Letter | Published:

Rare variants in CFI, C3 and C9 are associated with high risk of advanced age-related macular degeneration

Nature Genetics volume 45, pages 13661370 (2013) | Download Citation

Abstract

To define the role of rare variants in advanced age-related macular degeneration (AMD) risk, we sequenced the exons of 681 genes within all reported AMD loci and related pathways in 2,493 cases and controls. We first tested each gene for increased or decreased burden of rare variants in cases compared to controls. We found that 7.8% of AMD cases compared to 2.3% of controls are carriers of rare missense CFI variants (odds ratio (OR) = 3.6; P = 2 × 10−8). There was a predominance of dysfunctional variants in cases compared to controls. We then tested individual variants for association with disease. We observed significant association with rare missense alleles in genes other than CFI. Genotyping in 5,115 independent samples confirmed associations with AMD of an allele in C3 encoding p.Lys155Gln (replication P = 3.5 × 10−5, OR = 2.8; joint P = 5.2 × 10−9, OR = 3.8) and an allele in C9 encoding p.Pro167Ser (replication P = 2.4 × 10−5, OR = 2.2; joint P = 6.5 × 10−7, OR = 2.2). Finally, we show that the allele of C3 encoding Gln155 results in resistance to proteolytic inactivation by CFH and CFI. These results implicate loss of C3 protein regulation and excessive alternative complement activation in AMD pathogenesis, thus informing both the direction of effect and mechanistic underpinnings of this disorder.

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References

  1. 1.

    , , , & Age-related macular degeneration. Lancet 379, 1728–1738 (2012).

  2. 2.

    , , , & The US twin study of age-related macular degeneration: relative roles of genetic and environmental influences. Arch. Ophthalmol. 123, 321–327 (2005).

  3. 3.

    et al. Prevalence of age-related macular degeneration in the United States. Arch. Ophthalmol. 122, 564–572 (2004).

  4. 4.

    et al. Seven new loci associated with age-related macular degeneration. Nat. Genet. 45, 433–439 (2013).

  5. 5.

    et al. A rare penetrant mutation in CFH confers high risk of age-related macular degeneration. Nat. Genet. 43, 1232–1236 (2011).

  6. 6.

    et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

  7. 7.

    et al. zCall: a rare variant caller for array-based genotyping: genetics and population analysis. Bioinformatics 28, 2543–2545 (2012).

  8. 8.

    et al. Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion. Nat. Genet. 45, 197–201 (2013).

  9. 9.

    & Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. 83, 311–321 (2008).

  10. 10.

    et al. Testing for an unusual distribution of rare variants. PLoS Genet. 7, e1001322 (2011).

  11. 11.

    et al. Variation near complement factor I is associated with risk of advanced AMD. Eur. J. Hum. Genet. 17, 100–104 (2009).

  12. 12.

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

  13. 13.

    et al. A systematic survey of loss-of-function variants in human protein-coding genes. Science 335, 823–828 (2012).

  14. 14.

    , , & Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed. Hum. Mutat. 32, 661–668 (2011).

  15. 15.

    et al. Mutations in components of complement influence the outcome of Factor I–associated atypical hemolytic uremic syndrome. Kidney Int. 77, 339–349 (2010).

  16. 16.

    et al. Genetics of HUS: the impact of MCP, CFH, and IF mutations on clinical presentation, response to treatment, and outcome. Blood 108, 1267–1279 (2006).

  17. 17.

    et al. A mutation in factor I that is associated with atypical hemolytic uremic syndrome does not affect the function of factor I in complement regulation. Mol. Immunol. 44, 1835–1844 (2007).

  18. 18.

    et al. A missense mutation in factor I (IF) predisposes to atypical haemolytic uraemic syndrome. Pediatr. Nephrol. 22, 371–375 (2007).

  19. 19.

    et al. Differential impact of complement mutations on clinical characteristics in atypical hemolytic uremic syndrome. J. Am. Soc. Nephrol. 18, 2392–2400 (2007).

  20. 20.

    et al. Complement factor I: a susceptibility gene for atypical haemolytic uraemic syndrome. J. Med. Genet. 41, e84 (2004).

  21. 21.

    et al. Characterization of mutations in complement factor I (CFI) associated with hemolytic uremic syndrome. Mol. Immunol. 45, 95–105 (2008).

  22. 22.

    et al. CFH haplotypes without the Y402H coding variant show strong association with susceptibility to age-related macular degeneration. Nat. Genet. 38, 1049–1054 (2006).

  23. 23.

    et al. Associations of CFHR1-CFHR3 deletion and a CFH SNP to age-related macular degeneration are not independent. Nat. Genet. 42, 553–555; author reply 555–556 (2010).

  24. 24.

    et al. Common variation in three genes, including a noncoding variant in CFH, strongly influences risk of age-related macular degeneration. Nat. Genet. 38, 1055–1059 (2006).

  25. 25.

    , , , & Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nat. Genet. 33, 177–182 (2003).

  26. 26.

    et al. Variation in complement factor 3 is associated with risk of age-related macular degeneration. Nat. Genet. 39, 1200–1201 (2007).

  27. 27.

    et al. Complement C3 variant and the risk of age-related macular degeneration. N. Engl. J. Med. 357, 553–561 (2007).

  28. 28.

    et al. C9-R95X polymorphism in patients with neovascular age-related macular degeneration. Invest. Ophthalmol. Vis. Sci. 53, 508–512 (2012).

  29. 29.

    et al. Structure of complement fragment C3b–factor H and implications for host protection by complement regulators. Nat. Immunol. 10, 728–733 (2009).

  30. 30.

    et al. Mutations in complement C3 predispose to development of atypical hemolytic uremic syndrome. Blood 112, 4948–4952 (2008).

  31. 31.

    et al. Structural and functional characterization of factor H mutations associated with atypical hemolytic uremic syndrome. Am. J. Hum. Genet. 71, 1285–1295 (2002).

  32. 32.

    et al. Factor H and atypical hemolytic uremic syndrome: mutations in the C-terminus cause structural changes and defective recognition functions. J. Am. Soc. Nephrol. 17, 170–177 (2006).

  33. 33.

    et al. Mutations in factor H reduce binding affinity to C3b and heparin and surface attachment to endothelial cells in hemolytic uremic syndrome. J. Clin. Invest. 111, 1181–1190 (2003).

  34. 34.

    , , , & Functional basis of protection against age-related macular degeneration conferred by a common polymorphism in complement factor B. Proc. Natl. Acad. Sci. USA 106, 4366–4371 (2009).

  35. 35.

    et al. Common polymorphisms in C3, factor B, and factor H collaborate to determine systemic complement activity and disease risk. Proc. Natl. Acad. Sci. USA 108, 8761–8766 (2011).

  36. 36.

    et al. Tissue-specific host recognition by complement factor H is mediated by differential activities of its glycosaminoglycan-binding regions. J. Immunol. 190, 2049–2057 (2013).

  37. 37.

    et al. Common variants near FRK/COL10A1 and VEGFA are associated with advanced age-related macular degeneration. Hum. Mol. Genet. 20, 3699–3709 (2011).

  38. 38.

    et al. Genome-wide association study of advanced age-related macular degeneration identifies a role of the hepatic lipase gene (LIPC). Proc. Natl. Acad. Sci. USA 107, 7395–7400 (2010).

  39. 39.

    et al. Genetic variants near TIMP3 and high-density lipoprotein–associated loci influence susceptibility to age-related macular degeneration. Proc. Natl. Acad. Sci. USA 107, 7401–7406 (2010).

  40. 40.

    , & Evaluation of the clinical age-related maculopathy staging system. Ophthalmology 113, 260–266 (2006).

  41. 41.

    , , & Progression of age-related macular degeneration: association with body mass index, waist circumference, and waist-hip ratio. Arch. Ophthalmol. 121, 785–792 (2003).

  42. 42.

    , , , & A genomewide scan for age-related macular degeneration provides evidence for linkage to several chromosomal regions. Am. J. Hum. Genet. 73, 780–790 (2003).

  43. 43.

    et al. Dietary fat and risk for advanced age-related macular degeneration. Arch. Ophthalmol. 119, 1191–1199 (2001).

  44. 44.

    et al. Genetic and functional dissection of HTRA1 and LOC387715 in age-related macular degeneration. PLoS Genet. 6, e1000836 (2010).

  45. 45.

    et al. Toll-like receptor 3 and geographic atrophy in age-related macular degeneration. N. Engl. J. Med. 359, 1456–1463 (2008).

  46. 46.

    1000 Genomes Project Consortium.. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

  47. 47.

    et al. HLA-B*5701 genotype is a major determinant of drug-induced liver injury due to flucloxacillin. Nat. Genet. 41, 816–819 (2009).

  48. 48.

    et al. Whole-genome association study of bipolar disorder. Mol. Psychiatry 13, 558–569 (2008).

  49. 49.

    et al. Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease. Nat. Genet. 43, 1066–1073 (2011).

  50. 50.

    & Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  51. 51.

    et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

  52. 52.

    et al. Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science 337, 64–69 (2012).

  53. 53.

    et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 80–92 (2012).

  54. 54.

    et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

  55. 55.

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

  56. 56.

    Multilocus association mapping using variable-length Markov chains. Am. J. Hum. Genet. 78, 903–913 (2006).

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Acknowledgements

We thank the participants and numerous ophthalmologists throughout the country who took part in this study as well as the Age-Related Eye Disease Study Research Group. This research was supported in part by grants R01-EY11309 (J.M.S.), K08AR055688 (S.R.), U01HG0070033 (S.R.), F30HL103072 (M.T.) and R01-AI041592 (J.P.A. and E.C.M.) from the US National Institutes of Health (NIH); The Doris Duke Foundation (S.R.); the Edward N. & Della L. Thome Memorial Foundation (J.P.A.); the Massachusetts Lions Eye Research Fund, Inc. (J.M.S.); the Foundation Fighting Blindness (J.M.S.); the Macular Vision Research Foundation (J.M.S.); a Research to Prevent Blindness Challenge Grant to the New England Eye Center, Department of Ophthalmology, Tufts University School of Medicine; the American Macular Degeneration Foundation (J.M.S.); The Arnold and Mabel Beckman Initiative for Macular Research (J.M.S. and S.R.); and the Macular Degeneration Research Fund of the Ophthalmic Epidemiology and Genetics Service, New England Eye Center, Tufts Medical Center, Tufts University School of Medicine. N.K. is a Distinguished Brumley Professor. D.K. is a Wellcome Intermediate Clinical Fellow. We thank the French national Programme Hospitalier de Recherche Clinique (PHRC; E.S.).

Author information

Affiliations

  1. Ophthalmic Epidemiology and Genetics Service, New England Eye Center, Tufts Medical Center, Boston, Massachusetts, USA.

    • Johanna M Seddon
    • , Yi Yu
    •  & Robyn Reynolds
  2. Department of Ophthalmology, Tufts University School of Medicine, Boston, Massachusetts, USA.

    • Johanna M Seddon
  3. Sackler School of Graduate Biomedical Sciences, Tufts University, Boston, Massachusetts, USA.

    • Johanna M Seddon
  4. Division of Rheumatology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, USA.

    • Elizabeth C Miller
    • , Michael Triebwasser
    •  & John P Atkinson
  5. Center for Human Disease Modeling, Duke University, Durham, North Carolina, USA.

    • Perciliz L Tan
    •  & Nicholas Katsanis
  6. Department of Cell Biology, Duke University, Durham, North Carolina, USA.

    • Perciliz L Tan
    •  & Nicholas Katsanis
  7. Department of Pediatrics, Duke University, Durham, North Carolina, USA.

    • Perciliz L Tan
    •  & Nicholas Katsanis
  8. Partners HealthCare Center for Personalized Genetic Medicine, Boston, Massachusetts, USA.

    • Sivakumar Gowrisankar
    •  & Soumya Raychaudhuri
  9. Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA.

    • Jacqueline I Goldstein
    • , Mark J Daly
    •  & Soumya Raychaudhuri
  10. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA.

    • Jacqueline I Goldstein
    •  & Mark J Daly
  11. Institute of Genetic Medicine, Newcastle University, International Centre for Life, Newcastle-upon-Tyne, UK.

    • Holly E Anderson
    •  & David Kavanagh
  12. Department of Ophthalmology, Hôpital Intercommunal de Créteil, Hôpital Henri Mondor, Université Paris Est Créteil, Créteil, France.

    • Jennyfer Zerbib
    •  & Eric Souied
  13. Division of Genetics, Brigham and Women's Hospital, Boston, Massachusetts, USA.

    • Soumya Raychaudhuri
  14. Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, Massachusetts, USA.

    • Soumya Raychaudhuri
  15. Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK.

    • Soumya Raychaudhuri

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Contributions

J.M.S. and S.R. led the study. J.M.S., Y.Y., R.R., P.L.T., J.Z., E.S. and N.K. coordinated sequencing and genotyping. E.C.M., M.T., H.E.A., D.K. and J.P.A. conducted and interpreted complement functional studies. Y.Y., J.I.G., S.G., M.J.D. and S.R. conducted all statistical analyses.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Johanna M Seddon or Soumya Raychaudhuri.

Supplementary information

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    Supplementary Text and Figures

    Supplementary Figures 1–14 and Supplementary Tables 1 and 3–7

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    Supplementary Table 2

    (a) Results of burden tests for 681 genes targeted for sequencing. (b) Results of testing 1,824 single variants for association.

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DOI

https://doi.org/10.1038/ng.2741

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