Genomic risk scores for juvenile idiopathic arthritis and its subtypes
AuthorCanovas, R; Cobb, J; Brozynska, M; Bowes, J; Li, YR; Smith, SL; Hakonarson, H; Thomson, W; Ellis, JA; Abraham, G; ...
Source TitleAnnals of the Rheumatic Diseases
PublisherBMJ PUBLISHING GROUP
University of Melbourne Author/sAbraham, Gad; Inouye, Michael; Cobb, Joanna; Munro, Jane; Ellis, Justine; Canovas, Rodrigo
Document TypeJournal Article
CitationsCanovas, R., Cobb, J., Brozynska, M., Bowes, J., Li, Y. R., Smith, S. L., Hakonarson, H., Thomson, W., Ellis, J. A., Abraham, G., Munro, J. E. & Inouye, M. (2020). Genomic risk scores for juvenile idiopathic arthritis and its subtypes. ANNALS OF THE RHEUMATIC DISEASES, 79 (12), pp.1572-1579. https://doi.org/10.1136/annrheumdis-2020-217421.
Access StatusOpen Access
Open Access at PMChttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC7677485
NHMRC Grant codeNHMRC/1090462
OBJECTIVES: Juvenile idiopathic arthritis (JIA) is an autoimmune disease and a common cause of chronic disability in children. Diagnosis of JIA is based purely on clinical symptoms, which can be variable, leading to diagnosis and treatment delays. Despite JIA having substantial heritability, the construction of genomic risk scores (GRSs) to aid or expedite diagnosis has not been assessed. Here, we generate GRSs for JIA and its subtypes and evaluate their performance. METHODS: We examined three case/control cohorts (UK, US-based and Australia) with genome-wide single nucleotide polymorphism (SNP) genotypes. We trained GRSs for JIA and its subtypes using lasso-penalised linear models in cross-validation on the UK cohort, and externally tested it in the other cohorts. RESULTS: The JIA GRS alone achieved cross-validated area under the receiver operating characteristic curve (AUC)=0.670 in the UK cohort and externally-validated AUCs of 0.657 and 0.671 in the US-based and Australian cohorts, respectively. In logistic regression of case/control status, the corresponding odds ratios (ORs) per standard deviation (SD) of GRS were 1.831 (1.685 to 1.991) and 2.008 (1.731 to 2.345), and were unattenuated by adjustment for sex or the top 10 genetic principal components. Extending our analysis to JIA subtypes revealed that the enthesitis-related JIA had both the longest time-to-referral and the subtype GRS with the strongest predictive capacity overall across data sets: AUCs 0.82 in UK; 0.84 in Australian; and 0.70 in US-based. The particularly common oligoarthritis JIA also had a GRS that outperformed those for JIA overall, with AUCs of 0.72, 0.74 and 0.77, respectively. CONCLUSIONS: A GRS for JIA has potential to augment clinical JIA diagnosis protocols, prioritising higher-risk individuals for follow-up and treatment. Consistent with JIA heterogeneity, subtype-specific GRSs showed particularly high performance for enthesitis-related and oligoarthritis JIA.
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