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Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration
Open accessAbstract
Genome-wide association studies (GWAS) have identified genetic variants at 34 loci contributing to age-related macular degeneration (AMD)1-3. 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 datasets 4,5. 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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“In the retina, S2R is expressed in several cell types including the retinal pigment epithelium cells, photoreceptors, and retinal ganglion cells [70,71,72,73,74].”
From Sigma-2 Receptors—From Basic Biology to Therapeutic Target: A Focus on Age-Related Degenerative Diseases (2023) · cites this paper as [72]
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Genetic determinants of monocyte splicing are enriched for disease susceptibility loci
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The human THRB thyroid hormone receptor gene and the puzzle of retinal disease phenotypes
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InfluentialRevisiting retinal and macular degeneration in the genomics era
Swaroop, A., & Roska, B. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-026-00960-4 · Cited by 2
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Self-citationTranscending genome-wide association studies to create useful multi-omic views of glaucoma
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Discovering root causal genes with high-throughput perturbations
Strobl, E. V., & Gamazon, E. R. (2025) · eLife · doi:10.1101/2024.01.13.574491 · Cited by 2
“We next ran RCSP on 137 samples collected from CD4+ T cells of multiple sclerosis (MS; GSE137143) as well as Perturb-seq data of 1,989,578 lymphoblasts, or the precursors of T cells and other lymphocytes 21,35 .”
Identifying X-chromosome variants associated with age-related macular degeneration
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Autoencoder-Transformed Transcriptome Improves Genotype-Phenotype Association Studies
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Mechanisms of tissue-specific genetic regulation revealed by latent factors across eQTLsPreprint
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“Additionally, numerous studies have leveraged eQTLs to characterize the molecular basis of complex phenotypic variation [6, 7, 8, 9, 10].”
BackgroundSelf-citationAnsell, B. R. E., Bonelli, R., Thomas, S. N., Manda, A., Ratnapriya, R., Pinelli, M., Swaroop, A., di Bernardo, D., Banfi, S., & Bahlo, M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.22.543349 · Cited by 2
“The signal at PILRA/B colocalizes with a retinal eQTL linked to increased expression of both genes (Ratnapriya et al. 2019)exm2246229; (Ratnapriya et al.”
BackgroundMethodResultInfluentialSelf-citationFoote, I. F., Flint, J. P., Fürtjes, A. E., Mullin, D. S., Fisk, J. D., Karakach, T. K., Rutenberg, A. D., Martin, N. G., Lupton, M. K., Llewellyn, D. J., et al. (2024) · medRxiv · doi:10.1101/2024.07.24.24310923 · Cited by 2
Integrating Multimorbidity into a Whole-Body Understanding of Disease Using Spatial Genomics
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The neuronal chaperone proSAAS is highly expressed in the retina
Schaffer, N., Mitias, S., Guo, Y., Bernstein, S. L., & Lindberg, I. (2025) · PLoS ONE · doi:10.1371/journal.pone.0321867 · Cited by 1
“Proteostatic dysregulation is a major component in age-related vision decline [3,4], as retinal neurons become progressively impaired in their ability to properly manage protein turnover [5].”
Vuori, N., Antikainen, A. A., Haukka, J. K., Harjutsalo, V., Groop, P.-H., Sandholm, N., & on behalf of the FinnDiane Study Group (2025) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.66.13.36 · Cited by 1
“To identify putative target genes, we used expression quantitative trait loci (eQTL) from a retinal transcriptomic database, Eye Genotype Expression (EyeGEx; downloaded from the GTEXportal.org in 8.7.2019), 32 and from the eQTL-Gen Consortium (https://eqtlgen.org/cis-eqtls.html).”
InfluentialIntegrative eQTL and Mendelian randomization analysis reveals key genes in colorectal cancer
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“[8] TWAS has been successfully applied to identify risk genes for a range of diseases, including age-related macular degeneration, [9] systemic lupus erythematosus, [10] and stroke.”
Precision Is Not Enough: When Tools Outpace Translation in Ocular Gene Therapy
Elsayed, M. E. A. A., & MacLaren, R. E. (2026) · Genes · doi:10.3390/genes17030283 · Cited by 1
“…expanded the number of implicated loci, with large-scale analyses identifying over 30–34 loci and more than 50 independently associated variants that influence AMD risk; importantly, many signals map to non-coding regions, consistent with a major contribution of gene-regulatory mechanisms [23–25].”
A Graphical Method for Identifying Gene Clusters From RNA Sequencing Data
Patock, J. R., Ratnapriya, R., & Barman, A. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00061 · Cited by 1
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“To bridge this gap, integrating genome-wide association study (GWAS) and expression quantitative trait locus (eQTL) data help unravel genetic mechanisms and identify risk genes for various diseases (17, 18).”
Cai, J., Jiang, Y., Liu, X., Yang, T., Yang, P., & Chen, L. (2026) · Autophagy · doi:10.1080/15548627.2026.2673559 · Cited by 1
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“…studies have uncovered high-risk variants in several genes involved in extracellular matrix maintenance and immune regulation, including HTRA1, 53 — 57 CFH, 57,58 TIMP3, 59,60 MMP-9 and MMP-2, 29,30,59,60 C3, 57,60,61 complement factor B (CFB), 62 — 64 complitment factor I, 65 — 67 and vitronectin.”
Das Komplementsystem: von den Grundlagen zur klinischen Bedeutung
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Systems Biology for Multiplatform Data Integration: An Overview
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Discovering root causal genes with high-throughput perturbationsPreprint
Strobl, E. V., & Gamazon, E. R. (2024) · eLife · doi:10.7554/elife.100949 · Cited by 1
“We next downloaded bulk RNA-seq datasets derived from patients with age-related macular degeneration (AMD; GSE115828) and multiple sclerosis (MS; GSE137143) 17,32 .”
Aman, A. M., Díaz‐Torres, S., Lee, S. S., Driessen, S. J., de Vries, V. A., van der Heide, F. C. T., Kolovos, A., Schmidt, J. M., Marshall, H. N., Saleh, L., et al. (2026) · medRxiv · doi:10.64898/2026.02.03.26345426 · Cited by 1
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Vrathasha, V., Pahl, M. C., Pippin, J. A., Nikonov, S. S., He, J., Halimitabrizi, M., Moksha, L., Salowe, R., Edziah, A.-A., Bradford, Y., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.20.707051 · Cited by 1
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Animal and Human Models of Retinal Diseases
Fletcher, E. L., Pébay, A., Rutar, M., Kakavand, K., Wang, A. Y., Vessey, K. A., Jobling, A. I., & Greferath, U. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-809324-5.24191-6
Novel missense mutation E585K in retinitis pigmentosa leads to compromised RPGR splicing diversityPreprint
Liu, Y., Wan, J., Ren, C., Xu, Z.-H., Pan, X., Pan, J., Gao, R.-N., Liu, S.-Q., Zhang, J., Yao, Q.-H., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.22.109884
“Analysis of published retina RNA-Seq data We used published RNA-seq data to investigate of in vivo RPGR splicing (Ratnapriya et al. 2019).”
MethodSubramanian, R., & Sahoo, D. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.28.317313
Duchen, D., & BEATY, T. L. (2021) · medRxiv · doi:10.1101/2020.12.28.20248962
“Encoding a component of the BLOC1 protein complex required for the biogenesis and function of the 172 endosomal-lysosomal system, BLOC1S1 was previously hypothesized to play a role in synaptic 173 function.(24) BLOC1S1’s putative function as a transporter between endosomes and lysosomes, and the 174 observation that a…”
BackgroundUnderstanding retinal diseases with genotypic and transcriptomic data analysis
Manda, S. A. P. (2021) · Minerva Access (University of Melbourne)
Bonelli, R., Ansell, B. R. E., Woods, S. M., Lockwood, S. H., Bishop, P. N., Khan, K. N., Bahlo, M., & Fruttiger, M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.13.496007
Wang, J.-H., Wong, R. C. B., & Liu, G. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4194124
SGCP: a spectral self-learning method for clustering genes in co-expression networks
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“SGC without semi-supervised cluster improvement) and SGC with three pipelines (WGCNA, CoExpNets, CEMiTool) on 12 gene expression datasets: 4 DNA-microarray datasets [18, 48, 47, 5] and 8 RNA-sequencing datasets [43, 29, 24, 9, 41, 35, 36, 42].”
MethodJayarathna, D. K. (2023) · Queensland University of Technology · doi:10.5204/thesis.eprints.237470
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Molecular Genetics of Primary Open-Angle Glaucoma
Aboobakar, I. F., Wiggs, J. L., & Segrè, A. V. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13820-1.00144-4
Discovering Root Causal Genes with High Throughput PerturbationsPreprint
Strobl, E. V., & Gamazon, E. R. (2024) · eLife · doi:10.7554/elife.100949.1
dong, Z., Jia, J., Ren, D., Yu, K., Zhao, D., & Wang, D. (2024) · Research Square · doi:10.21203/rs.3.rs-5133569/v1
Role of LIPIN 1 in regulating metabolic homeostasis in the retinal pigment epithelium
Usoltseva, A. S., Litwin, C., Lee, M., Hill, C., Cai, J., & Chen, Y. (2024) · The FASEB Journal · doi:10.1096/fj.202400981r
“Dysregulated lipid metabolism has been linked to the pathogenesis of AMD, 1,57–59 and therefore, we examined additional markers of AMD, with a particular focus on inflammatory markers, in frozen sections of posterior eyes.”
Uncovering Novel Drugs that Restore Vision Using Orthogonal Pooling in Zebrafish
O’Brien, J., Colucci, P., Álvarez, Y., & Kennedy, B. N. (2025) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-76550-6_80
Discovering Root Causal Genes with High Throughput PerturbationsPreprint
Strobl, E. V., & Gamazon, E. R. (2025) · eLife · doi:10.7554/elife.100949.2
Deep learning-based prediction of tissue-specific splice sites in the human neural retinaPreprint
Riepe, T., de Bruijn, S. E., Roosing, S., Cremers, F. P. M., & Hoen, P. A. C. ’. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.10.637427
VandenBosch, L. S., & Cherry, T. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.22.638679
“Genetic variants within CREs have additionally been associated with altered retinal gene expression by expression quantitative trait locus (eQTL) analyses[15, 16].”
Sampson, J., Segrè, A. V., Bujakowska, K. M., Haynes, S., Baralle, D., Banka, S., Black, G. C. M., Sergouniotis, P. I., & Ellingford, J. M. (2025) · medRxiv · doi:10.1101/2025.04.25.25326445
Novel compound heterozygous CNGA3 mutation associated with retinal cone dystrophy
Sun, R., Wang, Y., Zhou, W., Chen, S., Li, Y., Pan, M., Ji, D., Li, J., & Shi, X. (2025) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2025.12905
“The GTEx retina data are based on EyeGEx data reported by Ratnapriya et al (14), with transcript abundance estimated using Kallisto v0.”
Genome-Wide Insights into the Genes and Pathways Shaping Human Foveal DevelopmentPreprint
Hunt, C., Yoon, H.-J., Lirio, A., Coley, K., Wang, J., Shrine, N. R. G., Shao, J., Maconachie, G. D. E., Tu, Z., Zippin, J. H., et al. (2025) · medRxiv · doi:10.1101/2025.06.18.25329836
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Aman, A. M., MacGregor, S., Díaz‐Torres, S., & Gharahkhani, P. (2025) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.66.15.11
“In addition, we used eQTL obtained from post-mortem retinal tissues, 32 but this data set has limited power ( n = 453).”
Decoding cell-class specific roles of non-coding variants in human retina
VandenBosch, L. S., Leonardson, A. S., & Cherry, T. J. (2025) · Scientific Reports · doi:10.1038/s41598-025-30821-9
“Many GWAS and eQTL studies have identified hundreds of variants associated with retinal diseases and expression of retinal disease-linked genes, many of which are present in non-coding sequences across the genome 16,17,55 .”
IL-18 receptor axis in allergic conjunctivitis: a multi-omics study
Yuan, L., Su, W., Li, L., Xiang, Y., Xu, Z., Wang, Y., Hua, X., & Yuan, X. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1704591
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D’haene, E., López-Soriano, V., Martínez‐García, P. M., Kalayanamontri, S., Rey, A. D., Sousa‐Ortega, A., Naranjo, S., de Sompele, S. V., Vantomme, L., Mahieu, Q., et al. (2024) · DIGITAL.CSIC (Spanish National Research Council (CSIC)) · doi:10.6084/m9.figshare.26721996
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“54 The expression of these transcription factors was further checked in single cell RNAseq data from human retina and transcription factors with average log expression > 1 in Müller cells was further considered ( Table S8) .”
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“In addition to identifying genetic variants implicated in gene expression regulation, we next identified genes enriched in different AMD states by performing a pseudobulk differential expression analysis between control cells and each of early dry AMD, geographic atrophy, and MNV donors ( SI Files 2-4 ).”
Influential
Metadata
- Authors
- Ratnapriya, R., Sosina, O. A., Starostik, M. R., Kwicklis, M., Kapphahn, R. J., Fritsche, L. G., Walton, A., Arvanitis, M., Gieser, L., Pietraszkiewicz, A., Montezuma, S. R., Chew, E. Y., Battle, A., Abecasis, G. R., Ferrington, D. A., Chatterjee, N., & Swaroop, A.
- Affiliations
- National Institutes of Health (US)
- University of Minnesota (US)
- University of Michigan (US)
- Johns Hopkins University (US)
- Corresponding author
- Ferrington, Deborah A.
- Chatterjee, Nilanjan
- Swaroop, Anand
- ORCID
- Venue
- Nature Genetics
- Published
- 2019-02-11
- Volume
- 51
- Issue
- 4
- Pages
- 606-610
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC6441365
- OpenAlex
- W2914762802
- Cited by
- 312
- References
- 56
- Citations by year
- 2026: 47 · 2025: 49 · 2024: 46 · 2023: 40 · 2022: 35 · 2021: 36 · 2020: 46 · 2019: 13
- FWCI
- 31.89
- Type
- journal article
- Language
- en
- Update notices
- correction (2019-05-08)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Minnesota Lions Eye Bank
- University of Minnesota
- National Institutes of Health
- National Human Genome Research Institute (T32HG000040)
- National Eye Institute (ZIAEY000475, Z01EY000450, R01EY026012, ZIAEY000474, ZIAEY000450, R01EY028554)
- Topics
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Ophthalmology › Retinal Development and Disorders
- Keywords
- age-related macular degeneration, retinal transcriptome, expression quantitative trait loci, transcriptome-wide association study, cis-eQTL, RLBP1, HIC1, PARP12, non-coding RNA, ocular genetics
- MeSH terms
- Gene Expression Regulation, Genotype, Humans, Macular Degeneration, Phenotype, Retina, Case-Control Studies, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Gene Expression Profiling, Quantitative Trait Loci, Genome-Wide Association Study, Transcriptome
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