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

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

This article has been updated


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.

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.


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

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

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

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    CAS  Article  Google Scholar 

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

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

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

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

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

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

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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

Authors and Affiliations



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).

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