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Abstract
Objectives: Juvenile idiopathic arthritis (JIA)-associated uveitis (JIAU) is a serious JIA comorbidity that can result in vision impairment. This study aimed to identify genetic risk factors, within the major histocompatibility complex , for JIAU and evaluate their contribution for improving risk classification when combined with clinical risk factors.
Methods: Data on single nucleotide polymorphisms, amino acids and classical human leukocyte antigen (HLA) alleles were available for 2,497 JIA patients without uveitis and 579 JIAU patients (female=2060, male=1015). Analysis was restricted to patients with inferred European ancestry. Forward conditional logistic regression identified genetic markers exceeding a Bonferroni corrected significance (6x10-6). Multivariable logistic regression estimated the effects of clinical and genetic risk factors and a likelihood ratio test calculated the improvement in model fit when adding genetic factors. Uveitis risk classification performance of a model integrating genetic and clinical risk factors was estimated using area under the receiver operator characteristic curve and compared to a model of clinical risk factors alone.
Results: Three genetic risk factors were identified mapping to HLA-DRB1, HLA-DPB1 and HLA-A. These markers were statistically independent from clinical risk factors and significantly improved the fit of a model when included with clinical risk factors (P = 3.3x10-23). The addition of genetic markers improved the classification of JIAU compared to a model of clinical risk factors alone (AUC 0.75 vs. 0.71).
Conclusions: Integration of a genetic and clinical risk prediction model outperforms a model based solely on clinical risk factors. Future JIAU risk prediction models should include genetic risk factors.
Methods: Data on single nucleotide polymorphisms, amino acids and classical human leukocyte antigen (HLA) alleles were available for 2,497 JIA patients without uveitis and 579 JIAU patients (female=2060, male=1015). Analysis was restricted to patients with inferred European ancestry. Forward conditional logistic regression identified genetic markers exceeding a Bonferroni corrected significance (6x10-6). Multivariable logistic regression estimated the effects of clinical and genetic risk factors and a likelihood ratio test calculated the improvement in model fit when adding genetic factors. Uveitis risk classification performance of a model integrating genetic and clinical risk factors was estimated using area under the receiver operator characteristic curve and compared to a model of clinical risk factors alone.
Results: Three genetic risk factors were identified mapping to HLA-DRB1, HLA-DPB1 and HLA-A. These markers were statistically independent from clinical risk factors and significantly improved the fit of a model when included with clinical risk factors (P = 3.3x10-23). The addition of genetic markers improved the classification of JIAU compared to a model of clinical risk factors alone (AUC 0.75 vs. 0.71).
Conclusions: Integration of a genetic and clinical risk prediction model outperforms a model based solely on clinical risk factors. Future JIAU risk prediction models should include genetic risk factors.
Original language | English |
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Journal | Arthritis and Rheumatology |
Publication status | Accepted/In press - 12 Jul 2024 |
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BCRD/BSPAR: UK JIA Biologics Registers: the BCRD and BSPAR Etanercept Studies
Hyrich, K. (PI), Mowbray, K. (Support team), Kearsley-Fleet, L. (Researcher), Sutton, E. (Support team) & Watson, K. (Support team)
Project: Research
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Centre for Epidemiology Versus Arthritis.
Dixon, W. (PI), Bruce, I. (CoI), Felson, D. (CoI), Hyrich, K. (CoI), Lunt, M. (CoI), Mcbeth, J. (CoI), Mcdonagh, J. (CoI), O'Neill, T. (CoI), Sergeant, J. (CoI), Verstappen, S. (CoI) & Serafimova, I. (Support team)
1/08/18 → 14/03/24
Project: Research