A weighted genetic risk score using all known susceptibility variants to estimate rheumatoid arthritis risk
Pappas, Dimitrios A
Greenberg, Jeffrey D
Eyre, SteveNote: Order does not necessarily reflect citation order of authors.
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CitationYarwood, A., B. Han, S. Raychaudhuri, J. Bowes, M. Lunt, D. A. Pappas, J. Kremer, et al. 2015. “A weighted genetic risk score using all known susceptibility variants to estimate rheumatoid arthritis risk.” Annals of the Rheumatic Diseases 74 (1): 170-176. doi:10.1136/annrheumdis-2013-204133. http://dx.doi.org/10.1136/annrheumdis-2013-204133.
AbstractBackground: 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.
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