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

Khan SA; Molparia B; Stoven V; Pandey G; Boeva V; Plenge R; Stolovitzky G; Weinblatt M; Kremer J; Pratap A; Poglayen D; Hajiloo M; Kurreeman F; Bonet J; Moreland L; Aittokallio T; Dillenberger D; Bunte K; Zhu F; Greenberg J; Coenen M; Pappas D; Shadick N; Bridges SL; Falcao AO; Khalfaoui B; Hidru D; Li HD; Cheng L; Jaiswal A; Bellon V; Corander J; Zhan XW; Klareskog L; Xiao GH; Panwar B; Wennerberg K; Norman T; Balagurusamy VSK; Saarela J; Stahl E; Aguilar D; Garcia-Garcia J; Yeung R; Planas-Iglesias J; Gopalacharyulu P; Saevarsdottir S; Pirinen M; Mariette X; Huizinga TWJ; Tang J; Miceli C; Mezlini AM; Suver C; Fornes O; Anton B; Wang B; Criswell L; Xie Y; Hoff B; Barton A; Torkamani A; Vert JP; Azencott CA; Kramer ER; Neto EC; Marin MA; Goldenberg A; Cui J; Sieberts SK; Padyukov L; Tang H; Guney E; Kaski S; Michaud K; Marttinen P; Chheda H; Allaart CF; Friend S; Eksi R; Gerlag D; Samwald M; Dumontier M; Tak PP; Gregersen PK; Zhao C; Wang T; Guan YF; Mangravite LM; Oliva B; de Vries N; Ammad-ud-din M; Wineinger NE
Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

Khan SA
Molparia B
Stoven V
Pandey G
Boeva V
Plenge R
Stolovitzky G
Weinblatt M
Kremer J
Pratap A
Poglayen D
Hajiloo M
Kurreeman F
Bonet J
Moreland L
Aittokallio T
Dillenberger D
Bunte K
Zhu F
Greenberg J
Coenen M
Pappas D
Shadick N
Bridges SL
Falcao AO
Khalfaoui B
Hidru D
Li HD
Cheng L
Jaiswal A
Bellon V
Corander J
Zhan XW
Klareskog L
Xiao GH
Panwar B
Wennerberg K
Norman T
Balagurusamy VSK
Saarela J
Stahl E
Aguilar D
Garcia-Garcia J
Yeung R
Planas-Iglesias J
Gopalacharyulu P
Saevarsdottir S
Pirinen M
Mariette X
Huizinga TWJ
Tang J
Miceli C
Mezlini AM
Suver C
Fornes O
Anton B
Wang B
Criswell L
Xie Y
Hoff B
Barton A
Torkamani A
Vert JP
Azencott CA
Kramer ER
Neto EC
Marin MA
Goldenberg A
Cui J
Sieberts SK
Padyukov L
Tang H
Guney E
Kaski S
Michaud K
Marttinen P
Chheda H
Allaart CF
Friend S
Eksi R
Gerlag D
Samwald M
Dumontier M
Tak PP
Gregersen PK
Zhao C
Wang T
Guan YF
Mangravite LM
Oliva B
de Vries N
Ammad-ud-din M
Wineinger NE
Katso/Avaa
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NATURE PUBLISHING GROUP
doi:10.1038/ncomms12460
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Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2021042716061
Tiivistelmä
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.
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