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Lookup NU author(s): Dr Svetlana CherlinORCiD, Professor Heather Cordell
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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.
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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