Genome-wide association analysis of anti-TNF drug response in patients with rheumatoid arthritis

Maša Umiċeviċ Mirkov, Jing Cui, Sita H Vermeulen, Eli A Stahl, Erik J M Toonen, Remco R Makkinje, Annette T Lee, Tom W J Huizinga, Renee Allaart, Anne Barton, Xavier Mariette, Corinne Richard Miceli, Lindsey A Criswell, Paul P Tak, Niek de Vries, Saedis Saevarsdottir, Leonid Padyukov, S Louis Bridges, Dirk-Jan van Schaardenburg, Tim L Jansen, Ellen A J Dutmer, Mart A F J van de Laar, Pilar Barrera, Timothy R D J Radstake, Piet L C M van Riel, Hans Scheffer, Barbara Franke, Han G Brunner, Robert M Plenge, Peter K Gregersen, Henk-Jan Guchelaar, Marieke J H Coenen, Maša Umiċeviċ Mirkov, Jing Cui, Sita H Vermeulen, Eli A Stahl, Erik J M Toonen, Remco R Makkinje, Annette T Lee, Tom W J Huizinga, Renee Allaart, Anne Barton, Xavier Mariette, Corinne Richard Miceli, Lindsey A Criswell, Paul P Tak, Niek de Vries, Saedis Saevarsdottir, Leonid Padyukov, S Louis Bridges, Dirk-Jan van Schaardenburg, Tim L Jansen, Ellen A J Dutmer, Mart A F J van de Laar, Pilar Barrera, Timothy R D J Radstake, Piet L C M van Riel, Hans Scheffer, Barbara Franke, Han G Brunner, Robert M Plenge, Peter K Gregersen, Henk-Jan Guchelaar, Marieke J H Coenen

Abstract

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.

Keywords: Anti-TNF; Gene Polymorphism; Pharmacogenetics; Rheumatoid Arthritis.

Figures

Figure 1. Study design of a multi-stage…
Figure 1. Study design of a multi-stage GWAS of response to anti-TNF medication in RA patients
We started out with meta-analysis of GWAS data from Dutch cohort comprised of 882 RA patients treated with anti-TNF medication. We selected 772 SNPs that reached p

Figure 2. Top gene network derived from…

Figure 2. Top gene network derived from Ingenuity Pathway Analysis

Genes/gene products are represented graphically…

Figure 2. Top gene network derived from Ingenuity Pathway Analysis
Genes/gene products are represented graphically as nodes and the biological relationship between two nodes is represented as an edge (line). Grey colour of the node is indicating genes that were identified in the stage 1 GWAS (p acts on B, A binds to B), dotted lines indicated an indirect interaction. All edges are supported by at least one reference from the literature or from canonical information stored in the Ingenuity Knowledge Base. Nodes are displayed using various shapes that represent the functional classes of the gene product( cytokines, enzyme, complex/group, transporter, transcription regulator, transmembrane receptor, ion channel, ligand-dependent nuclear receptor, kinase, growth factor, other).
Figure 2. Top gene network derived from…
Figure 2. Top gene network derived from Ingenuity Pathway Analysis
Genes/gene products are represented graphically as nodes and the biological relationship between two nodes is represented as an edge (line). Grey colour of the node is indicating genes that were identified in the stage 1 GWAS (p acts on B, A binds to B), dotted lines indicated an indirect interaction. All edges are supported by at least one reference from the literature or from canonical information stored in the Ingenuity Knowledge Base. Nodes are displayed using various shapes that represent the functional classes of the gene product( cytokines, enzyme, complex/group, transporter, transcription regulator, transmembrane receptor, ion channel, ligand-dependent nuclear receptor, kinase, growth factor, other).

Source: PubMed

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