Goulielmos, GN, Zervou, MI, Myrthianou, E et al. (3 more authors) (2016) Genetic data: The new challenge of personalized medicine, insights for rheumatoid arthritis patients. Gene, 583 (2). pp. 90-101. ISSN 0378-1119
Abstract
Rapid advances in genotyping technology, analytical methods, and the establishment of large cohorts for population genetic studies have resulted in a large new body of information about the genetic basis of human rheumatoid arthritis (RA). Improved understanding of the root pathogenesis of the disease holds the promise of improved diagnostic and prognostic tools based upon this information. In this review, we summarize the nature of new genetic findings in human RA, including susceptibility loci and gene-gene and gene-environment interactions, as well as genetic loci associated with sub-groups of patients and those associated with response to therapy. Possible uses of these data are discussed, such as prediction of disease risk as well as personalized therapy and prediction of therapeutic response and risk of adverse events. While these applications are largely not refined to the point of clinical utility in RA, it seems likely that multi-parameter datasets including genetic, clinical, and biomarker data will be employed in the future care of RA patients.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2016, Elsevier B.V. This is an author produced version of a paper published in Gene. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Gene polymorphisms; Genotyping technologies; Personalized medicine; Rheumatoid arthritis |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Institute of Rheumatology & Musculoskeletal Medicine (LIRMM) (Leeds) > Experimental Musculoskeletal Medicine (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 Jul 2016 11:49 |
Last Modified: | 11 Apr 2017 16:47 |
Published Version: | http://dx.doi.org/10.1016/j.gene.2016.02.004 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.gene.2016.02.004 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:98153 |