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
From 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.
S.H. Jung, PhD, Integrated Research Center for Genome Polymorphism, Department of Microbiology, The Catholic University of Korea; S.M. Cho, MS, Integrated Research Center for Genome Polymorphism, Department of Microbiology, The Catholic University of Korea; S.H. Yim, MD, Integrated Research Center for Genome Polymorphism, Department of Microbiology, The Catholic University of Korea; S.H. Kim, MS, Integrated Research Center for Genome Polymorphism, Department of Microbiology, The Catholic University of Korea; H.C. Park, MS, Integrated Research Center for Genome Polymorphism, Department of Microbiology, The Catholic University of Korea; M.L. Cho, PhD, Rheumatism Research Center, Catholic Research Institutes of Medical Science, The Catholic University of Korea; S.C. Shim, MD, PhD, Division of Rheumatology, Daejeon Rheumatoid and Degenerative Arthritis Center, Chungnam National University Hospital; T.H. Kim, MD, PhD, Hanyang University Hospital for Rheumatic Diseases; S.H. Park, MD, PhD, Division of Rheumatology, Department of Internal Medicine, Seoul St. Mary's Hospital; Y.J. Chung, MD, PhD, Integrated Research Center for Genome Polymorphism, Department of Microbiology, The Catholic University of Korea.
J Rheumatol. 2016 Dec;43(12):2136-2141. doi: 10.3899/jrheum.160347. Epub 2016 Oct 1.
To develop a genotype-based ankylosing spondylitis (AS) risk prediction model that is more sensitive and specific than HLA-B27 typing.
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).
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 (r = 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).
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.
开发一种基于基因型的强直性脊柱炎（AS）风险预测模型，该模型比HLA - B27分型更敏感、更具特异性。
为开发AS遗传风险评分（AS - GRS）模型，通过TaqMan检测法对648名个体（285例患者和363名对照）进行了5个拷贝数变异（CNV）、7个单核苷酸多态性（SNP）以及一个HLA - B27标记的检测。使用逻辑回归开发AS - GRS模型，并在一个更大的独立数据集（576例患者和680名对照）中进行验证。
通过逻辑回归，我们构建了由5个遗传成分组成的AS - GRS模型：HLA - B27、3个CNV（1q32.2、13q13.1和16p13.3）以及1个SNP（rs10865331）。模型中所有遗传因素的显著关联在独立验证集中均得到重复。通过曲线下面积衡量，AS - GRS模型的判别能力极佳：在模型构建集中为0.976（95%CI 0.96 - 0.99），在验证集中为0.951（95%CI 0.94 - 0.96）。当敏感性设定超过94%时，AS - GRS模型比仅基于HLA - B27的模型显示出更高的特异性和准确性。当我们根据AS - GRS评分将个体分为四分位数时，4组（低、中 - 1、中 - 2和高风险）的OR值随AS - GRS评分呈上升趋势（r = 0.950），且最高风险组患AS的风险比最低风险组高494倍（95%CI 237.3 - 1029.1）。
我们的AS - GRS可用于在主要症状出现前识别AS高风险个体，这可能通过早期治疗改善他们的预后。
