TY - JOUR
T1 - Genome-wide association analysis of anti-TNF drug response in patients with rheumatoid arthritis
AU - Mirkov, Maša Umiċeviċ
AU - Cui, Jing
AU - Vermeulen, Sita H.
AU - Stahl, Eli A.
AU - Toonen, Erik J.M.
AU - Makkinje, Remco R.
AU - Lee, Annette T.
AU - Huizinga, Tom W.J.
AU - Allaart, Renee
AU - Barton, Anne
AU - Mariette, Xavier
AU - Miceli, Corinne Richard
AU - Criswell, Lindsey A.
AU - Tak, Paul
AU - de Vries, Niek
AU - Saevarsdottir, Saedis
AU - Padyukov, Leonid
AU - Bridges, S. Louis
AU - van Schaardenburg, Dirk-Jan
AU - Jansen, Tim L.
AU - Dutmer, Ellen A.J.
AU - van de Laar, Mart A F J
AU - Barrera, Pilar
AU - Radstake, Timothy R.D.J.
AU - van Riel, Piet L.C.M.
AU - Scheffer, Hans
AU - Franke, Barbara
AU - Brunner, Han G.
AU - Plenge, Robert M.
AU - Gregersen, Peter K.
AU - Guchelaar, Henk-Jan
AU - Coenen, Marieke J.H.
PY - 2013/12/11
Y1 - 2013/12/11
N2 - Background
Treatment strategies blocking tumour necrosis factor (anti-TNF) have proven very successful in patients with rheumatoid arthritis (RA). However, a significant subset of patients does not respond for unknown reasons. Currently, there are no means of identifying these patients before treatment. This study was aimed at identifying genetic factors predicting anti-TNF treatment outcome in patients with RA using a genome-wide association approach.
Methods
We conducted a multistage, genome-wide association study with a primary analysis of 2 557 253 single-nucleotide polymorphisms (SNPs) in 882 patients with RA receiving anti-TNF therapy included through the Dutch Rheumatoid Arthritis Monitoring (DREAM) registry and the database of Apotheekzorg. Linear regression analysis of changes in the Disease Activity Score in 28 joints after 14 weeks of treatment was performed using an additive model. Markers with p<10−3 were selected for replication in 1821 patients from three independent cohorts. Pathway analysis including all SNPs with p<10−3 was performed using Ingenuity.
Results
772 markers showed evidence of association with treatment outcome in the initial stage. Eight genetic loci showed improved p value in the overall meta-analysis compared with the first stage, three of which (rs1568885, rs1813443 and rs4411591) showed directional consistency over all four cohorts studied. We were unable to replicate markers previously reported to be associated with anti-TNF outcome. Network analysis indicated strong involvement of biological processes underlying inflammatory response and cell morphology.
Conclusions
Using a multistage strategy, we have identified eight genetic loci associated with response to anti-TNF treatment. Further studies are required to validate these findings in additional patient collections.
AB - Background
Treatment strategies blocking tumour necrosis factor (anti-TNF) have proven very successful in patients with rheumatoid arthritis (RA). However, a significant subset of patients does not respond for unknown reasons. Currently, there are no means of identifying these patients before treatment. This study was aimed at identifying genetic factors predicting anti-TNF treatment outcome in patients with RA using a genome-wide association approach.
Methods
We conducted a multistage, genome-wide association study with a primary analysis of 2 557 253 single-nucleotide polymorphisms (SNPs) in 882 patients with RA receiving anti-TNF therapy included through the Dutch Rheumatoid Arthritis Monitoring (DREAM) registry and the database of Apotheekzorg. Linear regression analysis of changes in the Disease Activity Score in 28 joints after 14 weeks of treatment was performed using an additive model. Markers with p<10−3 were selected for replication in 1821 patients from three independent cohorts. Pathway analysis including all SNPs with p<10−3 was performed using Ingenuity.
Results
772 markers showed evidence of association with treatment outcome in the initial stage. Eight genetic loci showed improved p value in the overall meta-analysis compared with the first stage, three of which (rs1568885, rs1813443 and rs4411591) showed directional consistency over all four cohorts studied. We were unable to replicate markers previously reported to be associated with anti-TNF outcome. Network analysis indicated strong involvement of biological processes underlying inflammatory response and cell morphology.
Conclusions
Using a multistage strategy, we have identified eight genetic loci associated with response to anti-TNF treatment. Further studies are required to validate these findings in additional patient collections.
KW - IR-86362
KW - METIS-291290
U2 - 10.1136/annrheumdis-2012-202405
DO - 10.1136/annrheumdis-2012-202405
M3 - Article
VL - 72
SP - 1375
EP - 1381
JO - Annals of the rheumatic diseases
JF - Annals of the rheumatic diseases
SN - 0003-4967
ER -