, C. K. & Curcio, C. A. Photoreceptor inner segments in monkey and human retina: mitochondrial density, optics, and regional variation. Vis. Neurosci. 19, 395–407 (2002).

  10. 10.

    Curcio, C. A., Sloan, K. R., Kalina, R. E. & Hendrickson, A. E. Human photoreceptor topography. J. Comp. Neurol. 292, 497–523 (1990).

  11. 11.

    Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).

  12. 12.

    Gamazon, E. R. et al. Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation. Nat. Genet. 50, 956–967 (2018).

  13. 13.

    Hormozdiari, F. et al. Colocalization of GWAS and eQTL signals detects target genes. Am. J. Hum. Genet. 99, 1245–1260 (2016).

  14. 14.

    Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

  15. 15.

    Strunz, T. et al. A mega-analysis of expression quantitative trait loci (eQTL) provides insight into the regulatory architecture of gene expression variation in liver. Sci. Rep 8, 5865 (2018).

  16. 16.

    Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).

  17. 17.

    Gandal, M. J. et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359, 693–697 (2018).

  18. 18.

    Beck, T., Hastings, R. K., Gollapudi, S., Free, R. C. & Brookes, A. J. GWAS Central: a comprehensive resource for the comparison and interrogation of genome-wide association studies. Eur. J. Hum. Genet. 22, 949–952 (2014).

  19. 19.

    MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896–D901 (2017).

  20. 20.

    Chakravarti, A., Clark, A. G. & Mootha, V. K. Distilling pathophysiology from complex disease genetics. Cell 155, 21–26 (2013).

  21. 21.

    Gallagher, M. D. & Chen-Plotkin, A. S. The post-GWAS era: from association to function. Am. J. Hum. Genet. 102, 717–730 (2018).

  22. 22.

    Brown, C. D., Mangravite, L. M. & Engelhardt, B. E. Integrative modeling of eQTLs and cis-regulatory elements suggests mechanisms underlying cell type specificity of eQTLs. PLoS Genet. 9, e1003649 (2013).

  23. 23.

    Kozma, K. et al. Identification and characterization of aβ1,3-glucosyltransferase that synthesizes the Glc-β1,3-Fuc disaccharide on thrombospondin type 1 repeats. J. Biol. Chem. 281, 36742–36751 (2006).

  24. 24.

    Lesnik Oberstein, S. A. et al. Peters Plus syndrome is caused by mutations in B3GALTL, a putative glycosyltransferase. Am. J. Hum. Genet. 79, 562–566 (2006).

  25. 25.

    Langemeyer, L. & Ungermann, C. BORC and BLOC-1: shared subunits in trafficking complexes. Dev. Cell 33, 121–122 (2015).

  26. 26.

    Mullin, A. P. et al. Gene dosage in the dysbindin schizophrenia susceptibility network differentially affect synaptic function and plasticity. J. Neurosci. 35, 325–338 (2015).

  27. 27.

    Hormozdiari, F. et al. Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits. Nat. Genet. 50, 1041–1047 (2018).

  28. 28.

    Decanini, A., Nordgaard, C. L., Feng, X., Ferrington, D. A. & Olsen, T. W. Changes in select redox proteins of the retinal pigment epithelium in age-related macular degeneration. Am. J. Ophthalmol. 143, 607–615 (2007).

  29. 29.

    Gagnon-Bartsch, J. A. & Speed, T. P. Using control genes to correct for unwanted variation in microarray data. Biostatistics 13, 539–552 (2012).

  30. 30.

    Leek, J. T. svaseq: removing batch effects and other unwanted noise from sequencing data. Nucleic Acids Res. 42, e161 (2014).

  31. 31.

    Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).

  32. 32.

    Eisenberg, E. & Levanon, E. Y. Human housekeeping genes, revisited. Trends Genet. 29, 569–574 (2013).

  33. 33.

    Scherer, A. (ed.) Batch Effects and Noise in Microarray Experiments: Sources and Solutions (Wiley, West Sussex, UK, 2009).

  34. 34.

    Melé, M. et al. The human transcriptome across tissues and individuals. Science 348, 660–665 (2015).

  35. 35.

    Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

  36. 36.

    The Gene Ontology Consortium. Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Res. 45, D331–D338 (2017).

  37. 37.

    Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

  38. 38.

    Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

  39. 39.

    Roberts, A., Pimentel, H., Trapnell, C. & Pachter, L. Identification of novel transcripts in annotated genomes using RNA-Seq. Bioinformatics 27, 2325–2329 (2011).

  40. 40.

    Delaneau, O. et al. A complete tool set for molecular QTL discovery and analysis. Nat. Commun. 8, 15452 (2017).

  41. 41.

    Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).

  42. 42.

    Shabalin, A. A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).

  43. 43.

    Beasley, T. M., Erickson, S. & Allison, D. B. Rank-based inverse normal transformations are increasingly used, but are they merited? Behav. Genet. 39, 580–595 (2009).

  44. 44.

    Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006).

  45. 45.

    Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

  46. 46.

    Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

  47. 47.

    Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

  48. 48.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

  49. 49.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

  50. 50.

    Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

  51. 51.

    Newman, A. M. et al. Systems-level analysis of age-related macular degeneration reveals global biomarkers and phenotype-specific functional networks. Genome Med. 4, 16 (2012).

  52. 52.

    Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

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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).

Author information

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.

Competing interests

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

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

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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Further reading

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.