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

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; Muhammad Ammad-Ud-Din; Chloe Agathe Azencott; Víctor Bellón; Valentina Boeva; Kerstin Bunte; Himanshu Chheda; Lu Cheng; Jukka Corander; Michel Dumontier; Anna Goldenberg; Peddinti Gopalacharyulu; Mohsen Hajiloo; Daniel Hidru; Alok Jaiswal; Samuel Kaski; Beyrem Khalfaoui; Suleiman Ali Khan; Eric R. Kramer; Pekka Marttinen; 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

doi:10.1038/ncomms12460

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

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. FalcaoChristine Suver, Bruce Hoff, Venkat S K Balagurusamy, Donna Dillenberger, Elias Chaibub Neto, Thea Norman, Tero Aittokallio, Muhammad Ammad-Ud-Din, Chloe Agathe Azencott, Víctor Bellón, Valentina Boeva, Kerstin Bunte, Himanshu Chheda, Lu Cheng, Jukka Corander, Michel Dumontier, Anna Goldenberg, Peddinti Gopalacharyulu, Mohsen Hajiloo, Daniel Hidru, Alok Jaiswal, Samuel Kaski, Beyrem Khalfaoui, Suleiman Ali Khan, Eric R. Kramer, Pekka Marttinen, 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^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Scientific › peer-review

69 Citations (Scopus)

231 Downloads (Pure)

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.

Original language	English
Article number	12460
Pages (from-to)	1-9
Journal	Nature Communications
Volume	7
DOIs	https://doi.org/10.1038/ncomms12460
Publication status	Published - 23 Aug 2016
MoE publication type	A1 Journal article-refereed

Access to Document

10.1038/ncomms12460

ncomms12460Final published version, 434 KBLicence: CC BY

Cite this

Sieberts, S. K., Zhu, F., García-García, J., Stahl, E., Pratap, A., Pandey, G., Pappas, D., Aguilar, D., Anton, B., Bonet, J., Eksi, R., Fornés, O., Guney, E., Li, H., Marín, M. A., Panwar, B., Planas-Iglesias, J., Poglayen, D., Cui, J., ... Mangravite, L. M. (2016). Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis. Nature Communications, 7, 1-9. Article 12460. https://doi.org/10.1038/ncomms12460

@article{a38cd715446d48ff9960c2c69a8a4222,

title = "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 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.",

author = "Sieberts, {Solveig K.} and Fan Zhu and Javier Garc{\'i}a-Garc{\'i}a 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 Forn{\'e}s and Emre Guney and Hongdong Li and Mar{\'i}n, {Manuel Alejandro} and Bharat Panwar and Joan Planas-Iglesias and Daniel Poglayen and Jing Cui and Falcao, {Andre O.} and Christine Suver and Bruce Hoff and Balagurusamy, {Venkat S K} and Donna Dillenberger and Neto, {Elias Chaibub} and Thea Norman and Tero Aittokallio and Muhammad Ammad-Ud-Din and Azencott, {Chloe Agathe} and V{\'i}ctor Bell{\'o}n 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 Khan, {Suleiman Ali} and Kramer, {Eric R.} and Pekka Marttinen and Mezlini, {Aziz M.} and Bhuvan Molparia and Matti Pirinen and Janna Saarela and Matthias Samwald and V{\'e}ronique Stoven and Hao Tang and Jing Tang and Ali Torkamani and Vert, {Jean Phillipe} and Bo Wang and Tao Wang and Krister Wennerberg and Wineinger, {Nathan E.} 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 {De Vries}, Niek and Tak, {Paul P.} and Danielle Gerlag and Huizinga, {Tom W J} and Fina Kurreeman and Allaart, {Cornelia F.} and {Louis Bridges}, S. and Lindsey Criswell and Larry Moreland and Lars Klareskog and Saedis Saevarsdottir and Leonid Padyukov and Gregersen, {Peter K.} and Stephen Friend and Robert Plenge and Gustavo Stolovitzky and Baldo Oliva and Yuanfang Guan and Mangravite, {Lara M.}",

year = "2016",

month = aug,

day = "23",

doi = "10.1038/ncomms12460",

language = "English",

volume = "7",

pages = "1--9",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "Nature Portfolio",

}

Sieberts, SK, Zhu, F, García-García, J, Stahl, E, Pratap, A, Pandey, G, Pappas, D, Aguilar, D, Anton, B, Bonet, J, Eksi, R, Fornés, O, Guney, E, Li, H, Marín, MA, Panwar, B, Planas-Iglesias, J, Poglayen, D, Cui, J, Falcao, AO, Suver, C, Hoff, B, Balagurusamy, VSK, Dillenberger, D, Neto, EC, Norman, T, Aittokallio, T, Ammad-Ud-Din, M, Azencott, CA, Bellón, 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, SA, Kramer, ER, Marttinen, P, Mezlini, AM, Molparia, B, Pirinen, M, Saarela, J, Samwald, M, Stoven, V, Tang, H, Tang, J, Torkamani, A, Vert, JP, Wang, B, Wang, T, Wennerberg, K, Wineinger, NE, Xiao, G, Xie, Y, Yeung, R, Zhan, X, Zhao, C, Greenberg, J, Kremer, J, Michaud, K, Barton, A, Coenen, M, Mariette, X, Miceli, C, Shadick, N, Weinblatt, M, De Vries, N, Tak, PP, Gerlag, D, Huizinga, TWJ, Kurreeman, F, Allaart, CF, Louis Bridges, S, Criswell, L, Moreland, L, Klareskog, L, Saevarsdottir, S, Padyukov, L, Gregersen, PK, Friend, S, Plenge, R, Stolovitzky, G, Oliva, B, Guan, Y & Mangravite, LM 2016, 'Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis', Nature Communications, vol. 7, 12460, pp. 1-9. https://doi.org/10.1038/ncomms12460

TY - JOUR

T1 - Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

AU - Sieberts, Solveig K.

AU - Zhu, Fan

AU - García-García, Javier

AU - Stahl, Eli

AU - Pratap, Abhishek

AU - Pandey, Gaurav

AU - Pappas, Dimitrios

AU - Aguilar, Daniel

AU - Anton, Bernat

AU - Bonet, Jaume

AU - Eksi, Ridvan

AU - Fornés, Oriol

AU - Guney, Emre

AU - Li, Hongdong

AU - Marín, Manuel Alejandro

AU - Panwar, Bharat

AU - Planas-Iglesias, Joan

AU - Poglayen, Daniel

AU - Cui, Jing

AU - Falcao, Andre O.

AU - Suver, Christine

AU - Hoff, Bruce

AU - Balagurusamy, Venkat S K

AU - Dillenberger, Donna

AU - Neto, Elias Chaibub

AU - Norman, Thea

AU - Aittokallio, Tero

AU - Ammad-Ud-Din, Muhammad

AU - Azencott, Chloe Agathe

AU - Bellón, Víctor

AU - Boeva, Valentina

AU - Bunte, Kerstin

AU - Chheda, Himanshu

AU - Cheng, Lu

AU - Corander, Jukka

AU - Dumontier, Michel

AU - Goldenberg, Anna

AU - Gopalacharyulu, Peddinti

AU - Hajiloo, Mohsen

AU - Hidru, Daniel

AU - Jaiswal, Alok

AU - Kaski, Samuel

AU - Khalfaoui, Beyrem

AU - Khan, Suleiman Ali

AU - Kramer, Eric R.

AU - Marttinen, Pekka

AU - Mezlini, Aziz M.

AU - Molparia, Bhuvan

AU - Pirinen, Matti

AU - Saarela, Janna

AU - Samwald, Matthias

AU - Stoven, Véronique

AU - Tang, Hao

AU - Tang, Jing

AU - Torkamani, Ali

AU - Vert, Jean Phillipe

AU - Wang, Bo

AU - Wang, Tao

AU - Wennerberg, Krister

AU - Wineinger, Nathan E.

AU - Xiao, Guanghua

AU - Xie, Yang

AU - Yeung, Rae

AU - Zhan, Xiaowei

AU - Zhao, Cheng

AU - Greenberg, Jeff

AU - Kremer, Joel

AU - Michaud, Kaleb

AU - Barton, Anne

AU - Coenen, Marieke

AU - Mariette, Xavier

AU - Miceli, Corinne

AU - Shadick, Nancy

AU - Weinblatt, Michael

AU - De Vries, Niek

AU - Tak, Paul P.

AU - Gerlag, Danielle

AU - Huizinga, Tom W J

AU - Kurreeman, Fina

AU - Allaart, Cornelia F.

AU - Louis Bridges, S.

AU - Criswell, Lindsey

AU - Moreland, Larry

AU - Klareskog, Lars

AU - Saevarsdottir, Saedis

AU - Padyukov, Leonid

AU - Gregersen, Peter K.

AU - Friend, Stephen

AU - Plenge, Robert

AU - Stolovitzky, Gustavo

AU - Oliva, Baldo

AU - Guan, Yuanfang

AU - Mangravite, Lara M.

PY - 2016/8/23

Y1 - 2016/8/23

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84984705713&partnerID=8YFLogxK

U2 - 10.1038/ncomms12460

DO - 10.1038/ncomms12460

M3 - Article

AN - SCOPUS:84984705713

SN - 2041-1723

VL - 7

SP - 1

EP - 9

JO - Nature Communications

JF - Nature Communications

M1 - 12460

ER -

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

Abstract

Access to Document

Other files and links

Fingerprint

Cite this