TY - JOUR
T1 - Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis
AU - The Rheumatoid Arthritis Challenge Consortium
AU - Sieberts, Solveig K.
AU - Zhu, Fan
AU - García-García, Javier
AU - Stahl, Eli
AU - Pratap, Abhishek
AU - Pandey, Gaurav
AU - Pappas, Dimitrios
AU - Aguilar, Daniel
AU - Anton, Bernat
AU - Bonet, Jaume
AU - Eksi, Ridvan
AU - Fornés, Oriol
AU - Guney, Emre
AU - Li, Hongdong
AU - Marín, Manuel Alejandro
AU - Panwar, Bharat
AU - Planas-Iglesias, Joan
AU - Poglayen, Daniel
AU - Cui, Jing
AU - Falcao, Andre O.
AU - Suver, Christine
AU - Hoff, Bruce
AU - Balagurusamy, Venkat S K
AU - Dillenberger, Donna
AU - Neto, Elias Chaibub
AU - Norman, Thea
AU - Aittokallio, Tero
AU - Ammad-Ud-Din, Muhammad
AU - Azencott, Chloe Agathe
AU - Bellón, Víctor
AU - Boeva, Valentina
AU - Bunte, Kerstin
AU - Chheda, Himanshu
AU - Cheng, Lu
AU - Corander, Jukka
AU - Dumontier, Michel
AU - Goldenberg, Anna
AU - Gopalacharyulu, Peddinti
AU - Hajiloo, Mohsen
AU - Hidru, Daniel
AU - Jaiswal, Alok
AU - Kaski, Samuel
AU - Khalfaoui, Beyrem
AU - Khan, Suleiman Ali
AU - Tang, Hao
AU - Marttinen, Pekka
AU - Wang, Tao
AU - Xiao, Guanghua
AU - Xie, Yang
AU - Zhan, Xiaowei
N1 - Publisher Copyright:
© The Author(s) 2016.
PY - 2016/8/23
Y1 - 2016/8/23
N2 - Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in 1/4one-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.
AB - Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in 1/4one-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.
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U2 - 10.1038/ncomms12460
DO - 10.1038/ncomms12460
M3 - Article
C2 - 27549343
AN - SCOPUS:84984705713
SN - 2041-1723
VL - 7
JO - Nature communications
JF - Nature communications
M1 - 12460
ER -