Dissemin is shutting down on January 1st, 2025

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Nature Research, Nature Communications, 1(7), 2016

DOI: 10.1038/ncomms12460

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

Journal article published in 2016 by Jun Zhu, Niek de Vries, Jaume Bonet, Solveig K. Sieberts, Sieberts Sk, Jean-Philippe Vert, Venkat S. K. Balagurusamy, Javier García-García, Fan Zhu, Manuel Alejandro Marín, Joan Planas-Iglesias, Daniel Poglayen, Bharat Panwar, Eli Stahl, Jing Cui and other authors.
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

AbstractRheumatoid 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 (h2=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.