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


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.

SUBMITTER: Sieberts SK 

PROVIDER: S-EPMC4996969 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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

Sieberts Solveig K SK   Zhu Fan F   García-García Javier J   Stahl Eli E   Pratap Abhishek A   Pandey Gaurav G   Pappas Dimitrios D   Aguilar Daniel D   Anton Bernat B   Bonet Jaume J   Eksi Ridvan R   Fornés Oriol O   Guney Emre E   Li Hongdong H   Marín Manuel Alejandro MA   Panwar Bharat B   Planas-Iglesias Joan J   Poglayen Daniel D   Cui Jing J   Falcao Andre O AO   Suver Christine C   Hoff Bruce B   Balagurusamy Venkat S K VSK   Dillenberger Donna D   Neto Elias Chaibub EC   Norman Thea T   Aittokallio Tero T   Ammad-Ud-Din Muhammad M   Azencott Chloe-Agathe CA   Bellón Víctor V   Boeva Valentina V   Bunte Kerstin K   Chheda Himanshu H   Cheng Lu L   Corander Jukka J   Dumontier Michel M   Goldenberg Anna A   Gopalacharyulu Peddinti P   Hajiloo Mohsen M   Hidru Daniel D   Jaiswal Alok A   Kaski Samuel S   Khalfaoui Beyrem B   Khan Suleiman Ali SA   Kramer Eric R ER   Marttinen Pekka P   Mezlini Aziz M AM   Molparia Bhuvan B   Pirinen Matti M   Saarela Janna J   Samwald Matthias M   Stoven Véronique V   Tang Hao H   Tang Jing J   Torkamani Ali A   Vert Jean-Phillipe JP   Wang Bo B   Wang Tao T   Wennerberg Krister K   Wineinger Nathan E NE   Xiao Guanghua G   Xie Yang Y   Yeung Rae R   Zhan Xiaowei X   Zhao Cheng C   Greenberg Jeff J   Kremer Joel J   Michaud Kaleb K   Barton Anne A   Coenen Marieke M   Mariette Xavier X   Miceli Corinne C   Shadick Nancy N   Weinblatt Michael M   de Vries Niek N   Tak Paul P PP   Gerlag Danielle D   Huizinga Tom W J TWJ   Kurreeman Fina F   Allaart Cornelia F CF   Louis Bridges S S   Criswell Lindsey L   Moreland Larry L   Klareskog Lars L   Saevarsdottir Saedis S   Padyukov Leonid L   Gregersen Peter K PK   Friend Stephen S   Plenge Robert R   Stolovitzky Gustavo G   Oliva Baldo B   Guan Yuanfang Y   Mangravite Lara M LM  

Nature communications 20160823


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  ...[more]

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