Research output

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

Research output: Contribution to journalArticleAcademicpeer-review

Associated researcher

  • Sieberts, S. K.
  • Zhu, F.
  • García-García, J.
  • Stahl, E. A.
  • Pratap, A.
  • Pandey, G.
  • Pappas, D.
  • Aguilar, D.
  • Anton, B.
  • Bonet, J.
  • Eksi, R.
  • Fornés, O.
  • Guney, E.
  • Li, H.
  • Marín, M. A.
  • Panwar, B.
  • Planas-Iglesias, J.
  • Poglayen, D.
  • Cui, J.
  • Falcao, A. O.
  • Suver, C.
  • Hoff, B.
  • Balagurusamy, V. S. K.
  • Dillenberger, D.
  • Neto, E. C.
  • Norman, T.
  • Aittokallio, T.
  • Ammad-Ud-Din, M.
  • Azencott, C.
  • Bellón, V.
  • Boeva, V.
  • Bunte, K.
  • Chheda, H.
  • Cheng, L.
  • Corander, J.
  • Goldenberg, A.
  • Gopalacharyulu, P.
  • Hajiloo, M.
  • Hidru, D.
  • Jaiswal, A.
  • Kaski, S.
  • Khalfaoui, B.
  • Khan, S. A.
  • Kramer, E. R.
  • Marttinen, P.
  • Mezlini, A. M.
  • Molparia, B.
  • Pirinen, M.
  • Saarela, J.
  • Samwald, M.
  • Stoven, V.
  • Tang, H.
  • Tang, J.
  • Torkamani, A.
  • Vert, J.
  • Wang, B.
  • Wang, T.
  • Wennerberg, K.
  • Wineinger, N. E.
  • Xiao, G.
  • Xie, Y.
  • Yeung, R.
  • Zhan, X.
  • Zhao, C.
  • Greenberg, J.
  • Kremer, J.
  • Michaud, K.
  • Barton, A.
  • Coenen, M.
  • Mariette, X.
  • Miceli, C.
  • Shadick, N.
  • Weinblatt, M.
  • de Vries, N.
  • Tak, P. P.
  • Gerlag, D.
  • Huizinga, T. W. J.
  • Kurreeman, F.
  • Allaart, C. F.
  • Louis Bridges, S.
  • Criswell, L.
  • Moreland, L.
  • Klareskog, L.
  • Saevarsdottir, S.
  • Padyukov, L.
  • Gregersen, P. K.
  • Friend, S.
  • Plenge, R.
  • Stolovitzky, G.
  • Oliva, B.
  • Guan, Y.
  • Mangravite, L. M.
  • Bridges, S. L.
  • Criswell, L.
  • Moreland, L.
  • Klareskog, L.
  • Saevarsdottir, S.
  • Padyukov, L.
  • Gregersen, P. K.
  • Friend, S.
  • Plenge, R.
  • Stolovitzky, G.
  • Oliva, B.
  • Guan, Y.
  • Mangravite, L. M.

Abstract

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

    Research areas

  • Journal Article, METAANALYSIS, DISEASE, MISSING HERITABILITY, RISK, NETWORK INFERENCE, INFLIXIMAB, CLINICAL-RESPONSE, COMPLEX TRAITS, STACKED GENERALIZATION, GENOME-WIDE ASSOCIATION
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Details

Original languageEnglish
Article number12460
Pages (from-to)12460
Number of pages9
JournalNature Communications
Volume7
DOIs
Publication statusPublished - 23 Aug 2016
Externally publishedYes