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Clinical and epidemiological research
Extended report
A weighted genetic risk score using all known susceptibility variants to estimate rheumatoid arthritis risk
  1. Annie Yarwood1,
  2. Buhm Han2,
  3. Soumya Raychaudhuri1,2,
  4. John Bowes1,
  5. Mark Lunt1,
  6. Dimitrios A Pappas3,
  7. Joel Kremer5,
  8. Jeffrey D Greenberg4,
  9. Robert Plenge6,7,8,
  10. Rheumatoid Arthritis Consortium International (RACI),
  11. Jane Worthington1,9,
  12. Anne Barton1,9,
  13. Steve Eyre1
  1. 1Arthritis Research UK Epidemiology Unit, Centre for Musculoskeletal Research, Institute of Inflammation and Repair, The University of Manchester, Manchester, UK
  2. 2Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
  3. 3Division of Rheumatology, Department of Medicine, New York Presbyterian Hospital, College of Physicians and Surgeons, Columbia University, New York, New York, USA
  4. 4Department of Rheumatology, New York University Hospital for Joint Diseases, New York, New York, USA
  5. 5Department of Medicine, Albany Medical College, New York, New York, USA
  6. 6Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
  7. 7Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
  8. 8Medical and Population Genetics Program, Broad Institute, Cambridge, Massachusetts, USA
  9. 9NIHR Manchester Musculoskeletal Biomedical Research Unit, Manchester Academic Health Science Centre, Central Manchester Foundation Trust, Manchester, UK
  1. Correspondence to Dr Steve Eyre, Arthritis Research UK Epidemiology Unit, Stopford Building, The University of Manchester, Oxford Road, Manchester M13 9PT, UK; steve.eyre{at}manchester.ac.uk

Abstract

Background There is currently great interest in the incorporation of genetic susceptibility loci into screening models to identify individuals at high risk of disease. Here, we present the first risk prediction model including all 46 known genetic loci associated with rheumatoid arthritis (RA).

Methods A weighted genetic risk score (wGRS) was created using 45 RA non-human leucocyte antigen (HLA) susceptibility loci, imputed amino acids at HLA-DRB1 (11, 71 and 74), HLA-DPB1 (position 9) HLA-B (position 9) and gender. The wGRS was tested in 11 366 RA cases and 15 489 healthy controls. The risk of developing RA was estimated using logistic regression by dividing the wGRS into quintiles. The ability of the wGRS to discriminate between cases and controls was assessed by receiver operator characteristic analysis and discrimination improvement tests.

Results Individuals in the highest risk group showed significantly increased odds of developing anti-cyclic citrullinated peptide-positive RA compared to the lowest risk group (OR 27.13, 95% CI 23.70 to 31.05). The wGRS was validated in an independent cohort that showed similar results (area under the curve 0.78, OR 18.00, 95% CI 13.67 to 23.71). Comparison of the full wGRS with a wGRS in which HLA amino acids were replaced by a HLA tag single-nucleotide polymorphism showed a significant loss of sensitivity and specificity.

Conclusions Our study suggests that in RA, even when using all known genetic susceptibility variants, prediction performance remains modest; while this is insufficiently accurate for general population screening, it may prove of more use in targeted studies. Our study has also highlighted the importance of including HLA variation in risk prediction models.

  • Rheumatoid Arthritis
  • Gene Polymorphism
  • Autoimmune Diseases

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 3.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/3.0/

https://doi.org/10.1136/annrheumdis-2013-204133

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