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

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Article number12460
Pages (from-to)1-9
JournalNature Communications
Volume7
Publication statusPublished - 23 Aug 2016
MoE publication typeA1 Journal article-refereed

Researchers

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

Research units

  • Sage Bionetworks
  • University of Michigan, Ann Arbor
  • Pompeu Fabra University
  • Icahn School of Medicine at Mount Sinai (ISMMS)
  • Corrona LLC
  • Northeastern University
  • Harvard University
  • University of Lisbon
  • IBM
  • INSERM U900
  • University of Helsinki
  • Institute of Molecular Medicine Finland FIMM
  • Stanford University
  • University of Toronto
  • Scripps Research Institute
  • Medical University of Vienna
  • University of Texas at Dallas
  • New York University
  • Albany Medical College
  • Arthritis Research Center Foundation
  • Central Manchester Foundation Trust
  • Radboud University Nijmegen
  • INSERM U1184
  • University of Amsterdam
  • Glaxo Smith Kline
  • Leiden University
  • University of Alabama at Birmingham
  • University of California at San Francisco
  • University of Pittsburgh
  • Karolinska Institutet
  • North Shore-Long Island Jewish Health System
  • Merck

Abstract

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in 1/4one-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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