Genome-wide association study of response to methotrexate in early rheumatoid arthritis patients

Taylor, John; Bongartz, Tim; Massey, Jonathan; Mifsud, Borbála; Spiliopoulou, Athina; Scott, Ian C.; Wang, Jianmei; Morgan, Michael D.; Plant, Darren; Colombo, Marco; Orchard, Peter; Twigg, Sarah; McInnes, Iain B.; Porter, Duncan; Freeston, Jane; Nam, Jackie; Cordell, Heather J.; Isaacs, John D.; Strathdee, Jenna L.; Arnett, Donna K.; Hair, Maria J. H. de; Tak, Paul P.; Aslibekyan, Stella; Vollenhoven, Ronald F. van; Padyukov, Leonid; Bridges, S. Louis; Pitzalis, Costantino; Cope, Andrew P.; Verstappen, Suzanne; Emery, Paul; Barnes, Michael R.; Agakov, Felix; McKeigue, Paul; Mushiroda, Taisei; Kubo, Michiaki; Weinshilboum, Richard M.; Barton, Anne; Morgan, Ann W.; Barrett, Jennifer H.

doi:10.1038/s41397-018-0025-5

Cited by 46 publications

(41 citation statements)

References 57 publications

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“…However, for the anti-TNF data, the 6 months measures was available for most of the subjects. In view of this fact, and also in accord with Taylor et al (2018) and Massey et al (2018), we used the 6 months follow-up measure, or 3 months if this was not available, noting that for the MTX data set the precise duration of treatment was anyway not available. In view of this fact, and also in accord with Taylor et al (2018) and Massey et al (2018), we used the 6 months follow-up measure, or 3 months if this was not available, noting that for the MTX data set the precise duration of treatment was anyway not available.…”

Section: Discussionmentioning

confidence: 99%

“…Also, for subjects with both 3 and 6 months follow-up measures, the changes in treatment response were very similar (Supporting Information Figure 5). In view of this fact, and also in accord with Taylor et al (2018) and Massey et al (2018), we used the 6 months follow-up measure, or 3 months if this was not available, noting that for the MTX data set the precise duration of treatment was anyway not available.…”

Section: Discussionmentioning

confidence: 99%

“…This may reflect the fact that this trait shows one reasonably compelling signal of association, as supported by the Manhattan plot of the associations between the SNPs and the phenotype (Supporting Information Figure 2e). However, this association signal was not maintained when adding in the additional cohorts considered by Taylor et al (2018), and so most likely represents a statistical false positive. As for the other traits, the Manhattan plots of the p-values show little in the way of significant associations between the SNPs and the phenotype (Supporting Information Figure 2a-d).…”

Section: Prediction Based On Snps Alonementioning

confidence: 94%

“…Imputed genotype data at 7,542,957 genome-wide SNPs for 828 patients receiving MTX treatment (collected across a variety of cohorts, see Taylor et al, 2018 for details) were available; this corresponds to the MATURA-owned data on a subset of the patients described by Taylor et al (2018). Individual and SNP QC (as described above for the anti-TNF data set) resulted in a data set with 657 patients and 6,291,430 SNPs.…”

Section: Mtx Data Setmentioning

confidence: 99%

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Prediction of treatment response in rheumatoid arthritis patients using genome‐wide SNP data

Cherlin

Plant

Taylor

et al. 2018

Genetic Epidemiology

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Although a number of treatments are available for rheumatoid arthritis (RA), each of them shows a significant nonresponse rate in patients. Therefore, predicting a priori the likelihood of treatment response would be of great patient benefit. Here, we conducted a comparison of a variety of statistical methods for predicting three measures of treatment response, between baseline and 3 or 6 months, using genome‐wide SNP data from RA patients available from the MAximising Therapeutic Utility in Rheumatoid Arthritis (MATURA) consortium. Two different treatments and 11 different statistical methods were evaluated. We used 10‐fold cross validation to assess predictive performance, with nested 10‐fold cross validation used to tune the model hyperparameters when required. Overall, we found that SNPs added very little prediction information to that obtained using clinical characteristics only, such as baseline trait value. This observation can be explained by the lack of strong genetic effects and the relatively small sample sizes available; in analysis of simulated and real data, with larger effects and/or larger sample sizes, prediction performance was much improved. Overall, methods that were consistent with the genetic architecture of the trait were able to achieve better predictive ability than methods that were not. For treatment response in RA, methods that assumed a complex underlying genetic architecture achieved slightly better prediction performance than methods that assumed a simplified genetic architecture.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Prediction Based On Snps Alonementioning

confidence: 94%

Section: Mtx Data Setmentioning

confidence: 99%

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Prediction of treatment response in rheumatoid arthritis patients using genome‐wide SNP data

Cherlin

Plant

Taylor

et al. 2018

Genetic Epidemiology

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“…Ann Morgan and Jennifer Barrett for methotrexate response in RA, on behalf of the CARDERA, IACON, PAMERA, and RAMS Consortia; 59 Jonas Kuiper and Bobby Koeleman for the birdshot chorioretinopathy GWAS; 60 We thank Urs Christen for helpful discussions on IP-10.…”

Section: Acknowledgementsmentioning

confidence: 99%

Informed dimension reduction of clinically-related genome-wide association summary data characterises cross-trait axes of genetic risk

Burren

Reales

Wong

et al. 2020

Preprint

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Genome-wide association studies (GWAS) have uncovered pervasive genetic overlap between common clinically related immune-mediated diseases (IMD). To distinguish axes of IMD risk, and extend genetic knowledge of rare IMDs and subtypes, we developed a Bayesian shrinkage approach to perform a disease-focused decomposition of IMD GWAS summary statistics. We derive 13 components which summarise the multidimensional patterns of IMD genetic risk including those related to raised eosinophil count and serum IP-10. Projection of UK Biobank data demonstrated the IMD-specificity and accuracy of our reduced dimension basis in independent datasets. By projecting 22 rare IMD or IMD subtypes onto the basis we were able to identify disease-discriminating components and suggest novel associations. Requiring only summary level data, our approach allows the genetic architectures across any range of clinically-related traits to be characterised in fewer dimensions, facilitating the analysis of studies with modest sample size, where classical GWAS approaches are challenging.

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Toward Individualized Prediction of Response to Methotrexate in Early Rheumatoid Arthritis: A Pharmacogenomics‐Driven Machine Learning Approach

Myasoedova

Athreya

Crowson

et al. 2022

Arthritis Care & Research

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on behalf of the Pharmacogenetics of Methotrexate in Rheumatoid Arthritis Consortium Objective. To test the ability of machine learning (ML) approaches with clinical and genomic biomarkers to predict methotrexate treatment response in patients with early rheumatoid arthritis (RA).Methods. Demographic, clinical, and genomic data from 643 patients of European ancestry with early RA (mean age 54 years; 70% female) subdivided into a training (n = 336) and validation cohort (n = 307) were used. The genomic data comprised 160 single-nucleotide polymorphisms (SNPs) previously associated with RA or methotrexate metabolism. Response to methotrexate monotherapy was defined as good or moderate by the European Alliance of Associations for Rheumatology (EULAR) response criteria at the 3-month follow-up. Supervised ML methods were trained with 5 repeats and 10-fold cross-validation using the training cohort. Prediction performance was validated in the independent validation cohort.Results. Supervised ML methods combining age, sex, smoking, rheumatoid factor, baseline Disease Activity Score in 28 joints (DAS28) scores and 160 SNPs predicted EULAR response at 3 months with the area under the receiver operating curve of 0.84 (P = 0.05) in the training cohort and achieved a prediction accuracy of 76% (P = 0.05) in the validation cohort (sensitivity 72%, specificity 77%). Intergenic SNPs rs12446816, rs13385025, rs113798271, and ATIC (rs2372536) had variable importance above 60.0 and along with baseline DAS28 scores were among the top predictors of methotrexate response.Conclusion. Pharmacogenomic biomarkers combined with baseline DAS28 scores can be useful in predicting response to methotrexate in patients with early RA. Applying ML to predict treatment response holds promise for guiding effective RA treatment choices, including timely escalation of RA therapies.

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Genome-wide association study of response to methotrexate in early rheumatoid arthritis patients

Cited by 46 publications

References 57 publications

Prediction of treatment response in rheumatoid arthritis patients using genome‐wide SNP data

Prediction of treatment response in rheumatoid arthritis patients using genome‐wide SNP data

Informed dimension reduction of clinically-related genome-wide association summary data characterises cross-trait axes of genetic risk

Toward Individualized Prediction of Response to Methotrexate in Early Rheumatoid Arthritis: A Pharmacogenomics‐Driven Machine Learning Approach

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