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Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis
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(2016) Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis. Nature Communications, 7 . 12460. doi:10.1038/ncomms12460 ISSN 2041-1723.
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Official URL: http://dx.doi.org/10.1038/ncomms12460
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 (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.
| Item Type: | Journal Article | ||||||||
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| Subjects: | R Medicine > RC Internal medicine | ||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Biomedical Sciences > Translational & Experimental Medicine Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School |
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| Library of Congress Subject Headings (LCSH): | Rheumatoid arthritis -- Treatment, Rheumatoid arthritis -- Genetic aspects | ||||||||
| Journal or Publication Title: | Nature Communications | ||||||||
| Publisher: | Nature Publishing Group | ||||||||
| ISSN: | 2041-1723 | ||||||||
| Official Date: | 23 August 2016 | ||||||||
| Dates: |
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| Volume: | 7 | ||||||||
| Article Number: | 12460 | ||||||||
| DOI: | 10.1038/ncomms12460 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||
| Date of first compliant deposit: | 19 July 2017 | ||||||||
| Date of first compliant Open Access: | 19 July 2017 | ||||||||
| Funder: | National Institutes of Health (U.S.) (NIH), International Business Machines Corporation (IBM), Corrona (Firm), United States. Agency for Healthcare Research and Quality, Genentech, Inc., Eli Lilly and Company, Momenta Pharmaceuticals, Pfizer Inc. | ||||||||
| Grant number: | R01GM114434, R01GM105857, JRCAR053351, JDGAR054412 (NIH), R01HS018517 (United States. Agency for Healthcare Research and Quality) |
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