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  • Review Article
  • Published:

The genetics revolution in rheumatology: large scale genomic arrays and genetic mapping

Nature Reviews Rheumatology volume 13, pages 421–432 (2017)Cite this article

Subjects

Key Points

  • Large-scale genome-wide association studies (GWAS), meta-analyses, fine-mapping and studies with populations of diverse ancestries have identified hundreds of genetic loci that predispose individuals to rheumatic diseases

  • Genetic susceptibility regions overlap between diseases, suggesting common disease mechanisms and treatment choices

  • Genetic associations can implicate pathways (for example, T helper cell differentiation) involved in disease onset and/or outcome

  • A number of existing drugs (such as tofacitinib, tocilizumab and abatacept) target proteins encoded by genes identified through GWAS, suggesting that genetic studies can help identify novel therapeutic targets

  • Most disease-associated variants map to noncoding regions responsible for controlling gene expression and DNA conformational analysis is beginning to help in the challenging task of unravelling their function

Abstract

Susceptibility to rheumatic diseases, such as osteoarthritis, rheumatoid arthritis, ankylosing spondylitis, systemic lupus erythematosus, juvenile idiopathic arthritis and psoriatic arthritis, includes a large genetic component. Understanding how an individual's genetic background influences disease onset and outcome can lead to a better understanding of disease biology, improved diagnosis and treatment, and, ultimately, to disease prevention or cure. The past decade has seen great progress in the identification of genetic variants that influence the risk of rheumatic diseases. The challenging task of unravelling the function of these variants is ongoing. In this Review, the major insights from genetic studies, gained from advances in technology, bioinformatics and study design, are discussed in the context of rheumatic disease. In addition, pivotal genetic studies in the main rheumatic diseases are highlighted, with insights into how these studies have changed the way we view these conditions in terms of disease overlap, pathways of disease and potential new therapeutic targets. Finally, the limitations of genetic studies, gaps in our knowledge and ways in which current genetic knowledge can be fully translated into clinical benefit are examined.

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Figure 1: Timeline of the major advances in rheumatoid arthritis genetics research.
Figure 2: Overlap of associated loci among five rheumatic diseases.
Figure 3: Genes of the T helper cell differentiation pathway implicated in rheumatic diseases.

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Acknowledgements

The authors' research work was supported by the Wellcome Trust (grant number 095684), Arthritis Research UK (grant number 20385) and the National Institute for Health Research Manchester Musculoskeletal Biomedical Research Unit. The views expressed in this publication are those of the authors and not necessarily those of the UK National Health Service, the National Institute for Health Research or the UK Department of Health.

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Authors and Affiliations

  1. Arthritis Research UK Centre for Genetics and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Stopford Building, Oxford Road, Manchester, M13 9PT, UK

    Stephen Eyre, Gisela Orozco & Jane Worthington

  2. NIHR Manchester Musculoskeletal Biomedical Research Unit, Manchester Academic Health Sciences Centre, Central Manchester Foundation Trust, Grafton Street., Manchester, M13 9NT, UK

    Jane Worthington

Authors
  1. Stephen Eyre
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  2. Gisela Orozco
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  3. Jane Worthington
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Contributions

All authors researched the data for the article, provided substantial contributions to discussions of its content, wrote the article and undertook review and/or editing of the manuscript before submission.

Corresponding author

Correspondence to Jane Worthington.

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The authors declare no competing financial interests.

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Glossary

Single nucleotide polymorphism (SNP)

DNA sequence variation of a single nucleotide that occurs at a specific position in the genome.

Candidate gene

In genetic association studies, the candidate gene approach tests associations between genetic variation within pre-specified genes of interest and phenotypes.

Genome-wide significance threshold

P value threshold that is considered statistically significant after multiple testing correction in GWAS, typically P <5 × 10−8.

Non-synonymous SNPs

SNPs that map to coding genes and result in an amino acid change in the protein they encode.

Statistical power

Probability that a test correctly rejects the null hypothesis.

Risk score

Estimate of the cumulative contribution of genetic factors to a specific outcome of interest in an individual that takes into account the reported risk alleles.

Linkage disequilibrium

Nonrandom association of alleles at different loci in a given population.

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Eyre, S., Orozco, G. & Worthington, J. The genetics revolution in rheumatology: large scale genomic arrays and genetic mapping. Nat Rev Rheumatol 13, 421–432 (2017). https://doi.org/10.1038/nrrheum.2017.80

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