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

Show full item record



Permalink

http://hdl.handle.net/10138/166931

Citation

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 . https://doi.org/10.1038/ncomms12460

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: University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Department of Mathematics and Statistics
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Department of Computer Science
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
Date: 2016-08
Language: eng
Number of pages: 9
Belongs to series: Nature Communications
ISSN: 2041-1723
URI: http://hdl.handle.net/10138/166931
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 (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.
Subject: GENOME-WIDE ASSOCIATION
STACKED GENERALIZATION
MISSING HERITABILITY
NETWORK INFERENCE
CLINICAL-RESPONSE
COMPLEX TRAITS
DISEASE
RISK
METAANALYSIS
INFLIXIMAB
3121 General medicine, internal medicine and other clinical medicine
113 Computer and information sciences
Rights:


Files in this item

Total number of downloads: Loading...

Files Size Format View
ncomms12460.pdf 360.9Kb PDF View/Open

This item appears in the following Collection(s)

Show full item record