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

巨细胞动脉炎(GCA)是最常见的原发系统性脉管炎，仅发生在50年后。它是失明和中风的一个可预防的原因。视觉表现出现在40%的早期，范围从一过性复视到突发性、永久性视力丧失。不可逆转的缺血并发症，包括失明，在19%的英国患者中发生，尽管及时治疗。颅外和大血管持续的血管炎症导致晚期血管狭窄，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一治疗，复发率很高，2-3年后仍有50%的糖皮质激素依赖。这与其他全身性炎症性疾病形成鲜明对比，在这些疾病中，早期免疫抑制治疗可以改善患者预后，减少组织损伤。我们假设，GCA中活跃的主要致病途径可以通过分析来自相关免疫学、血管或组织重构数据集的多基因风险分数和蛋白质数量性状基因座(PQTL)来识别。将使用表型良好的GCA队列，包括全基因组的基因数据、组织学数据和用于蛋白质组分析的相关样本集合，并结合与免疫和血管功能相关的特征以及基质周转的公开可访问数据。多基因风险评分将根据相关的免疫学、心血管和组织重塑临床表型(例如，来自UK Biobank和公开可用的数据集)生成。与扇贝联合会(一个发现pQTL和Olink蛋白新生物标记物的合作国际框架)一起，将以前所未有的统计能力为循环蛋白确定pQTL(约25,000名参与者中的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易感性和选定的临床表型(PMR/缺血性并发症)的影响。将对GCA患者的一部分样本进行循环蛋白分析。关于蛋白质水平和pQTL与临床亚型或结果的具体假设将在早期工作的基础上制定，并在这个子集中进行测试。根据扇贝联盟的先验信息，将进行一项发现GCA中pQTL的全基因组关联研究。还将有机会利用计算生物学和药物化合物图谱工具，针对与GCA或选定的表型亚群(缺血并发症、胸主动脉瘤)相关的关键致病途径进行治疗。

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.