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Genome-wide association analyses identify distinct genetic architectures for age-related macular degeneration across ancestries

Nature Genetics volume 56, pages 2659–2671 (2024)Cite this article

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Abstract

To effectively reduce vision loss due to age-related macular generation (AMD) on a global scale, knowledge of its genetic architecture in diverse populations is necessary. A critical element, AMD risk profiles in African and Hispanic/Latino ancestries, remains largely unknown. We combined data in the Million Veteran Program with five other cohorts to conduct the first multi-ancestry genome-wide association study of AMD and discovered 63 loci (30 novel). We observe marked cross-ancestry heterogeneity at major risk loci, especially in African-ancestry populations which demonstrate a primary signal in a major histocompatibility complex class II haplotype and reduced risk at the established CFH and ARMS2/HTRA1 loci. Dissecting local ancestry in admixed individuals, we find significantly smaller marginal effect sizes for CFH risk alleles in African ancestry haplotypes. Broadening efforts to include ancestrally distinct populations helped uncover genes and pathways that boost risk in an ancestry-dependent manner and are potential targets for corrective therapies.

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Fig. 1: Overview of AMD GWAS meta-analysis primary and secondary analyses.
Fig. 2: Penetrance and pleiotropy of the AMD PRS.
Fig. 3: GWAS analyses identify 27 novel loci in EA and the first loci in AA and HA.
Fig. 4: MR of pigmentation traits on AMD risk in European ancestries (n = 57,290 cases, 324,430 controls).
Fig. 5: Marked cross-ancestry heterogeneity at major AMD risk loci.
Fig. 6: Local ancestry analysis of the CFH and ARMS2/HTRA1 loci.
Fig. 7: Regional Miami plot of genetically regulated gene and isoform expression at the 1q32 (CD46/CD55) locus.

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Data availability

The full summary-level association data from the multi-ancestry meta-analysis, ancestry-stratified meta-analyses and individual population association analyses in the MVP, Genentech and the UK Biobank are available via the dbGaP study accession number phs001672.v12.p1. IAMDGC summary statistics are available through http://eaglep.case.edu/iamdgc_web/. GERA AMD GWAS summary statistics are available by reasonable request to Hélène Choquet (Helene.Choquet@kp.org). Polygenic scores based on EA and AA GWAS are deposited in the PGS Catalog (https://www.pgscatalog.org/) with accessions PGS004606 and PGS004607. Data used for brain transcriptome model generation are available from PsychENCODE (http://resource.psychencode.org/); genotypes are controlled data and access instructions are provided at https://www.synapse.org/#!Synapse:syn4921369/wiki/477467. STARNET-based EpiXcan transcriptomic imputation models are available for download at https://labs.icahn.mssm.edu/roussos-lab/resources/. Data used for GTEx-based transcriptomic imputation models are available at https://www.gtexportal.org/home/datasets. MSigDB: http://software.broadinstitute.org/gsea/msigdb. For ancestry-specific linkage disequilibrium r2 with top SNPs in Regional TWAS Miami plots, 1000 G Phase3 v5 was used available at http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/. REMC: https://egg2.wustl.edu/roadmap/web_portal/. GO datasets were retrieved from the Gary Bader lab (http://download.baderlab.org/EM_Genesets/current_release/Human/entrezgene/GO/). Version Human_GOALL_with_GO_iea_July_01_2021_entrezgene.gmt was used.

Code availability

Software and analytical methods used in data analyses include PrediXcan (https://github.com/hakyimlab/PredictDB-Tutorial) to generate PrediXcan transcriptomic imputation models, EpiXcan (https://bitbucket.org/roussoslab/epixcan/src/master/) to generate EpiXcan transcriptomic imputation models, S-PrediXcan (https://github.com/hakyimlab/MetaXcan) to perform TWAS, PLINK2 (https://www.cog-genomics.org/plink/2.0/) for on-demand generation of ancestry-specific linkage disequilibrium r2 with top SNPs in Regional TWAS Miami plots, GOGO (https://github.com/zwang-bioinformatics/GOGO) for semantic enrichment analysis, and R v.4.2.2 for statistical analyses and plotting (https://www.R-project.org).

References

  1. Fleckenstein, M. et al. Age-related macular degeneration. Nat. Rev. Dis. Primers 7, 31 (2021).

    Article  PubMed  Google Scholar 

  2. Vanderbeek, B. L. et al. Racial differences in age-related macular degeneration rates in the United States: a longitudinal analysis of a managed care network. Am. J. Ophthalmol. 152, 273–282.e3 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Mahr, M. A., Hodge, D. O. & Erie, J. C. Racial differences in age-related macular degeneration and associated anti-vascular endothelial growth factor intravitreal injections among Medicare beneficiaries. Ophthalmol. Retina 2, 1188–1195 (2018).

    Article  PubMed  Google Scholar 

  4. Wang, D., Jiang, Y., He, M., Scheetz, J. & Wang, W. Disparities in the global burden of age-related macular degeneration: An analysis of trends from 1990 to 2015. Curr. Eye Res. 44, 657–663 (2019).

    Article  PubMed  Google Scholar 

  5. Haines, J. L. et al. Complement factor H variant increases the risk of age-related macular degeneration. Science 308, 419–421 (2005).

    Article  PubMed  CAS  Google Scholar 

  6. Klein, R. J. et al. Complement factor H polymorphism in age-related macular degeneration. Science 308, 385–389 (2005).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Edwards, A. O. et al. Complement factor H polymorphism and age-related macular degeneration. Science 308, 421–424 (2005).

    Article  PubMed  CAS  Google Scholar 

  8. 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).

    Article  PubMed  CAS  Google Scholar 

  9. Gaziano, J. M. et al. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70, 214–223 (2016).

    Article  PubMed  Google Scholar 

  10. Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C. A. & Smoller, J. W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Pietzner, M. et al. Genetic architecture of host proteins involved in SARS-CoV-2 infection. Nat. Commun. 11, 6397 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Kumar, P., Henikoff, S. & Ng, P. C. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4, 1073–1081 (2009).

    Article  PubMed  CAS  Google Scholar 

  14. Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Komatsu, M. & Ruoslahti, E. R-Ras is a global regulator of vascular regeneration that suppresses intimal hyperplasia and tumor angiogenesis. Nat. Med. 11, 1346–1350 (2005).

    Article  PubMed  CAS  Google Scholar 

  16. Sawada, J., Li, F. & Komatsu, M. R-Ras protein inhibits autophosphorylation of vascular endothelial growth factor receptor 2 in endothelial cells and suppresses receptor activation in tumor vasculature. J. Biol. Chem. 290, 8133–8145 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Vähätupa, M. et al. Lack of R-Ras leads to increased vascular permeability in ischemic retinopathy. Invest. Ophthalmol. Vis. Sci. 57, 4898–4909 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Surendran, P. et al. Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension. Nat. Genet. 48, 1151–1161 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Campbell, R. A. et al. Placental HTRA1 cleaves α1-antitrypsin to generate a NET-inhibitory peptide. Blood 138, 977–988 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Fawcett, K. A. et al. Pleiotropic associations of heterozygosity for the SERPINA1 Z allele in the UK Biobank. ERJ Open Res. 7, 00049–2021 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Yin, X. et al. Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci. Nat. Commun. 13, 1644 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Simcoe, M. J. et al. Genome-wide association study identifies two common loci associated with pigment dispersion syndrome/pigmentary glaucoma and implicates myopia in its development. Ophthalmology 129, 626–636 (2022).

    Article  PubMed  Google Scholar 

  23. Simcoe, M. et al. Genome-wide association study in almost 195,000 individuals identifies 50 previously unidentified genetic loci for eye color. Sci. Adv. 7, eabd1239 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Hysi, P. G. et al. Genome-wide association meta-analysis of individuals of European ancestry identifies new loci explaining a substantial fraction of hair color variation and heritability. Nat. Genet. 50, 652–656 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Liyanage, U. E. et al. Combined analysis of keratinocyte cancers identifies novel genome-wide loci. Hum. Mol. Genet. 28, 3148–3160 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Visconti, A. et al. Genome-wide association study in 176,678 Europeans reveals genetic loci for tanning response to sun exposure. Nat. Commun. 9, 1684 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Burgess, S. & Thompson, S. G. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur. J. Epidemiol. 32, 377–389 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Lundgren, S. et al. Tissue distribution of human gp330/megalin, a putative Ca2+-sensing protein. J. Histochem. Cytochem. 45, 383–392 (1997).

    Article  PubMed  CAS  Google Scholar 

  29. Storm, T., Heegaard, S., Christensen, E. I. & Nielsen, R. Megalin-deficiency causes high myopia, retinal pigment epithelium-macromelanosomes and abnormal development of the ciliary body in mice. Cell Tissue Res. 358, 99–107 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Noel, N. C. L. & MacDonald, I. M. RP1L1 and inherited photoreceptor disease: A review. Surv. Ophthalmol. 65, 725–739 (2020).

    Article  PubMed  Google Scholar 

  31. Han, B. & Eskin, E. Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies. Am. J. Hum. Genet. 88, 586–598 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Zeng, B. et al. Multi-ancestry eQTL meta-analysis of human brain identifies candidate causal variants for brain-related traits. Nat. Genet. 54, 161–169 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Maples, B. K., Gravel, S., Kenny, E. E. & Bustamante, C. D. RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference. Am. J. Hum. Genet. 93, 278–288 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Atkinson, E. G. et al. Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power. Nat. Genet. 53, 195–204 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Hughes, A. E. et al. A common CFH haplotype, with deletion of CFHR1 and CFHR3, is associated with lower risk of age-related macular degeneration. Nat. Genet. 38, 1173–1177 (2006).

    Article  PubMed  CAS  Google Scholar 

  38. Sivakumaran, T. A. et al. A 32 kb critical region excluding Y402H in CFH mediates risk for age-related macular degeneration. PLoS ONE 6, e25598 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Holmes, L. V. et al. Determining the population frequency of the CFHR3/CFHR1 deletion at 1q32. PLoS ONE 8, e60352 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Cipriani, V. et al. Increased circulating levels of factor H-related protein 4 are strongly associated with age-related macular degeneration. Nat. Commun. 11, 778 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Pappas, C. M. et al. Protective chromosome 1q32 haplotypes mitigate risk for age-related macular degeneration associated with the CFH-CFHR5 and ARMS2/HTRA1 loci. Hum. Genomics 15, 60 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Cipriani, V. et al. Beyond factor H: the impact of genetic-risk variants for age-related macular degeneration on circulating factor-H-like 1 and factor-H-related protein concentrations. Am. J. Hum. Genet. 108, 1385–1400 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Zheng, X. et al. HIBAG–HLA genotype imputation with attribute bagging. Pharmacogenomics J. 14, 192–200 (2014).

    Article  PubMed  CAS  Google Scholar 

  44. Megiorni, F. & Pizzuti, A. HLA-DQA1 and HLA-DQB1 in celiac disease predisposition: practical implications of the HLA molecular typing. J. Biomed. Sci. 19, 88 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Kutszegi, N. et al. HLA-DRB1*07:01-HLA-DQA1*02:01-HLA-DQB1*02:02 haplotype is associated with a high risk of asparaginase hypersensitivity in acute lymphoblastic leukemia. Haematologica 102, 1578–1586 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Fernandez, C. A. et al. HLA-DRB1*07:01 is associated with a higher risk of asparaginase allergies. Blood 124, 1266–1276 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Kutszegi, N. et al. Two tagging single-nucleotide polymorphisms to capture HLA-DRB1*07:01-DQA1*02:01-DQB1*02:02 haplotype associated with asparaginase hypersensitivity. Br. J. Clin. Pharmacol. 87, 2542–2548 (2021).

    Article  PubMed  CAS  Google Scholar 

  48. Mullins, R. F., Russell, S. R., Anderson, D. H. & Hageman, G. S. Drusen associated with aging and age-related macular degeneration contain proteins common to extracellular deposits associated with atherosclerosis, elastosis, amyloidosis, and dense deposit disease. FASEB J. 14, 835–846 (2000).

    Article  PubMed  CAS  Google Scholar 

  49. Zhang, W. et al. Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits. Nat. Commun. 10, 3834 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Ratnapriya, R. et al. Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration. Nat. Genet. 51, 606–610 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

    Article  Google Scholar 

  53. Franzén, O. et al. Cardiometabolic risk loci share downstream cis- and trans-gene regulation across tissues and diseases. Science 353, 827–830 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Liu, Y. et al. ACAT: a fast and powerful p value combination method for rare-variant analysis in sequencing studies. Am. J. Hum. Genet. 104, 410–421 (2019).

  55. Sjöstedt, E. et al. An atlas of the protein-coding genes in the human, pig, and mouse brain. Science 367, eaay5947 (2020).

    Article  PubMed  Google Scholar 

  56. Strunz, T. et al. A transcriptome-wide association study based on 27 tissues identifies 106 genes potentially relevant for disease pathology in age-related macular degeneration. Sci. Rep. 10, 1584 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Hunter-Zinck, H. et al. Genotyping array design and data quality control in the Million Veteran Program. Am. J. Hum. Genet. 106, 535–548 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Nédélec, Y. et al. Genetic ancestry and natural selection drive population differences in immune responses to pathogens. Cell 167, 657–669 (2016).

    Article  PubMed  Google Scholar 

  59. Randolph, H. E. et al. Genetic ancestry effects on the response to viral infection are pervasive but cell type specific. Science 374, 1127–1133 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Klein, B. E. K. et al. Sunlight exposure, pigmentation, and incident age-related macular degeneration. Invest. Ophthalmol. Vis. Sci. 55, 5855–5861 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Halladay, C. W. et al. Genetically-guided algorithm development and sample size optimization for age-related macular degeneration cases and controls in electronic health records from the VA Million Veteran Program. AMIA Jt Summits Transl. Sci. Proc. 2019, 153–162 (2019).

    PubMed  PubMed Central  Google Scholar 

  62. Fang, H. et al. Harmonizing genetic ancestry and self-identified race/ethnicity in genome-wide association studies. Am. J. Hum. Genet. 105, 763–772 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Mbatchou, J. et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nat. Genet. 53, 1097–1103 (2021).

    Article  PubMed  CAS  Google Scholar 

  65. Yang, J., Zaitlen, N. A., Goddard, M. E., Visscher, P. M. & Price, A. L. Advantages and pitfalls in the application of mixed-model association methods. Nat. Genet. 46, 100–106 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Chen, H. et al. Control for population structure and relatedness for binary traits in genetic association studies via logistic mixed models. Am. J. Hum. Genet. 98, 653–666 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Shen, L. et al. Differences in the genetic susceptibility to age-related macular degeneration clinical subtypes. Invest. Ophthalmol. Vis. Sci. 56, 4290–4299 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Jorgenson, E. et al. Common coding variants in the HLA-DQB1 region confer susceptibility to age-related macular degeneration. Eur. J. Hum. Genet. 24, 1049–1055 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  72. 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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Heinze, G. & Schemper, M. A solution to the problem of separation in logistic regression. Stat. Med. 21, 2409–2419 (2002).

    Article  PubMed  Google Scholar 

  74. Delaneau, O., Zagury, J.-F., Robinson, M. R., Marchini, J. L. & Dermitzakis, E. T. Accurate, scalable and integrative haplotype estimation. Nat. Commun. 10, 5436 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Mao, X. et al. A genomewide admixture mapping panel for Hispanic/Latino populations. Am. J. Hum. Genet. 80, 1171–1178 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  78. McInnes, L., Healy, J., Saul, N. & Großberger, L. UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3, 861 (2018).

  79. Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Mizrahi-Man, O. et al. Novel genotyping algorithms for rare variants significantly improve the accuracy of Applied BiosystemsTM AxiomTM array genotyping calls: Retrospective evaluation of UK Biobank array data. PLoS ONE 17, e0277680 (2022).

  81. Cirulli, E. T. et al. Genome-wide rare variant analysis for thousands of phenotypes in over 70,000 exomes from two cohorts. Nat. Commun. 11, 542 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  83. Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    Article  PubMed  CAS  Google Scholar 

  84. Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).

  85. Brown, B. C., Asian Genetic Epidemiology Network Type 2 Diabetes Consortium, Ye, C. J., Price, A. L. & Zaitlen, N. Transethnic genetic-correlation estimates from summary statistics. Am. J. Hum. Genet. 99, 76–88 (2016).

  86. Carroll, R. J., Bastarache, L. & Denny, J. C. R PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment. Bioinformatics 30, 2375–2376 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Fullard, J. F. et al. Single-nucleus transcriptome analysis of human brain immune response in patients with severe COVID-19. Genome Med. 13, 118 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  88. Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. Barbeira, A. N. et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat. Commun. 9, 1825 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

  92. 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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  94. Gene Ontology Consortium. The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Res. 49, D325–D334 (2021).

  95. Zhao, C. & Wang, Z. GOGO: An improved algorithm to measure the semantic similarity between gene ontology terms. Sci. Rep. 8, 15107 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  96. Charrad, M., Ghazzali, N., Boiteau, V. & Niknafs, A. NbClust: an R package for determining the relevant number of clusters in a data set. J. Stat. Softw. 61, 1–36 (2014).

    Article  Google Scholar 

  97. Murtagh, F. & Legendre, P. Ward’s hierarchical agglomerative clustering method: which algorithms implement ward’s criterion? J. Classif. 31, 274–295 (2014).

    Article  Google Scholar 

  98. Rousseeuw, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987).

  99. Feinerer, I., Hornik, K. & Meyer, D. Text mining infrastructure in R. J. Stat. Softw. 25, 1–54 (2008).

    Article  Google Scholar 

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Acknowledgements

The unsung heroes of this publication are the US veterans who participated in this study and contributed their time and effort to improve the lives of all veterans and populations worldwide. Key individuals who collected, curated or cleaned data for the MVP are listed in the Supplementary Information. Dr. Igo passed away during the drafting of this paper and is remembered for his tireless contributions. We are further grateful to Catherine Tcheandjieu and Elizabeth Atkinson for advice on local ancestry inference and Tractor implementation, and to Jonathan Haines and Rachel Mester (UCLA) for comments on an earlier version of the manuscript. Finally, we wish to acknowledge the UK Biobank Eye and Vision Consortium who helped collect the eye phenotypes in the UK Biobank. This research has been conducted using the UK Biobank Resource under Application Number 2112. Support from the VA Office of Research & Development is acknowledged by N.S.P. (I01BX003364, I01BX04557, IK6BX005233), P.R. (I01BX004189) and S.J.F. (IK6BX005787). We acknowledge NIH support to P.R. (R01AG050986, R01AG065582, R01AG067025, R01MH125246, U01MH116442), to G.V. (K08MH122911), to H.C. and E.J. (R01EY027004) and to R.B.M., J.Y. and H.C. (RC2AG036607). We acknowledge NIH Core Grants to the Departments of Ophthalmology at Case Western Reserve University (P30EY011373) and Cleveland Clinic School of Medicine at Case Western Reserve University (P30EY025585) and unrestricted support from Research to Prevent Blindness to the Departments of Ophthalmology at Case Western Reserve University, Cleveland Clinic School of Medicine at Case Western Reserve University, and SUNY Buffalo. The IAMDGC acknowledges support from the NIH (R01EY022310, X01HG006934). This project was supported by the Clinical and Translational Science Collaborative of Cleveland (UL1TR002548). S.K.I. was supported by the International Retinal Research Foundation. P.G.H. received support from the BrightFocus Foundation. This research was supported by the Robert Wood Johnson Foundation, Research Program on Genes, the Environment and Health Wayne and Gladys Valley Foundation, the Ellison Medical Foundation and Kaiser Permanente Community Benefit Programs. The UK Biobank Eye and Vision Consortium is supported by grants from Moorfields Eye Charity, The NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, the Alcon Research Institute, and the International Glaucoma Association (UK). The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the Department of Veterans Affairs or any agency of the US federal government.

Author information

Author notes

  1. These authors contributed equally: Bryan R. Gorman, Georgios Voloudakis, Robert P. Igo Jr.

  2. These authors jointly supervised this work: Panos Roussos, Neal S. Peachey, Sudha K. Iyengar.

  3. Deceased: Robert P. Igo, Jr.

Authors and Affiliations

  1. Center for Data and Computational Sciences (C-DACS), VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA

    Bryan R. Gorman, Kyriacos Markianos, Frederick Dong, Patrick A. Schreiner & Saiju Pyarajan

  2. Booz Allen Hamilton, McLean, VA, USA

    Bryan R. Gorman, Frederick Dong & Patrick A. Schreiner

  3. Center for Disease Neurogenomics, Department of Psychiatry; Friedman Brain Institute; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Georgios Voloudakis, Biao Zeng, Sanan Venkatesh, Wen Zhang & Panos Roussos

  4. Center for Precision Medicine and Translational Therapeutics, VISN 2 Mental Illness Research, Education, and Clinical Center (MIRECC), James J. Peters Veterans Affairs Medical Center, New York/New Jersey VA Health Care Network, Bronx, NY, USA

    Georgios Voloudakis, Sanan Venkatesh & Panos Roussos

  5. Research Service, VA Northeast Ohio Healthcare System, Cleveland, OH, USA

    Robert P. Igo Jr., Tyler Kinzy, Jessica N. Cooke Bailey, Dana C. Crawford, Elisa Bala, Neal S. Peachey & Sudha K. Iyengar

  6. Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA

    Robert P. Igo Jr., Tyler Kinzy, Jessica N. Cooke Bailey, Dana C. Crawford, Jessica N. Cooke Bailey, Reneé Laux, Sudha K. Iyengar & Sudha K. Iyengar

  7. Center of Innovation in Long Term Services and Supports, VA Providence Healthcare System, Providence, RI, USA

    Christopher W. Halladay

  8. Research Service, VA New York Harbor Healthcare System, Brooklyn, NY, USA

    Tim B. Bigdeli

  9. Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA

    Tim B. Bigdeli

  10. Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA

    Jessica N. Cooke Bailey, Dana C. Crawford, Jessica N. Cooke Bailey, Sudha K. Iyengar & Sudha K. Iyengar

  11. Department of Genetics & Genome Sciences, Case Western Reserve University, Cleveland, OH, USA

    Jessica N. Cooke Bailey, Dana C. Crawford, Jessica N. Cooke Bailey, Sudha K. Iyengar & Sudha K. Iyengar

  12. Eye Clinic, VA Northeast Ohio Healthcare System, Cleveland, OH, USA

    Tamer Hadi

  13. Department of Ophthalmology and Visual Sciences, University Hospitals Eye Institute, Cleveland, OH, USA

    Sudha K. Iyengar, Tamer Hadi & Sudha K. Iyengar

  14. Eye Clinic, VA Western NY Healthcare System, Buffalo, NY, USA

    Matthew D. Anger

  15. Ophthalmology, Jacobs School of Medicine and Biomedical Sciences, SUNY-University at Buffalo, Buffalo, NY, USA

    Matthew D. Anger, Steven J. Fliesler & Jack M. Sullivan

  16. Department of Human Genetics, Genentech, South San Francisco, CA, USA

    Amy Stockwell & Brian L. Yaspan

  17. Department of Ophthalmology, Kaiser Permanente Northern California, Redwood City, CA, USA

    Ronald B. Melles

  18. Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA

    Jie Yin & Hélène Choquet

  19. Southampton Eye Unit, University Hospital Southampton National Health Service Foundation Trust, Southampton, UK

    Rebecca Kaye & Andrew J. Lotery

  20. Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK

    Angela J. Cree, Srinivas V. Goverdhan, Andrew J. Lotery, Rebecca Kaye & Andrew J. Lotery

  21. Section of Ophthalmology, School of Life Course Sciences, King’s College London, London, UK

    Karina Patasova & Pirro G. Hysi

  22. Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK

    Karina Patasova & Pirro G. Hysi

  23. National Institute for Health and Care Research Biomedical Research Centre, Moorfields Eye Hospital National Health Service Foundation Trust, London, UK

    Praveen J. Patel

  24. Institute of Ophthalmology, University College London, London, UK

    Valentina Cipriani & Praveen J. Patel

  25. Regeneron Genetics Center, Tarrytown, NY, USA

    Eric Jorgenson

  26. UCL Great Ormond Street Institute of Child Health, King’s College London, London, UK

    Pirro G. Hysi

  27. Sørlandet Sykehus Arendal, Arendal Hospital, Arendal, Norway

    Pirro G. Hysi

  28. Million Veteran Program Coordinating Center, VA Boston Healthcare System, Boston, MA, USA

    J. Michael Gaziano

  29. Division of Aging, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    J. Michael Gaziano

  30. VA Palo Alto Epidemiology Research and Information Center for Genomics, VA Palo Alto Health Care System, Palo Alto, CA, USA

    Philip S. Tsao & Themistocles L. Assimes

  31. Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Philip S. Tsao & Themistocles L. Assimes

  32. Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA

    Philip S. Tsao

  33. Research Service, VA Western NY Healthcare System, Buffalo, NY, USA

    Steven J. Fliesler & Jack M. Sullivan

  34. Biochemistry, Jacobs School of Medicine and Biomedical Sciences, SUNY-University at Buffalo, Buffalo, NY, USA

    Steven J. Fliesler

  35. Graduate Program in Neurosciences, Jacobs School of Medicine and Biomedical Sciences, SUNY-University at Buffalo, Buffalo, NY, USA

    Steven J. Fliesler & Jack M. Sullivan

  36. Section of Ophthalmology, VA Providence Healthcare System, Providence, RI, USA

    Paul B. Greenberg

  37. Division of Ophthalmology, Alpert Medical School, Brown University, Providence, RI, USA

    Paul B. Greenberg

  38. Section of Cardiology, Medical Service, VA Providence Healthcare System, Providence, RI, USA

    Wen-Chih Wu

  39. Division of Cardiology, Department of Medicine, Alpert Medical School, Brown University, Providence, RI, USA

    Wen-Chih Wu

  40. Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Saiju Pyarajan

  41. Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA

    Stephanie A. Hagstrom & Neal S. Peachey

  42. Department of Ophthalmology, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA

    Neal S. Peachey

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

    Lars G. Fritsche, Sebanti Sengupta, Jennifer L. Bragg-Gresham, Xiaowei Zhan, Alan M. Kwong, Johanna R. Foerster & Gonçalo R. Abecasis

  44. Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany

    Wilmar Igl, Mathias Gorski, Klaus Stark, Caroline Brandl, Matthias Olden & Iris M. Heid

  45. Institute of Human Genetics, University of Regensburg, Regensburg, Germany

    Felix Grassman, Caroline Brandl & Bernhard H. F. Weber

  46. Department of Internal Medicine–Nephrology, Kidney Epidemiology and Cost Center, University of Michigan, Ann Arbor, MI, USA

    Jennifer L. Bragg-Gresham

  47. Menzies Research Institute Tasmania, School of Medicine, University of Tasmania, Hobart, Tasmania, Australia

    Kathryn P. Burdon, Brendan J. Vote, David A. Mackey & Alex W. Hewitt

  48. Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, WI, USA

    Scott J. Hebring, Terrie Kitchner & Murray H. Brilliant

  49. Department of Ophthalmology, University of California, San Diego, CA, USA

    Cindy Wen, Hongrong Luo, Daniel Chen, Ouyang Ouyang, Ken Flagg, Daniel Lin, Guanping Mao, Henry Ferreyra & Kang Zhang

  50. Veterans Affairs San Diego Healthcare System, La Jolla, CA, USA

    Cindy Wen, Hongrong Luo, Daniel Chen, Ouyang Ouyang, Ken Flagg, Daniel Lin, Guanping Mao, Henry Ferreyra & Kang Zhang

  51. Department of Ophthalmology, Retina Service, Massachusetts Eye and Ear, Harvard University, Boston, MA, USA

    Ivana K. Kim

  52. Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    David Cho & Dwight Stambolian

  53. Department of Ophthalmology, Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Donald Zack, Hendrik P. N. Scholl & Peter Campochiaro

  54. Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Donald Zack

  55. Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Donald Zack & Peter Campochiaro

  56. Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Donald Zack

  57. Institut de la Vision, Université Pierre et Marie Curie, Paris, France

    Donald Zack

  58. Hôpital Intercommunal de Créteil, Hôpital Henri Mondor, Université Paris Est Créteil, Créteil, France

    Eric Souied

  59. Department of Ophthalmology, University of Bonn, Bonn, Germany

    Hendrik P. N. Scholl & Frank G. Holz

  60. Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA

    Kristine E. Lee, Stacy M. Meuer, Chelsea E. Myers, Emily L. Moore, Ronald Klein & Barbara E. K. Klein

  61. Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA

    David J. Hunter & Debra A. Schaumberg

  62. Department of Nutrition, Harvard School of Public Health, Boston, MA, USA

    David J. Hunter

  63. John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA

    Rebecca J. Sardell, Monique D. Courtenay, William K. Scott & Margaret A. Pericak-Vance

  64. Centre for Vision Research, Department of Ophthalmology, University of Sydney, Sydney, New South Wales, Australia

    Paul Mitchell, Gerald Liew, Ava G. Tan, Bamini Gopinath & Jie Jin Wang

  65. Westmead Millennium Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia

    Paul Mitchell, Gerald Liew, Ava G. Tan, Bamini Gopinath & Jie Jin Wang

  66. Department of Ophthalmology, Columbia University, New York, NY, USA

    Joanna E. Merriam, John C. Merriam, R. Theodore Smith & Rando Allikmets

  67. Moorfields Eye Hospital, London, UK

    Valentina Cipriani, Anthony T. Moore & John R. W. Yates

  68. Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, TN, USA

    Joshua D. Hoffman & J. Allie McGrath

  69. Department of Ophthalmology, University Hospital of Cologne, Cologne, Germany

    Tina Schick, Lebriz Ersoy, Albert Caramoy, Thomas Langmann & Sascha Fauser

  70. Department of Ophthalmology, Radboud University Medical Centre, Nijmegen, The Netherlands

    Yara T. E. Lechanteur, Nicole T. M. Saksens, Eiko K. deJohn, Carel B. Hoyng & Anneke I. den Hollander

  71. Centre for Eye Research Australia, University of Melbourne, East Melbourne, Victoria, Australia

    Robyn H. Guymer, Melinda S. Cain, Andrea J. Richardson, Paul N. Baird & Alex W. Hewitt

  72. Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia

    Robyn H. Guymer, Helena Hai Liang, David A. Mackey, Melinda S. Cain, Andrea J. Richardson, Paul N. Baird & Alex W. Hewitt

  73. South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, USA

    Matthew P. Johnson & John Blangero

  74. Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA

    Yingda Jiang & Daniel E. Weeks

  75. Medical Research Council (MRC) Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK

    Chloe M. Stanton & Caroline Hayward

  76. Department of Ophthalmology, Erasmus Medical Center, Rotterdam, The Netherlands

    Gabriëlle H. S. Buitendijk & Caroline C. W. Klaver

  77. Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands

    Gabriëlle H. S. Buitendijk, Cornelia M. van Duijn & Caroline C. W. Klaver

  78. Department of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Xiaowei Zhan

  79. Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Xiaowei Zhan

  80. Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute, US National Institutes of Health, Bethesda, MD, USA

    Alexis Boleda, Matthew Brooks, Linn Gieser, Rinki Ratnapriya & Anand Swaroop

  81. Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, MI, USA

    Kari E. Branham, John R. Heckenlively & Mohammad I. Othman

  82. Centre for Eye Research Australia, University of Melbourne, East Melbourne, Victoria, Australia

    Helena Hai Liang & David A. Mackey

  83. Department of Ophthalmology, Flinders Medical Centre, Flinders University, Adelaide, South Australia, Australia

    Emmanuelle Souzeau, Janette Hall, Stewart Lake & Jamie E. Craig

  84. Centre for Ophthalmology and Visual Science, Lions Eye Institute, University of Western Australia, Perth, Western Australia, Australia

    Ian L. McAllister, Timothy Isaacs, David A. Mackey, Ian J. Constable, Jane C. Khan & Alex W. Hewitt

  85. Sichuan Provincial Key Laboratory for Human Disease Gene Study, Hospital of the University of Electronic Science and Technology of China, Chengdu, China

    Zhenglin Yang

  86. Sichuan Provincial People’s Hospital, Chengdu, China

    Zhenglin Yang

  87. Sichuan Translational Medicine Hospital, Chinese Academy of Sciences, Chengdu, China

    Zhenglin Yang

  88. Molecular Medicine Research Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China

    Zhiquang Su & Kang Zhang

  89. EyeCentre Southwest, Stuttgart, Germany

    Claudia Nvon Strachwitz

  90. University Eye Clinic, Ludwig Maximilians University, Munich, Germany

    Armin Wolf & Guenther Rudolph

  91. Department of Ophthalmology, University Hospital Regensburg, Regensburg, Germany

    Caroline Brandl

  92. Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, USA

    Margaux A. Morrison, Deniste J. Morgan & Margaret DeAngelis

  93. Department of Medicine (Biomedical Genetics), Boston University Schools of Medicine and Public Health, Boston, MA, USA

    Matthew Schu & Lindsay A. Farrer

  94. Department of Ophthalmology, Boston University Schools of Medicine and Public Health, Boston, MA, USA

    Matthew Schu & Lindsay A. Farrer

  95. Department of Neurology, Boston University Schools of Medicine and Public Health, Boston, MA, USA

    Matthew Schu & Lindsay A. Farrer

  96. Department of Epidemiology, Boston University Schools of Medicine and Public Health, Boston, MA, USA

    Matthew Schu & Lindsay A. Farrer

  97. Department of Biostatistics, Boston University Schools of Medicine and Public Health, Boston, MA, USA

    Matthew Schu & Lindsay A. Farrer

  98. Department of Ophthalmology, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Republic of Korea

    Jeeyun Ahn

  99. Centre for Experimental Medicine, Queen’s University, Belfast, UK

    Giuliana Silvestri

  100. Department of Ophthalmology, School of Medicine, University of Thessaly, Larissa, Greece

    Evangelia E. Tsironi

  101. Department of Ophthalmology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea

    Kyu Hyung Park

  102. Department of Ophthalmology, Weill Cornell Medical College, New York, NY, USA

    Anton Orlin

  103. Department of Ophthalmology, Scheie Eye Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Alexander Brucker

  104. Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Mingyao Li

  105. Department of Ophthalmology, University of Alabama at Birmingham, Birmingham, AL, USA

    Christine A. Curcio

  106. INSERM, Paris, France

    Saddek Mohand-Saïd, José-Alain Sahel, Isabelle Audo, Frédéric Blond & Thierry Léveillard

  107. Department of Genetics, Institut de la Vision, Paris, France

    Saddek Mohand-Saïd, José-Alain Sahel, Isabelle Audo, Frédéric Blond & Thierry Léveillard

  108. Centre National de la Recherche Scientifique (CNRS), Paris, France

    Saddek Mohand-Saïd, José-Alain Sahel, Isabelle Audo, Frédéric Blond & Thierry Léveillard

  109. Centre Hospitalier National d’Ophtalmologie des Quinze-Vingts, Paris, France

    Saddek Mohand-Saïd, José-Alain Sahel & Mustapha Benchaboune

  110. University College London Institute of Ophthalmology, University College London, London, UK

    José-Alain Sahel, Anthony T. Moore & John R. W. Yates

  111. Fondation Ophtalmologique Adolphe de Rothschild, Paris, France

    José-Alain Sahel

  112. Académie des Sciences–Institut de France, Paris, France

    José-Alain Sahel

  113. Department of Molecular Genetics, Institute of Ophthalmology, London, UK

    Isabelle Audo

  114. University Hospital Southampton, Southampton, UK

    Christina A. Rennie

  115. Department of Ophthalmology, Hadassah Hebrew University Medical Center, Jerusalem, Israel

    Michelle Grunin, Shira Hagbi-Levi & Itay Chowers

  116. Center for Human Disease Modeling, Duke University, Durham, NC, USA

    Nicholas Katsanis

  117. Department of Cell Biology, Duke University, Durham, NC, USA

    Nicholas Katsanis

  118. Department of Pediatrics, Duke University, Durham, NC, USA

    Nicholas Katsanis

  119. Centre d’Etude du Polymorphisme Humain (CEPH), Fondation Jean Dausset, Paris, France

    Hélène Blanché & Jean-François Deleuze

  120. Commissariat à l’Energie Atomique et aux Energies Alternatives (CEA), Institut de Génomique, Centre National de Génotypage, Evry, France

    Jean-François Deleuze

  121. Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA

    Robert P. Igo Jr., Barbara Truitt & Jonathan L. Haines

  122. Department of Ophthalmology, Duke University Medical Center, Durham, NC, USA

    Michael A. Hauser & Eric A. Postel

  123. Department of Medicine, Duke University Medical Center, Durham, NC, USA

    Michael A. Hauser

  124. Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA

    Michael A. Hauser

  125. Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Naples, FL, USA

    Stephen G. Schwartz & Jaclyn L. Kovach

  126. Department of Ophthalmology, New York University School of Medicine, New York, NY, USA

    R. Theodore Smith

  127. Department of Ophthalmology, Royal Perth Hospital, Perth, Western Australia, Australia

    Jane C. Khan

  128. Department of Medical Genetics, Cambridge Institute for Medical Research, Cambridge, UK

    Jane C. Khan, Humma Shahidi & John R. W. Yates

  129. Department of Ophthalmology, Cambridge University Hospitals National Health Service (NHS) Foundation Trust, Cambridge, UK

    Humma Shahidi

  130. Department of Ophthalmology, University of California San Francisco Medical School, San Francisco, CA, USA

    Anthony T. Moore

  131. Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, TN, USA

    Milam A. Brantley Jr. & Anita Agarwal

  132. Casey Eye Institute, Oregon Health and Science University, Portland, OR, USA

    Tammy M. Martin & Michael L. Klein

  133. Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA

    Daniel E. Weeks

  134. School of Clinical Sciences, University of Edinburgh, Edinburgh, UK

    Bal Dhillon

  135. Center for Inherited Disease Research (CIDR) Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Kimberly F. Doheny & Jane Romm

  136. Department of Ophthalmology, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA

    Michael B. Gorin

  137. Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA

    Michael B. Gorin

  138. Department of Human Genetics, Radboud University Medical Centre, Nijmegen, The Netherlands

    Anneke I. den Hollander

  139. Department of Pathology and Cell Biology, Columbia University, New York, NY, USA

    Rando Allikmets

  140. Center for Translational Medicine, Moran Eye Center, University of Utah School of Medicine, Salt Lake City, UT, USA

    Debra A. Schaumberg

  141. Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Debra A. Schaumberg

  142. Division of Epidemiology and Clinical Applications, Clinical Trials Branch, National Eye Institute, US National Institutes of Health, Bethesda, MD, USA

    Emily Y. Chew

  143. Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA

    Jonathan L. Haines

Authors
  1. Bryan R. Gorman
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  2. Georgios Voloudakis
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  3. Robert P. Igo Jr.
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  4. Tyler Kinzy
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  5. Christopher W. Halladay
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  6. Tim B. Bigdeli
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  7. Biao Zeng
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  8. Sanan Venkatesh
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  9. Jessica N. Cooke Bailey
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  10. Dana C. Crawford
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  11. Kyriacos Markianos
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  12. Frederick Dong
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  13. Patrick A. Schreiner
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  14. Wen Zhang
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  15. Tamer Hadi
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  16. Matthew D. Anger
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  17. Amy Stockwell
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  18. Ronald B. Melles
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  19. Jie Yin
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  20. Hélène Choquet
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  21. Rebecca Kaye
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  22. Karina Patasova
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  23. Praveen J. Patel
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  24. Brian L. Yaspan
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  25. Eric Jorgenson
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  26. Pirro G. Hysi
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  27. Andrew J. Lotery
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  28. J. Michael Gaziano
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  29. Philip S. Tsao
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  30. Steven J. Fliesler
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  31. Jack M. Sullivan
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  32. Paul B. Greenberg
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  33. Wen-Chih Wu
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  34. Themistocles L. Assimes
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  35. Saiju Pyarajan
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  36. Panos Roussos
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  37. Neal S. Peachey
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  38. Sudha K. Iyengar
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Consortia

VA Million Veteran Program

  • J. Michael Gaziano
  • , Philip S. Tsao
  •  & Saiju Pyarajan

International AMD Genomics Consortium (IAMDGC)

  • Lars G. Fritsche
  • , Wilmar Igl
  • , Jessica N. Cooke Bailey
  • , Felix Grassman
  • , Sebanti Sengupta
  • , Jennifer L. Bragg-Gresham
  • , Kathryn P. Burdon
  • , Scott J. Hebring
  • , Cindy Wen
  • , Mathias Gorski
  • , Ivana K. Kim
  • , David Cho
  • , Donald Zack
  • , Eric Souied
  • , Hendrik P. N. Scholl
  • , Elisa Bala
  • , Kristine E. Lee
  • , David J. Hunter
  • , Rebecca J. Sardell
  • , Paul Mitchell
  • , Joanna E. Merriam
  • , Valentina Cipriani
  • , Joshua D. Hoffman
  • , Tina Schick
  • , Yara T. E. Lechanteur
  • , Robyn H. Guymer
  • , Matthew P. Johnson
  • , Yingda Jiang
  • , Chloe M. Stanton
  • , Gabriëlle H. S. Buitendijk
  • , Xiaowei Zhan
  • , Alan M. Kwong
  • , Alexis Boleda
  • , Matthew Brooks
  • , Linn Gieser
  • , Rinki Ratnapriya
  • , Kari E. Branham
  • , Johanna R. Foerster
  • , John R. Heckenlively
  • , Mohammad I. Othman
  • , Brendan J. Vote
  • , Helena Hai Liang
  • , Emmanuelle Souzeau
  • , Ian L. McAllister
  • , Timothy Isaacs
  • , Janette Hall
  • , Stewart Lake
  • , David A. Mackey
  • , Ian J. Constable
  • , Jamie E. Craig
  • , Terrie Kitchner
  • , Zhenglin Yang
  • , Zhiquang Su
  • , Hongrong Luo
  • , Daniel Chen
  • , Ouyang Ouyang
  • , Ken Flagg
  • , Daniel Lin
  • , Guanping Mao
  • , Henry Ferreyra
  • , Klaus Stark
  • , Claudia Nvon Strachwitz
  • , Armin Wolf
  • , Caroline Brandl
  • , Guenther Rudolph
  • , Matthias Olden
  • , Margaux A. Morrison
  • , Deniste J. Morgan
  • , Matthew Schu
  • , Jeeyun Ahn
  • , Giuliana Silvestri
  • , Evangelia E. Tsironi
  • , Kyu Hyung Park
  • , Lindsay A. Farrer
  • , Anton Orlin
  • , Alexander Brucker
  • , Mingyao Li
  • , Christine A. Curcio
  • , Saddek Mohand-Saïd
  • , José-Alain Sahel
  • , Isabelle Audo
  • , Mustapha Benchaboune
  • , Angela J. Cree
  • , Christina A. Rennie
  • , Srinivas V. Goverdhan
  • , Michelle Grunin
  • , Shira Hagbi-Levi
  • , Peter Campochiaro
  • , Nicholas Katsanis
  • , Frank G. Holz
  • , Frédéric Blond
  • , Hélène Blanché
  • , Jean-François Deleuze
  • , Robert P. Igo Jr.
  • , Barbara Truitt
  • , Neal S. Peachey
  • , Stacy M. Meuer
  • , Chelsea E. Myers
  • , Emily L. Moore
  • , Ronald Klein
  • , Michael A. Hauser
  • , Eric A. Postel
  • , Monique D. Courtenay
  • , Stephen G. Schwartz
  • , Jaclyn L. Kovach
  • , William K. Scott
  • , Gerald Liew
  • , Ava G. Tan
  • , Bamini Gopinath
  • , John C. Merriam
  • , R. Theodore Smith
  • , Jane C. Khan
  • , Humma Shahidi
  • , Anthony T. Moore
  • , J. Allie McGrath
  • , Reneé Laux
  • , Milam A. Brantley Jr.
  • , Anita Agarwal
  • , Lebriz Ersoy
  • , Albert Caramoy
  • , Thomas Langmann
  • , Nicole T. M. Saksens
  • , Eiko K. deJohn
  • , Carel B. Hoyng
  • , Melinda S. Cain
  • , Andrea J. Richardson
  • , Tammy M. Martin
  • , John Blangero
  • , Daniel E. Weeks
  • , Bal Dhillon
  • , Cornelia M. van Duijn
  • , Kimberly F. Doheny
  • , Jane Romm
  • , Caroline C. W. Klaver
  • , Caroline Hayward
  • , Michael B. Gorin
  • , Michael L. Klein
  • , Paul N. Baird
  • , Anneke I. den Hollander
  • , Sascha Fauser
  • , John R. W. Yates
  • , Rando Allikmets
  • , Jie Jin Wang
  • , Debra A. Schaumberg
  • , Barbara E. K. Klein
  • , Stephanie A. Hagstrom
  • , Itay Chowers
  • , Andrew J. Lotery
  • , Thierry Léveillard
  • , Kang Zhang
  • , Murray H. Brilliant
  • , Alex W. Hewitt
  • , Anand Swaroop
  • , Emily Y. Chew
  • , Margaret A. Pericak-Vance
  • , Margaret DeAngelis
  • , Dwight Stambolian
  • , Jonathan L. Haines
  • , Sudha K. Iyengar
  • , Bernhard H. F. Weber
  • , Gonçalo R. Abecasis
  •  & Iris M. Heid

Contributions

Conceptualization: B.R.G., G.V., R.P.I., P.R., N.S.P., S.K.I. Acquisition, analysis or interpretation of data: B.R.G., G.V., R.P.I., T.K., C.W.H., T.B.B., B.Z., S.V., J.N.C.B., D.C.C., K.M., F.D., P.A.S., W.Z., T.H., M.D.A., A.S., R.B.M., J.Y., H.C., R.K., K.P., P.J.P., B.L.Y., E.J., P.G.H., A.J.L., J.M.G., P.S.T., S.J.F., J.M.S., P.B.G., W.-C.W., T.L.A., S.P., P.R., N.S.P., S.K.I. Drafting of the manuscript: B.R.G., G.V., R.P.I., P.R., N.S.P., S.K.I. Critical revision of the manuscript for important intellectual content: B.R.G., G.V., T.K., T.B.B., J.N.C.B., D.C.C., P.G.H., A.J.L., S.J.F., H.C., B.L.Y., P.B.G., P.R., N.S.P., S.K.I.

Corresponding authors

Correspondence to Panos Roussos, Neal S. Peachey or Sudha K. Iyengar.

Ethics declarations

Competing interests

A.S. and B.L.Y. are employees of Genentech/Roche and hold stock and stock options in Roche. E.J. is an employee and a stockholder of Regeneron Genetics Center. The other authors declare no competing interests.

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Peer review information

Nature Genetics thanks Qi Yan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 PRS distribution of AMD cases and controls.

(a) Distribution of the normalized PRS in cases and controls in MVP EA. (b) Hazard ratio (95% CI) by PRS decile for time-to-AMD-diagnosis in unrelated EA in a Cox proportional hazards model, with the first decile as reference (n = 436,374; 32,357 events). (c) Distribution of the normalized PRS in AA. (d) Hazard ratio (95% CI) by PRS decile in unrelated AA (n = 112,182; 2,332 events). (e) Distribution of the normalized PRS in HA. (f) Hazard ratio (95% CI) by PRS decile in unrelated HA (n = 47,668; 1,631 events). The PRS was constructed using weights derived from the IAMDGC European-ancestry GWAS8.

Extended Data Fig. 2 Conditional analysis of PLTP and MMP9 signals in the 20q13 locus.

Regional association plots of: (a) the EA meta-analysis, demonstrating a primary signal near PLTP; (b) the IAMDGC study8, demonstrating a primary signal near MMP9; (c) the EA meta-analysis conditioned on the index SNP from the IAMDGC study (rs1888235); (d) the IAMDGC study conditioned on the index SNP from the EA meta-analysis (rs17447545). LD r² values were calculated in the MVP EA cohort. The dashed line denotes genome-wide significance (5 × 10−8).

Extended Data Fig. 3 Local ancestry decomposition of the ARMS2/HTRA1 locus.

(a) Regional association plots of AMD risk at ARMS2/HTRA1 in a standard GWAS in AA individuals (top), and the EUR (middle) and AFR (bottom) tracts from the Tractor analysis. (b) Regional association plots of AMD risk at ARMS2/HTRA1 in a standard GWAS in HA individuals (top), followed by the EUR ancestry tract, NAT ancestry tract, and AFR ancestry tract (bottom) from the Tractor analysis. The dashed line denotes genome-wide significance (5 × 10−8). LD r2 was calculated relative to the ARMS2 A69S allele using the best matched ancestry panel. Odds ratios for the ARMS2 A69S allele are given in Fig. 6.

Extended Data Fig. 4 Characterization of TWAS results across tissues.

(a) Pearson correlation of AMD association z-scores across the 38 tissues analyzed from GTEx, EyeGEx, STARNET, and PEC. (b) Hierarchical clustering of tissues based on AMD associations using Ward’s linkage method.

Extended Data Fig. 5 Gene set enrichment analysis (GSEA) of TWAS genes.

(a) GSEA of TWAS using the DLPFC transcripts (genes and isoforms) model, and (b) the multi-tissue meta-analysis. Enrichment odds ratios and 95% confidence intervals for the top ten enrichments by effect size are presented.

Extended Data Fig. 6 Semantic clustering of enriched gene ontology (GO) terms in the AMD multi-tissue transcriptome-wide association study (TWAS) identifies immune and lipid homeostasis functions.

First, we performed enrichment of AMD TWAS associated genes (FDR < 0.05 in ACAT meta-analysis of all tissues) for GO terms corresponding to ontologies of molecular functions (MFO), biological processes (BPO), and cellular components (CCO). Semantic clustering of the GO terms that had enrichment FDR < 0.1 (Fisher’s exact test) and could be clustered identified 7 clusters (BPO1-BPO5, CCO1, and MFO1). For each of these clusters we provide a word cloud based on the description of the GO terms where words that appear more frequently are larger; clusters are ordered from left to right and top to bottom from the most significant to the least significant based on their meta-analyzed p-value (ACAT). Major themes that are associated with AMD are immune functions and lipid homeostasis.

Extended Data Fig. 7 Enrichment of rare variant burden associations in TWAS meta-analysis genes.

TWAS meta-analysis genes were those with FDR < 0.05; only protein-coding genes were considered (12 rare variant gene sets were considered from the rare variant analysis). Mask 1: Loss of function mutations only; Mask 2: Loss of function + missense mutations; Mask 3: Loss of function + missense + splice mutations. Enrichment odds ratios and 95% confidence intervals are presented. In addition to CFH and CFI, genes prioritized by this analysis include CETP, ABCA7, ELP5 and B3GLCT.

Supplementary information

Supplementary Information

Supplementary Notes 1–5, Supplementary Discussion, Supplementary Figs. 1–7, Legends to Supplementary Tables 1–32 and References.

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Supplementary Tables 1–32

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Gorman, B.R., Voloudakis, G., Igo, R.P. et al. Genome-wide association analyses identify distinct genetic architectures for age-related macular degeneration across ancestries. Nat Genet 56, 2659–2671 (2024). https://doi.org/10.1038/s41588-024-01764-0

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