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Developing a Risk-scoring Model for Ankylosing Spondylitis Based on a Combination of HLA-B27, Single-nucleotide
Seung-Hyun Jung1,2, Sung-Min Cho1,2, Seon-Hee Yim1,2
1From the Integrated Research Center for Genome Polymorphism, Department of Microbiology, The Catholic University of Korea, College of Medicine; Rheumatism Research Center, Catholic Research Institutes of Medical Science, The Catholic University of Korea, College of Medicine; Hanyang University Hospital for Rheumatic Diseases; Division of Rheumatology, Department of Internal Medicine, Seoul St. Mary's Hospital, Seoul; Division of Rheumatology, Daejeon Rheumatoid and Degenerative Arthritis Center, Chungnam National University Hospital, Daejeon, South Korea.
Objective:
To develop a genotype-based ankylosing spondylitis (AS) risk prediction model that is more sensitive and specific than HLA-B27 typing.
Methods:
To develop the AS genetic risk scoring (AS-GRS) model, 648 individuals (285 cases and 363 controls) were examined for 5 copy number variants (CNV), 7 single-nucleotide polymorphisms (SNP), and an HLA-B27 marker by TaqMan assays. The AS-GRS model was developed using logistic regression and validated with a larger independent set (576 cases and 680 controls).
Results:
Through logistic regression, we built the AS-GRS model consisting of 5 genetic components: HLA-B27, 3 CNV (1q32.2, 13q13.1, and 16p13.3), and 1 SNP (rs10865331). All significant associations of genetic factors in the model were replicated in the independent validation set. The discriminative ability of the AS-GRS model measured by the area under the curve was excellent: 0.976 (95% CI 0.96-0.99) in the model construction set and 0.951 (95% CI 0.94-0.96) in the validation set. The AS-GRS model showed higher specificity and accuracy than the HLA-B27-only model when the sensitivity was set to over 94%. When we categorized the individuals into quartiles based on the AS-GRS scores, OR of the 4 groups (low, intermediate-1, intermediate-2, and high risk) showed an increasing trend with the AS-GRS scores (r2 = 0.950) and the highest risk group showed a 494× higher risk of AS than the lowest risk group (95% CI 237.3-1029.1).
Conclusion:
Our AS-GRS could be used to identify individuals at high risk for AS before major symptoms appear, which may improve the prognosis for them through early treatment.
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