Novel Drug Targets for Treating Rheumatoid Arthritis based on Analysis of Human Genetic Data

类风湿性关节炎是一种致残性和疼痛的疾病，可导致患者生活质量的大幅下降，并与过早死亡和巨大的社会经济成本有关。尽管最近在治疗该病方面取得了进展，但仍有几个领域的需求尚未得到满足，包括解决治疗失败或部分缓解的问题。在这个项目中，一种用于分析遗传数据的新的数学方法将应用于从类风湿性关节炎患者产生的数据集。我们相信，与传统方法相比，该分析将识别出更多与疾病相关的基因。这份基因清单将为确定治疗这种疾病的新药靶点提供良好的基础。由于这些药物靶点来自对人类遗传信息的分析，它们更有可能与人类疾病相关，因此在临床开发中获得成功的可能性更大。

Rheumatoid arthritis is a disabling and painful condition that can cause substantial reduction in quality of life to patients, and is associated with early death and significant socioeconomic costs. Despite recent progress in treating the disease, there are several areas of unmet need, including addressing treatment failures or partial response. In this project, a novel mathematical method for analysing genetic data will be applied to a dataset generated from patients with rheumatoid arthritis. We are confident that the analysis will identify significantly more genes associated with the disease than conventional methods. This list of genes will provide an excellent basis for identifying new drug targets to treat this disease. As these drug targets are derived from analysis of human genetic information, they are more likely to be relevant to the human disease, and therefore will have a greater probability of success in clinical development.