Mining large international genetic datasets to identify new therapeutic targets in giant cell arteritis through innovative genetic methodology

挖掘大型国际遗传数据集，通过创新的遗传方法确定巨细胞动脉炎的新治疗靶点

• 批准号: 2278375
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2278375
• 关键词: Mining; large; international; genetic; datasets

Giant cell arteritis (GCA) is the commonest primary systemic vasculitis, occurring exclusively after 50yrs. It is a preventable cause of blindness and stroke. Visual manifestations occur early in 40% and range from transient diplopia to sudden, permanent visual loss. Irreversible ischaemic complications, including blindness, occur in 19% of UK patients, despite prompt treatment. Ongoing vascular inflammation in the extracranial and large vessels leads to late vascular stenoses and has a 17 fold increased incidence of thoracic aortic aneurysms. Most patients commence glucocorticoid monotherapy and there is a high relapse rate with 50% remaining glucocorticoid dependent 2-3 years later. This is in marked contrast with other systemic inflammatory disorders where early immunosuppressive therapy leads to improved patient outcomes and reduced tissue damage. We hypothesise that major pathogenic pathways active in GCA can be identified through the analysis of polygenic risk scores and protein quantitative trait loci (pQTL) derived from relevant immunological, vascular or tissue remodelling datasets. Well-phenotyped GCA cohorts with genome-wide genotypic data, histological data and associated sample collections for proteomic analysis will be used, combined with publicly accessible data of traits related to immune and vascular function and also matrix turnover. Polygenic risk scores will be generated from relevant immunological, cardiovascular and tissue remodelling clinical phenotypes (e.g. from UK Biobank and publicly available datasets. In conjunction with the SCALLOP Consortium (a collaborative international framework for the discovery of pQTLs and novel biomarkers for Olink proteins) pQTLs will be identified for circulating proteins at unprecedented statistical power (980 plasma proteins in ~ 25,000 participants, many of which are implicated in vascular inflammation). The derived polygenic risk scores and pQTLs will be analysed to identify the influence on GCA susceptibility and selected clinical phenotypes (PMR/ischaemic complications). A subset of GCA patients' samples will be analysed for circulating proteins. Specific hypotheses relating protein levels and pQTL to clinical subtype or outcome will be formulated based on the earlier work and tested in this subset. A discovery genome-wide association study of pQTLs in GCA will be conducted, weighted by prior information from the SCALLOP Consortium. There will also be opportunities for the therapeutic targeting of key pathogenic pathways associated with GCA or selected phenotypic subgroups (ischaemic complications, thoracic aortic aneurysms) using computational biology and pharmacological compound profiling tools.

巨细胞动脉炎 (GCA) 是最常见的原发性系统性血管炎，仅发生在 50 岁以后。它是导致失明和中风的可预防原因。视觉表现在 40% 的早期就出现，范围从短暂的复视到突然的、永久性的视力丧失。尽管及时治疗，仍有 19% 的英国患者出现不可逆的缺血性并发症，包括失明。颅外和大血管中持续的血管炎症会导致晚期血管狭窄，并使胸主动脉瘤的发病率增加 17 倍。大多数患者开始糖皮质激素单药治疗，复发率很高，2-3 年后仍有 50% 的患者仍依赖糖皮质激素。这与其他全身性炎症性疾病形成鲜明对比，在其他全身性炎症性疾病中，早期免疫抑制治疗可以改善患者的预后并减少组织损伤。我们假设，通过分析源自相关免疫、血管或组织重塑数据集的多基因风险评分和蛋白质数量性状位点 (pQTL)，可以识别 GCA 中活跃的主要致病途径。将使用具有全基因组基因型数据、组织学数据和用于蛋白质组分析的相关样本集合的表型良好的 GCA 队列，并结合与免疫和血管功能以及基质周转相关的可公开获取的性状数据。多基因风险评分将从相关的免疫学、心血管和组织重塑临床表型（例如来自英国生物银行和公开数据集）中生成。与 SCALLOP Consortium（一个发现 pQTL 和 Olink 蛋白新型生物标志物的国际合作框架）合作，将在以下时间鉴定循环蛋白的 pQTL： 前所未有的统计能力（约 25,000 名参与者的 980 种血浆蛋白，其中许多与血管炎症有关）。将分析得出的多基因风险评分和 pQTL，以确定对 GCA 易感性和选定临床表型（PMR/缺血并发症）的影响。 GCA 患者样本的一个子集将被分析循环蛋白质。具体假设 将根据早期工作制定相关蛋白质水平和 pQTL 与临床亚型或结果的关系，并在此子集中进行测试。将进行 GCA 中 pQTL 的全基因组关联研究，并根据扇贝联盟的先前信息进行加权。也将有机会针对与 GCA 或选定的表型亚组（缺血性并发症、胸部并发症）相关的关键致病途径进行治疗。 主动脉瘤）使用计算生物学和药理学化合物分析工具。