类风湿关节炎（RA）是一种全身性自身免疫疾病。东北地区患病率高，治疗不及时将导致较高的致残率。RA的发病和治疗具有很强的个体异质性，目前国际上尚无根治RA的治疗方案。本项目应用流行病学调查的方法，收集辽宁省地区500名RA患病人群的临床病理学因素和用药、预后信息，结合生物信息学和分子生物学手段检测RA患者的个体化用药基因多态性。采用贝叶斯多水平模型分析高维遗传数据，通过构造和新的先验分布，有效估计微小遗传效应和罕见变异效应，并整合复杂交互作用和已有生物学先验知识，构建类风湿关节炎更加精准的疾病预后风险预测模型。本项目的完成将为RA预后风险预测模型的构建提供新的研究思路，也为RA患者合理化个体性用药提供有力的预测工具，具有重要的理论和现实价值。

Rheumatoid arthritis (RA) is a systemic chronic disease characterized by an inflammation of the joints with an autoimmune profile. There is a high prevalence rate at the northeast region of China, and if the treatment is not timely, which may lead to a high rate of disability. The pathogenesis and treatment of RA have a strong individual heterogeneity, at present, there is still no radical cure for RA in the world. Using epidemiological investigation methods, this project will collect clinical pathological factors and treatment and prognostic information of 500 RA population at Liaoning Province, and then detect the gene polymorphisms related to individualized medication by combination with bioinformatics and molecular biology methods. Using Bayesian hierarchical model analysis which is performed by constructing a new priori distribution , the effective estimation of tiny genetic effect and the effect of rare variants, and the integration of complex interaction and existing biological prior knowledge, the more accurate RA prognosis prediction model could be constructed based on high-dimensional clinical and genetic data. The completion of the project will provide a new research ideas for the construction of RA prognostic risk prediction model, but also provide a powerful tool for the rational use of individual drugs in RA patients.