@article{dream2016,
author = {Solveig Sieberts and Fan Zhu and Javier Garc\'ia-Garc\'ia and Eli Stahl and Abhishek Pratap and Gaurav Pandey and Dimitrios Pappas and Daniel Aguilar and Bernat Anton and Jaume Bonet and Ridvan Eksi and Oriol Fornés and Emre Guney and Hongdong Li and Manuel Marín and Bharat Panwar and Joan Planas-Iglesias and Daniel Poglayen and Jing Cui and Andre Falcao and Christine Suver and Bruce Hoff and Venkat Balagurusamy and Donna Dillenberger and Elias Chaibub Neto and Thea Norman and Tero Aittokallio and Muhammad Ammad-ud-din and Chloe-Agathe Azencott and Víctor Bellón and Valentina Boeva and Kerstin Bunte and Himanshu Chheda and Lu Cheng and Jukka Corander and Michel Dumontier and Anna Goldenberg and Peddinti Gopalacharyulu and Mohsen Hajiloo and Daniel Hidru and Alok Jaiswal and Samuel Kaski and Beyrem Khalfaoui and Suleiman Khan and Eric Kramer and Pekka Marttinen and Aziz Mezlini and Bhuvan Molparia and Matti Pirinen and Janna Saarela and Matthias Samwald and Véronique Stoven and Hao Tang and Jing Tang and Ali Torkamani and Jean-Philippe Vert and Bo Wang and Tao Wang and Krister Wennerberg and Nathan Wineinger and Guanghua Xiao and Yang Xie and Rae Yeung and Xiaowei Zhan and Cheng Zhao and Jeff Greenberg and Joel Kremer and Kaleb Michaud and Anne Barton and Marieke Coenen and Xavier Mariette and Corinne Miceli and Nancy Shadick and Michael Weinblatt and Niek de Vries and Paul Tak and Danielle Gerlag and Tom W. J. Huizinga and Fina Kurreeman and Cornelia Allaart and Stanley Bridges and Lindsey Criswell and Larry Moreland and Lars Klareskog and Saedis Saevarsdottir and Leonid Padyukov and Peter Gregersen and Stephen Friend and Robert Plenge and Gustavo Stolovitzky and Baldomero Oliva and Yuanfang Guan and Lara Mangravite},
title = {Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis},
journal = {Nature Communications},
pages = {},
volume = {7},
number = {12460},
number2 = {NCOMMS-15-15784D},
year = {2016},
doi = {10.1038/ncomms12460},
url = {http://dx.doi.org/10.1038/ncomms12460},
pmcid = {PMC4996969},
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},
}