Developing a Risk-scoring Model for Ankylosing Spondylitis Based on a Combination of HLA-B27, Single-nucleotide Polymorphism, and Copy Number Variant Markers
- Authors
- Jung, Seung-Hyun; Cho, Sung-Min; Yim, Seon-Hee; Kim, So-Hee; Park, Hyeon-Chun; Cho, Mi-La; Shim, Seung-Cheol; Kim, Tae-Hwan; Park, Sung-Hwan; Chung, Yeun-Jun
- Issue Date
- Dec-2016
- Publisher
- J RHEUMATOL PUBL CO
- Keywords
- ANKYLOSING SPONDYLITIS; COPY NUMBER VARIATION; SINGLE-NUCLEOTIDE POLYMORPHISM; HLA-B27; GENETIC RISK SCORING
- Citation
- JOURNAL OF RHEUMATOLOGY, v.43, no.12, pp.2136 - 2141
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF RHEUMATOLOGY
- Volume
- 43
- Number
- 12
- Start Page
- 2136
- End Page
- 2141
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/4872
- DOI
- 10.3899/jrheum.160347
- ISSN
- 0315-162X
- Abstract
- 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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