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

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Sieberts , S K , Zhu , F , Garcia-Garcia , J , Stahl , E , Pratap , A , Pandey , G , Pappas , D , Aguilar , D , Anton , B , Bonet , J , Eksi , R , Fornes , O , Guney , E , Li , H , Marin , M A , Panwar , B , Planas-Iglesias , J , Poglayen , D , Cui , J , Falcao , A O , Suver , C , Hoff , B , Balagurusamy , V S K , Dillenberger , D , Neto , E C , Norman , T , Aittokallio , T , Ammad-ud-din , M , Azencott , C-A , Bellon , V , Boeva , V , Bunte , K , Chheda , H , Cheng , L , Corander , J , Dumontier , M , Goldenberg , A , Gopalacharyulu , P , Hajiloo , M , Hidru , D , Jaiswal , A , Kaski , S , Khalfaoui , B , Khan , S A , Kramer , E R , Marttinen , P , Pirinen , M , Saarela , J , Tang , J , Wennerberg , K & Rheumatoid Arth Challenge 2016 , ' Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis ' , Nature Communications , vol. 7 , 12460 .

Title: Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis
Author: Sieberts, Solveig K.; Zhu, Fan; Garcia-Garcia, Javier; Stahl, Eli; Pratap, Abhishek; Pandey, Gaurav; Pappas, Dimitrios; Aguilar, Daniel; Anton, Bernat; Bonet, Jaume; Eksi, Ridvan; Fornes, Oriol; Guney, Emre; Li, Hongdong; Marin, Manuel Alejandro; Panwar, Bharat; Planas-Iglesias, Joan; Poglayen, Daniel; Cui, Jing; Falcao, Andre O.; Suver, Christine; Hoff, Bruce; Balagurusamy, Venkat S. K.; Dillenberger, Donna; Neto, Elias Chaibub; Norman, Thea; Aittokallio, Tero; Ammad-ud-din, Muhammad; Azencott, Chloe-Agathe; Bellon, Victor; Boeva, Valentina; Bunte, Kerstin; Chheda, Himanshu; Cheng, Lu; Corander, Jukka; Dumontier, Michel; Goldenberg, Anna; Gopalacharyulu, Peddinti; Hajiloo, Mohsen; Hidru, Daniel; Jaiswal, Alok; Kaski, Samuel; Khalfaoui, Beyrem; Khan, Suleiman Ali; Kramer, Eric R.; Marttinen, Pekka; Pirinen, Matti; Saarela, Janna; Tang, Jing; Wennerberg, Krister; Rheumatoid Arth Challenge
Contributor organization: Institute for Molecular Medicine Finland
Department of Mathematics and Statistics
Helsinki Institute for Information Technology
Jukka Corander / Principal Investigator
Department of Computer Science
Janna Saarela / Principal Investigator
Krister Wennerberg / Principal Investigator
Biostatistics Helsinki
Statistical and population genetics
Date: 2016-08
Language: eng
Number of pages: 9
Belongs to series: Nature Communications
ISSN: 2041-1723
Abstract: Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in Bone-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 ( 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.
Description: Correction: vol 7, 13205, 2016, doi:10.1038/ncomms13205
3121 General medicine, internal medicine and other clinical medicine
113 Computer and information sciences
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion

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