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Inflammation

, Volume 36, Issue 2, pp 279–284 | Cite as

Association of Rheumatoid Arthritis Risk Alleles with Response to Anti-TNF Biologics: Results from the CORRONA Registry and Meta-analysis

  • Dimitrios A. Pappas
  • Cheongeun Oh
  • Robert M. Plenge
  • Joel M. Kremer
  • Jeffrey D. Greenberg
Article

Abstract

In this study, we investigated whether genetic variants known to be related with susceptibility to rheumatoid arthritis (RA) are also associated with response to therapy with anti-tumor necrosis factor (anti-TNF) biologics; 233 patients enrolled in the Consortium of Rheumatology Researchers of North America (CORRONA) RA registry were studied. Findings were combined with results from an international collaborative study (N = 1,283) in a meta-analysis (N = 1,516). Multivariate models investigating the association between single nucleotide polymorphisms (SNPs) and change in RA disease activity were adjusted for age, gender, concomitant methotrexate, and baseline disease activity. In the CORRONA cohort, nominal associations with disease activity improvement were observed for the rs1980422 SNP of the CD28 gene in multivariate models (coefficient −0.377, p = 0.005) but were not significant after adjustment for multiple comparisons (q = 0.10). In the meta-analysis, the only SNP with nominal associations with change in DAS28 was the rs2812378 SNP of the CCL21 gene (coefficient 1.9195, p = 0.0068). This association was not significant after adjustment for multiple comparisons (q = 0.143). We conclude that the established RA risk alleles studied were not significantly associated with response to anti-TNF biologics in the CORRONA cohort or the meta-analysis.

KEY WORDS

rheumatoid arthritis pharmacogenetics TNF-alpha inhibitors 

References

  1. 1.
    Kievit, W., E.M. Adang, J. Fransen, H.H. Kuper, M.A. van de Laar, T.L. Jansen, C.M. De Gendt, D.J. De Rooij, H.L. Brus, P.C. Van Oijen, and P.C. Van Riel. 2008. The effectiveness and medication costs of three anti-tumour necrosis factor alpha agents in the treatment of rheumatoid arthritis from prospective clinical practice data. Annals of the Rheumatic Diseases 67: 1229–1234.PubMedCrossRefGoogle Scholar
  2. 2.
    Cui, J., S. Saevarsdottir, B. Thomson, L. Padyukov, A.H. van der Helm-van Mil, J. Nititham, L.B. Hughes, N. de Vries, S. Raychaudhuri, L. Alfredsson, J. Askling, S. Wedrén, B. Ding, C. Guiducci, G.J. Wolbink, J.B. Crusius, I.E. van der Horst-Bruinsma, M. Herenius, M.E. Weinblatt, N.A. Shadick, J. Worthington, F. Batliwalla, M. Kern, A.W. Morgan, A.G. Wilson, J.D. Isaacs, K. Hyrich, M.F. Seldin, L.W. Moreland, T.W. Behrens, C.F. Allaart, L.A. Criswell, T.W. Huizinga, P.P. Tak, S.L. Bridges Jr., R.E. Toes, A. Barton, L. Klareskog, P.K. Gregersen, E.W. Karlson, and R.M. Plenge. 2010. Rheumatoid arthritis risk allele PTPRC is also associated with response to anti-tumor necrosis factor alpha therapy. Arthritis and Rheumatism 62: 1849–1861.PubMedGoogle Scholar
  3. 3.
    Kremer, J.M. 2006. The CORRONA database. Autoimmun Reviews 5: 46–54.CrossRefGoogle Scholar
  4. 4.
    Stahl, E.A., S. Raychaudhuri, E.F. Remmers, G. Xie, S. Eyre, B.P. Thomson, Y. Li, F.A. Kurreeman, A. Zhernakova, A. Hinks, C. Guiducci, R. Chen, L. Alfredsson, C.I. Amos, K.G. Ardlie, BIRAC Consortium, A. Barton, J. Bowes, E. Brouwer, N.P. Burtt, J.J. Catanese, J. Coblyn, M.J. Coenen, K.H. Costenbader, L.A. Criswell, J.B. Crusius, J. Cui, P.I. de Bakker, P.L. De Jager, B. Ding, P. Emery, E. Flynn, P. Harrison, L.J. Hocking, T.W. Huizinga, D.L. Kastner, X. Ke, A.T. Lee, X. Liu, P. Martin, A.W. Morgan, L. Padyukov, M.D. Posthumus, T.R. Radstake, D.M. Reid, M. Seielstad, M.F. Seldin, N.A. Shadick, S. Steer, P.P. Tak, W. Thomson, A.H. van der Helm-van Mil, I.E. van der Horst-Bruinsma, C.E. van der Schoot, P.L. van Riel, M.E. Weinblatt, A.G. Wilson, G.J. Wolbink, B.P. Wordsworth, YEAR Consortium, C. Wijmenga, E.W. Karlson, R.E. Toes, N. de Vries, A.B. Begovich, J. Worthington, K.A. Siminovitch, P.K. Gregersen, L. Klareskog, and R.M. Plenge. 2010. Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci. Nature Genetics 42: 508–514.PubMedCrossRefGoogle Scholar
  5. 5.
    Raychaudhuri, S., E.F. Remmers, A.T. Lee, R. Hackett, C. Guiducci, N.P. Burtt, L. Gianniny, B.D. Korman, L. Padyukov, F.A. Kurreeman, M. Chang, J.J. Catanese, B. Ding, S. Wong, A.H. van der Helm-van Mil, B.M. Neale, J. Coblyn, J. Cui, P.P. Tak, G.J. Wolbink, J.B. Crusius, I.E. van der Horst-Bruinsma, L.A. Criswell, C.I. Amos, M.F. Seldin, D.L. Kastner, K.G. Ardlie, L. Alfredsson, K.H. Costenbader, D. Altshuler, T.W. Huizinga, N.A. Shadick, M.E. Weinblatt, N. de Vries, J. Worthington, M. Seielstad, R.E. Toes, E.W. Karlson, A.B. Begovich, L. Klareskog, P.K. Gregersen, M.J. Daly, and R.M. Plenge. 2008. Common variants at CD40 and other loci confer risk of rheumatoid arthritis. Nature Genetics 40: 1216–1223.PubMedCrossRefGoogle Scholar
  6. 6.
    Bentley, M.J., J.D. Greenberg, and G.W. Reed. 2010. A modified rheumatoid arthritis disease activity score without acute-phase reactants (mDAS28) for epidemiological research. Journal of Rheumatology 37: 1607–1614.PubMedCrossRefGoogle Scholar
  7. 7.
    Green GH and Diggle PJ. On the operational characteristics of the Benjamini and Hochberg false discovery rate procedure. Stat Appl Genet Mol Biol 2007;6:Article27.Google Scholar
  8. 8.
    Normand, S.L. 1999. Meta-analysis: Formulating, evaluating, combining, and reporting. Statistics in Medicine 18: 321–359.PubMedCrossRefGoogle Scholar
  9. 9.
    Klareskog, L., A.I. Catrina, and S. Paget. 2009. Rheumatoid arthritis. Lancet 373: 659–672.PubMedCrossRefGoogle Scholar
  10. 10.
    Plenge, R.M. 2009. Recent progress in rheumatoid arthritis genetics: One step towards improved patient care. Current Opinion in Rheumatology 21: 262–271.PubMedCrossRefGoogle Scholar
  11. 11.
    Potter, C., K.L. Hyrich, A. Tracey, M. Lunt, D. Plant, D.P. Symmons, W. Thomson, J. Worthington, P. Emery, A.W. Morgan, A.G. Wilson, J. Isaacs, A. Barton, and and the BRAGGSS. 2009. Association of rheumatoid factor and anti-cyclic citrullinated peptide positivity, but not carriage of shared epitope or PTPN22 susceptibility variants, with anti-tumour necrosis factor response in rheumatoid arthritis. Annals of the Rheumatic Diseases 68: 69–74.PubMedCrossRefGoogle Scholar
  12. 12.
    Tan, R.J., L.J. Gibbons, C. Potter, K.L. Hyrich, A.W. Morgan, A.G. Wilson, J.D. Isaacs, BRAGGSS, and A. Barton. 2010. Investigation of rheumatoid arthritis susceptibility genes identifies association of AFF3 and CD226 variants with response to anti-tumour necrosis factor treatment. Annals of the Rheumatic Diseases 69: 1029–1035.PubMedCrossRefGoogle Scholar
  13. 13.
    Purcell, S., S.S. Cherny, and P.C. Sham. 2003. Genetic Power Calculator: Design of linkage and association genetic mapping studies of complex traits. Bioinformatics 19(1): 149–150.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Dimitrios A. Pappas
    • 1
  • Cheongeun Oh
    • 2
  • Robert M. Plenge
    • 3
  • Joel M. Kremer
    • 4
  • Jeffrey D. Greenberg
    • 5
  1. 1.Division of Rheumatology, Department of Medicine, New York Presbyterian Hospital, College of Physicians and SurgeonsColumbia UniversityNew YorkUSA
  2. 2.Biostatistics, Department of Population HealthNew York University School of MedicineNew YorkUSA
  3. 3.Division of Rheumatology, Immunology and AllergyHarvard Medical SchoolBostonUSA
  4. 4.Department of MedicineAlbany Medical Center and The Center for RheumatologyAlbanyUSA
  5. 5.Department of RheumatologyNew York University Hospital for Joint DiseasesNew YorkUSA

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