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

Lookup NU author(s): Dr Svetlana Cherlin, Professor Heather Cordell

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

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.


Publication metadata

Author(s): Cherlin S, Plant D, Taylor JC, Colombo M, Spiliopoulou A, Tzanis E, Morgan AW, Barnes MR, McKeigue P, Barrett JH, Pitzalis C, Barton A, Cordell HJ

Publication type: Article

Publication status: Published

Journal: Genetic Epidemiology

Year: 2018

Volume: 42

Issue: 8

Pages: 754-771

Print publication date: 01/12/2018

Online publication date: 12/10/2018

Acceptance date: 28/07/2018

Date deposited: 13/12/2018

ISSN (print): 0741-0395

ISSN (electronic): 1098-2272

Publisher: John Wiley & Sons, Inc.

URL: https://doi.org/10.1002/gepi.22159

DOI: 10.1002/gepi.22159


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