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Molecular Medicine

, Volume 21, Issue 1, pp 769–781 | Cite as

Genome-Wide Association Study of Late-Onset Myasthenia Gravis: Confirmation of TNFRSF11A and Identification of ZBTB10 and Three Distinct HLA Associations

  • Michael F. Seldin
  • Omar K. Alkhairy
  • Annette T. Lee
  • Janine A. Lamb
  • Jon Sussman
  • Ritva Pirskanen-Matell
  • Fredrik Piehl
  • Jan J. G. M. Verschuuren
  • Anna Kostera-Pruszczyk
  • Piotr Szczudlik
  • David McKee
  • Angelina H. Maniaol
  • Hanne F. Harbo
  • Benedicte A. Lie
  • Arthur Melms
  • Henri-Jean Garchon
  • Nicholas Willcox
  • Peter K. Gregersen
  • Lennart Hammarstrom
Research Article

Abstract

To investigate the genetics of late-onset myasthenia gravis (LOMG), we conducted a genome-wide association study imputation of >6 million single nucleotide polymorphisms (SNPs) in 532 LOMG cases (anti-acetylcholine receptor [AChR] antibody positive; onset age ≥50 years) and 2,128 controls matched for sex and population substructure. The data confirm reported TNFRSF11A associations (rs4574025, P = 3.9 × 10−7, odds ratio [OR] 1.42) and identify a novel candidate gene, ZBTB10, achieving genome-wide significance (rs6998967, P = 8.9 × 10−10, OR 0.53). Several other SNPs showed suggestive significance including rs2476601 (P = 6.5 × 10−6, OR 1.62) encoding the PTPN22 R620W variant noted in early-onset myasthenia gravis (EOMG) and other autoimmune diseases. In contrast, EOMG-associated SNPs in TNIP1 showed no association in LOMG, nor did other loci suggested for EOMG. Many SNPs within the major histocompatibility complex (MHC) region showed strong associations in LOMG, but with smaller effect sizes than in EOMG (highest OR ∼2 versus ∼6 in EOMG). Moreover, the strongest associations were in opposite directions from EOMG, including an OR of 0.54 for DQA1*05:01 in LOMG (P = 5.9 × 10−12) versus 2.82 in EOMG (P = 3.86 × 10−45). Association and conditioning studies for the MHC region showed three distinct and largely independent association peaks for LOMG corresponding to (a) MHC class II (highest attenuation when conditioning on DQA1), (b) HLA-A and (c) MHC class III SNPs. Conditioning studies of human leukocyte antigen (HLA) amino acid residues also suggest potential functional correlates. Together, these findings emphasize the value of subgrouping myasthenia gravis patients for clinical and basic investigations and imply distinct predisposing mechanisms in LOMG.

Notes

Acknowledgments

This work was supported by the National Institutes of Health (NIH/NIAID RO1-AI-68759 to P K Gregersen); by grants from the Palle Ferb Foundation and the Swedish Research Council (to OK Alkhairy and L Hammarstrom); by program grants from the UK Medical Research Council (to N Willcox); by the Prinses Beatrix Fonds (to JJGM Verschuuren); and by grants from the South-Eastern Norwegian Regional Health Authority and the Norwegian Association for Patients with Muscle Diseases (to AH Maniaol). We thank the Norwegian Bone Marrow Registry for control DNAs. We thank the CIDR for providing access to the Health And Retirement Study (HRS) through dbGaP Study Accession: phs000428.v1.p1. We also thank E Bakker in the Department of Human Genetics, Leiden University Medical Center, the Medical Research Council and Myasthenia Gravis Association in the UK, and the many patients and their physicians in all the centers who generously participated in this study.

Supplementary material

10020_2015_2101769_MOESM1_ESM.pdf (1.2 mb)
Supplementary material, approximately 1.21 MB.

References

  1. 1.
    McGrogan A, Sneddon S, de Vries CS. (2010) The incidence of myasthenia gravis: a systematic literature review. Neuroepidemiology. 34:171–83.CrossRefGoogle Scholar
  2. 2.
    Meriggioli MN, Sanders DB. (2009) Autoimmune myasthenia gravis: emerging clinical and biological heterogeneity. Lancet. 8:475–90.CrossRefGoogle Scholar
  3. 3.
    Carr AS, Cardwell CR, McCarron PO, McConville J. (2010) A systematic review of population based epidemiological studies in Myasthenia Gravis. BMC Neurol. 10:46.CrossRefGoogle Scholar
  4. 4.
    Lindstrom JM, Seybold ME, Lennon VA, Whittingham S, Duane DD. (1976) Antibody to acetylcholine receptor in myasthenia gravis. Prevalence, clinical correlates, and diagnostic value. Neurology. 26:1054–9.CrossRefGoogle Scholar
  5. 5.
    Chuang WY, et al. (2014) Late-onset myasthenia gravis-CTLA4(low) genotype association and low-for-age thymic output of naive T cells. J. Autoimmun. 52:122–9.CrossRefGoogle Scholar
  6. 6.
    Maniaol AH, et al. (2012) Late onset myasthenia gravis is associated with HLA DRB1*15:01 in the Norwegian population. PLoS One. 7:e36603.CrossRefGoogle Scholar
  7. 7.
    Compston DA, Vincent A, Newsom-Davis J, Batchelor JR. (1980) Clinical, pathological, HLA antigen and immunological evidence for disease heterogeneity in myasthenia gravis. Brain. 103:579–601.CrossRefGoogle Scholar
  8. 8.
    Evoli A, Batocchi AP, Minisci C, Di Schino C, Tonali P. (2000) Clinical characteristics and prognosis of myasthenia gravis in older people. J. Am. Geriatr. Soc. 48:1442–8.CrossRefGoogle Scholar
  9. 9.
    Akaishi T, et al. (2014) Insights into the classification of myasthenia gravis. PLoS One. 9:e106757.CrossRefGoogle Scholar
  10. 10.
    Janer M, et al. (1999) A susceptibility region for myasthenia gravis extending into the HLA-class I sector telomeric to HLA-C. Hum. Immunol. 60:909–17.CrossRefGoogle Scholar
  11. 11.
    Avidan N, Le Panse R, Berrih-Aknin S, Miller A. (2014) Genetic basis of myasthenia gravis: a comprehensive review. J. Autoimmun. 52:146–53.CrossRefGoogle Scholar
  12. 12.
    Giraud M, et al. (2001) Linkage of HLA to myasthenia gravis and genetic heterogeneity depending on anti-titin antibodies. Neurology. 57:1555–60.CrossRefGoogle Scholar
  13. 13.
    Grob D, Brunner N, Namba T, Pagala M. (2008) Lifetime course of myasthenia gravis. Muscle Nerve. 37:141–9.CrossRefGoogle Scholar
  14. 14.
    Vandiedonck C, et al. (2006) Association of the PTPN22*R620W polymorphism with autoimmune myasthenia gravis. Ann. Neurol. 59:404–7.CrossRefGoogle Scholar
  15. 15.
    Gregersen PK, et al. (2012) Risk for myasthenia gravis maps to a (151) Pro—>Ala change in TNIP1 and to human leukocyte antigen-B*08. Ann Neurol 72:927–935.CrossRefGoogle Scholar
  16. 16.
    Renton AE, et al. (2015) A genome-wide association study of myasthenia gravis. JAMA Neurol. 72:396–404.CrossRefGoogle Scholar
  17. 17.
    Purcell S, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81:559–75.CrossRefGoogle Scholar
  18. 18.
    Pritchard JK, Stephens M, Donnelly P. (2000) Inference of population structure using multilocus genotype data. Genetics. 155:945–59.PubMedPubMedCentralGoogle Scholar
  19. 19.
    Falush D, Stephens M, Pritchard JK. (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics. 164:1567–87.PubMedPubMedCentralGoogle Scholar
  20. 20.
    Kosoy R, et al. (2009) Ancestry informative marker sets for determining continental origin and admixture proportions in common populations in America. Hum. Mutat. 30:69–78.CrossRefGoogle Scholar
  21. 21.
    Price AL, et al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38:904–9.CrossRefGoogle Scholar
  22. 22.
    O’Connell J, et al. (2014) A general approach for haplotype phasing across the full spectrum of relatedness. PLoS Genet. 10:e1004234.CrossRefGoogle Scholar
  23. 23.
    Fuchsberger C, Abecasis GR, Hinds DA. (2014) minimac2: faster genotype imputation. Bioinformatics. 31:782–4.CrossRefGoogle Scholar
  24. 24.
    Howie BN, Donnelly P, Marchini J. (2009) A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5:e1000529.CrossRefGoogle Scholar
  25. 25.
    Jia X, et al. (2013) Imputing amino acid polymorphisms in human leukocyte antigens. PLoS One. 8:e64683.CrossRefGoogle Scholar
  26. 26.
    Browning BL, Browning SR. (2009) A unified approach to genotype imputation and haplotypephase inference for large data sets of trios and unrelated individuals. Am. J. Hum. Genet. 84:210–23.CrossRefGoogle Scholar
  27. 27.
    Boyle AP, et al. (2012) Annotation of functional variation in personal genomes using RegulomeDB. Genome Res.22:1790–7.CrossRefGoogle Scholar
  28. 28.
    Pruim RJ, et al. (2010) LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics. 26:2336–7.CrossRefGoogle Scholar
  29. 29.
    Degner JF, et al. (2012) DNase I sensitivity QTLs are a major determinant of human expression variation. Nature. 482:390–4.CrossRefGoogle Scholar
  30. 30.
    Coviello AD, et al. (2012) A genome-wide association meta-analysis of circulating sex hormone-binding globulin reveals multiple Loci implicated in sex steroid hormone regulation. PLoS Genet. 8:e1002805.CrossRefGoogle Scholar
  31. 31.
    Ferreira MA, et al. (2014) Genome-wide association analysis identifies 11 risk variants associated with the asthma with hay fever phenotype. J. Allergy Clin. Immunol. 133:1564–71.CrossRefGoogle Scholar
  32. 32.
    Eriksson N, et al. (2012) Novel associations for hypothyroidism include known autoimmune risk loci. PLoS One. 7:e34442.CrossRefGoogle Scholar
  33. 33.
    Paternoster L, et al. (2011) Meta-analysis of genomewide association studies identifies three new risk loci for atopic dermatitis. Nat. Genet. 44:187–92.CrossRefGoogle Scholar
  34. 34.
    Hinds DA, et al. (2013) A genome-wide association meta-analysis of self-reported allergy identifies shared and allergy-specific susceptibility loci. Nat. Genet. 45:907–11.CrossRefGoogle Scholar
  35. 35.
    Tillotson LG. (1999) RIN ZF, a novel zinc finger gene, encodes proteins that bind to the CACC element of the gastrin promoter. J. Biol. Chem. 274:8123–8.CrossRefGoogle Scholar
  36. 36.
    Li X, et al. (2010) MicroRNA-27a indirectly regulates estrogen receptor α expression and hormone responsiveness in MCF-7 breast cancer cells. Endocrinology. 151:2462–73.CrossRefGoogle Scholar
  37. 37.
    Tone M, Powell MJ, Tone Y, Thompson SA, Waldmann H. (2000) IL-10 gene expression is controlled by the transcription factors Sp1 and Sp3. J. Immunol. 165:286–91.CrossRefGoogle Scholar
  38. 38.
    Tone M, Tone Y, Babik JM, Lin CY, Waldmann H. (2002) The role of Sp1 and NF-kappa B in regulating CD40 gene expression. J. Biol. Chem. 277:8890–7.CrossRefGoogle Scholar
  39. 39.
    Bynote KK, et al. (2008) Estrogen receptor-alpha deficiency attenuates autoimmune disease in (NZB x NZW)F1 mice. Genes Immun. 9:137–52.CrossRefGoogle Scholar
  40. 40.
    Rubtsova K, Marrack P, Rubtsov AV. (2015) Sexual dimorphism in autoimmunity. J. Clin. Invest. 125:2187–93.CrossRefGoogle Scholar
  41. 41.
    Kaya GA, et al. (2014) The association of PTPN22 R620W polymorphism is stronger with late-onset AChR-myasthenia gravis in Turkey. PLoS One. 9:e104760.CrossRefGoogle Scholar
  42. 42.
    Bottini N, et al. (2004) A functional variant of lymphoid tyrosine phosphatase is associated with type I diabetes. Nat. Genet. 36:337–8.CrossRefGoogle Scholar
  43. 43.
    Begovich AB, et al. (2004) A missense single-nucleotide polymorphism in a gene encoding a protein tyrosine phosphatase (PTPN22) is associated with rheumatoid arthritis. Am. J. Hum. Genet. 75:330–7.CrossRefGoogle Scholar
  44. 44.
    Kyogoku C, et al. (2004) Genetic association of the R620W polymorphism of protein tyrosine phosphatase PTPN22 with human SLE. Am. J. Hum. Genet. 75:504–7.CrossRefGoogle Scholar
  45. 45.
    Bottini N, Peterson EJ. (2014) Tyrosine phosphatase PTPN22: multifunctional regulator of immune signaling, development, and disease. Ann. Rev. Immunol. 32:83–119.CrossRefGoogle Scholar
  46. 46.
    Hughes AE, et al. (2000) Mutations in TNFRSF11A, affecting the signal peptide of RANK, cause familial expansile osteolysis. Nat. Genet. 24:45–8.CrossRefGoogle Scholar
  47. 47.
    Guerrini MM, et al. (2008) Human osteoclast-poor osteopetrosis with hypogammaglobulinemia due to TNFRSF11A (RANK) mutations. Am. J. Hum. Genet. 83:64–76.CrossRefGoogle Scholar
  48. 48.
    Albagha OM, et al. (2010) Genome-wide association study identifies variants at CSF1, OPTN and TNFRSF11A as genetic risk factors for Paget’s disease of bone. Nat. Genet. 42:520–4.CrossRefGoogle Scholar
  49. 49.
    Estrada K, et al. (2012) Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat. Genet. 44:491–501.CrossRefGoogle Scholar
  50. 50.
    Roberts NA, et al. (2012) Rank signaling links the development of invariant gammadelta T cell progenitors and Aire(+) medullary epithelium. Immunity. 36:427–37.CrossRefGoogle Scholar
  51. 51.
    Anderson DM, et al. (1997) A homologue of the TNF receptor and its ligand enhance T-cell growth and dendritic-cell function. Nature. 390:175–9.CrossRefGoogle Scholar
  52. 52.
    Dougall WC, et al. (1999) RANK is essential for osteoclast and lymph node development. Genes Dev. 13:2412–24.CrossRefGoogle Scholar
  53. 53.
    Mewar D, et al. (2006) Haplotype-specific gene expression profiles in a telomeric major histocompatibility complex gene cluster and susceptibility to autoimmune diseases. Genes Immun. 7:625–31.CrossRefGoogle Scholar
  54. 54.
    Noble JA, et al. (2010) HLA class I and genetic susceptibility to type 1 diabetes: results from the type 1 Diabetes Genetics Consortium. Diabetes. 59:2972–9.CrossRefGoogle Scholar
  55. 55.
    Raychaudhuri S, et al. (2012) Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis. Nat. Genet. 44:291–6.CrossRefGoogle Scholar
  56. 56.
    Jawaheer D, et al. (2002) Dissecting the genetic complexity of the association between human leukocyte antigens and rheumatoid arthritis. Am. J. Hum. Genet. 71:585–94.CrossRefGoogle Scholar
  57. 57.
    Morris DL, et al. (2012) Unraveling multiple MHC gene associations with systemic lupus erythematosus: model choice indicates a role for HLA alleles and non-HLA genes in Europeans. Am. J. Hum. Genet. 91:778–93.CrossRefGoogle Scholar
  58. 58.
    Quinones-Lombrana A, et al. (2008) BAT1 promoter polymorphism is associated with rheumatoid arthritis susceptibility. J. Rheumatol. 35:741–4.PubMedGoogle Scholar
  59. 59.
    Gockel I, et al. (2014) Common variants in the HLA-DQ region confer susceptibility to idiopathic achalasia. Nat. Genet. 46:901–4.CrossRefGoogle Scholar
  60. 60.
    McMichael AJ, Gotch FM, Santos-Aguado J, Strominger JL. (1988) Effect of mutations and variations of HLA-A2 on recognition of a virus peptide epitope by cytotoxic T lymphocytes. Proc. Natl. Acad. Sci. U. S. A. 85:9194–8.CrossRefGoogle Scholar
  61. 61.
    Tackenberg B, et al. (2007) Clonal expansions of CD4+ B helper T cells in autoimmune myasthenia gravis. Eur. J. Immunol. 37:849–63.CrossRefGoogle Scholar
  62. 62.
    Selmi C, Brunetta E, Raimondo MG, Meroni PL. (2012) The X chromosome and the sex ratio of autoimmunity. Autoimmun. Rev. 11:A531–7.CrossRefGoogle Scholar
  63. 63.
    Padua L, et al. (2000) SFEMG in ocular myasthenia gravis diagnosis. Clin. Neurophysiol. 111:1203–7.CrossRefGoogle Scholar
  64. 64.
    Farrugia ME, Jacob S, Sarrigiannis PG, Kennett RP. (2009) Correlating extent of neuromuscular instability with acetylcholine receptor antibodies. Muscle Nerve. 39:489–93.CrossRefGoogle Scholar
  65. 65.
    Witoonpanich R, Dejthevaporn C, Sriphrapradang A, Pulkes T. (2011) Electrophysiological and immunological study in myasthenia gravis: diagnostic sensitivity and correlation. Clin. Neurophysiol. 122:1873–7.CrossRefGoogle Scholar

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Authors and Affiliations

  • Michael F. Seldin
    • 1
  • Omar K. Alkhairy
    • 2
  • Annette T. Lee
    • 3
  • Janine A. Lamb
    • 4
  • Jon Sussman
    • 5
  • Ritva Pirskanen-Matell
    • 6
  • Fredrik Piehl
    • 6
  • Jan J. G. M. Verschuuren
    • 7
  • Anna Kostera-Pruszczyk
    • 8
  • Piotr Szczudlik
    • 8
  • David McKee
    • 5
  • Angelina H. Maniaol
    • 9
  • Hanne F. Harbo
    • 10
  • Benedicte A. Lie
    • 11
  • Arthur Melms
    • 12
    • 13
  • Henri-Jean Garchon
    • 14
  • Nicholas Willcox
    • 15
  • Peter K. Gregersen
    • 3
  • Lennart Hammarstrom
    • 2
  1. 1.Department of Biochemistry and Molecular Medicine, and Department of MedicineUniversity of CaliforniaDavisUSA
  2. 2.Division of Clinical ImmunologyKarolinska Institutet at Karolinska University Hospital HuddingeStockholmSweden
  3. 3.The Robert S. Boas Center for Genomics and Human GeneticsFeinstein Institute for Medical Research, North Shore-LIJ Health SystemManhassetUSA
  4. 4.Centre for Integrated Genomic Medical Research, Manchester Academic Health Science CentreUniversity of ManchesterManchesterUK
  5. 5.Department of NeurologyGreater Manchester Neuroscience CentreManchesterUK
  6. 6.Department of NeurologyKarolinska University Hospital SolnaStockholmSweden
  7. 7.Department of NeurologyLeiden University Medical CenterLeidenthe Netherlands
  8. 8.Department of NeurologyMedical University of WarsawWarsawPoland
  9. 9.Department of NeurologyOslo University HospitalOsloNorway
  10. 10.Department of NeurologyOslo University Hospital and University of OsloOsloNorway
  11. 11.Department of Medical GeneticsUniversity of Oslo and Oslo University HospitalOsloNorway
  12. 12.Department of NeurologyTübingen University Medical CenterTübingenGermany
  13. 13.Neurologische KlinikUniversitätsklinikum ErlangenErlangenGermany
  14. 14.INSERM U1173University of VersaillesCampus Paris-SaclayFrance
  15. 15.Nuffield Department of Clinical Neurosciences, Weatherall Institute for Molecular MedicineUniversity of OxfordOxfordUK

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