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

Sieberts, Solveig K.; García-García, Javier, 1982-; Aguilar, Daniel; Anton, Bernat; Bonet Martínez, Jaume, 1982-; Fornés Crespo, Oriol, 1983-; Marín López, Manuel Alejandro, 1987-; Planas Iglesias, Joan, 1980-; Poglayen, Daniel, 1984-; Oliva Miguel, Baldomero; Mangravite, Lara M.

doi:http://dx.doi.org/10.1038/ncomms12460

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

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Sieberts SK, Zhu F, García-García J, Stahl E, Pratap A, Pandey G, et al. Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis. Nat Commun. 2016 Aug 23;7:12460. doi: 10.1038/ncomms12460

Abstract

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widelyused to reduce disease progression, treatment fails inBone-third of patients. No biomarkercurrently 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 RApatients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictionsdeveloped by 73 research groups using the most comprehensive available data and covering awide range of state-of-the-art modelling methodologies. Despite a significant geneticheritability estimate of treatment non-response trait (h2¼0.18,Pvalue¼0.02), nosignificant genetic contribution to prediction accuracy is observed. Results formally confirmthe expectations of the rheumatology community that SNP information does not significantlyimprove predictive performance relative to standard clinical traits, thereby justifying arefocusing of future efforts on collection of other data