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

S Sieberts Fan Zhu 1 Javier García-García 2 Eli Stahl Abhishek Pratap Gaurav Pandey 3 Dimitrios Pappas Daniel Aguilar 4 Bernat Anton Bo Bonet 5 Ridvan Eksi Oriol Fornés 6 Emre Guney Hongdong Li 7 Manuel Marín Bharat Panwar Joan Planas-Iglesias Daniel Poglayen Jing Cui 7 Andre O. Falcao 8 Christine Suver Bruce Hoff S Balagurusamy Donna Dillenberger Elias Chaibub Neto Thea Norman Tero Aittokallio Muhammad Ammad-Ud-Din Chloe-Agathe Azencott 9, 10 Víctor Bellón 9, 10 Valentina Boeva 9, 10 Kerstin Bunte Himanshu Chheda Cheng Cheng 11 Jukka Corander Michel Dumontier 12 Anna Goldenberg 13 Peddinti Gopalacharyulu 14 Mohsen Hajiloo Daniel Hidru Alok Jaiswal 15 S Kaski Beyrem Khalfaoui Ali Khan 15 Eric R. Kramer 16 Pekka Marttinen Aziz M. Mezlini Bhuvan Molparia Matti Pirinen Janna Saarela 17 Matthias Samwald Véronique Stoven 9, 10 Hao Tang 18 Jing Tang Ali Torkamani Jean-Philippe Vert 9, 10 Bo Wang 19, 20 Tao Wang 21 Krister Wennerberg Nathan E. Wineinger Guanghua Guan 22 Yang Xie 23, 24 Rae Yeung 25 Xiaowei Zhan Cheng Zhao 26 Manuel Calaza Haitham Elmarakeby S. Heath 27 Quan Long 28 Jonathan D. Moore 29 Stephen Opiyo S. Savage Jun Zhu 30 Jeff Greenberg Joel Kremer 31 Kaleb Michaud Anne Barton Marieke Coenen 32 Xavier Mariette 33 Corinne Miceli 34 Nancy Shadick Michael Weinblatt 35 Niek de Vries 36 Paul P. Tak 37 Danielle Gerlag Tom W J Huizinga 38 Fina Kurreeman 39 Cornelia F. Allaart S Bridges S Louis Bridges Lindsey Criswell 40 Larry Moreland Lars Klareskog 41 Saedis Saevarsdottir Leonid Padyukov 42 Peter K Gregersen Stephen Friend Robert Plenge 43 Gustavo Stolovitzky Baldo Oliva 44 Yuanfang Guan 45 Lara M Mangravite
4 DYNBIO LEGOS
LEGOS - Laboratoire d'études en Géophysique et océanographie spatiales
8 CA3 - Computational Intelligence Research Group
CTS - Centre of Technology and Systems, FCT NOVA - Faculdade de Ciências e Tecnologia
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.
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https://hal.archives-ouvertes.fr/hal-01420921
Contributor : Jean-Philippe Vert <>
Submitted on : Thursday, January 21, 2021 - 9:33:38 AM
Last modification on : Thursday, February 25, 2021 - 9:46:05 AM

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S Sieberts, Fan Zhu, Javier García-García, Eli Stahl, Abhishek Pratap, et al.. Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis.. Nature Communications, Nature Publishing Group, 2016, 7, pp.12460. ⟨10.1038/ncomms12460⟩. ⟨hal-01420921⟩

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