Using genetics of biomarkers and Bayesian shrinkage prediction to identify genetic factors of giant cell arteritis and its complications

在过去十年中，在确定许多常见疾病的遗传基础方面取得了重大进展。遗传发现主要是由大型联合体联合研究队列来推动的，以提高统计能力来进行全基因组关联研究。对于罕见疾病和有用的临床指标，如疾病进展，大样本量很难或不可能收集，减缓了遗传发现的速度。这些疾病之一是巨细胞动脉炎（GCA）。越来越多的证据表明GCA具有遗传易感性，但缺乏对导致疾病风险的遗传变异的强有力表征[1，2]。GCA是最常见的成人血管炎。它只发生在50岁以上的人群中，其特征是大血管炎症。血管炎导致缺血性并发症，包括不可逆的视力丧失。这些发生在19%的英国患者，尽管及时治疗。风湿性多肌痛（PMR），其特征是肌肉疼痛和僵硬，存在于50%的GCA患者中。大多数患者开始糖皮质激素单药治疗，一旦临床症状减轻，则逐渐减少。然而，GCA和PMR都具有高复发率，其中50%在2-3年后仍然依赖糖皮质激素，导致显著的毒性和不良事件。我们将使用最大的GCA队列集合之一研究GCA的遗传成分，其中包含全基因组基因型数据和临床表型数据，包括PMR和缺血并发症。为了增加统计功效，我们建议以两种新颖的方式减少假设空间的大小1。功能工程：将全基因组遗传数据分解为较少数量的遗传代理指标（基因座特异性遗传风险评分），用于相关中间性状，包括免疫和血管生物标志物和临床性状。2.高级统计方法：应用贝叶斯稀疏预测模型来了解哪些遗传分数信息量最大。评价对遗传评分（例如基于生物学途径）或GCA患者（例如基于PMR发生率）分组的分层收缩先验分布的可能扩展。最近的两篇出版物证明了在样本量相对较小的重要临床领域中，所提出的方法优于传统GWAS [3，4]。本项目的目的是使用基因型数据来阐明GCA的发病机制和相关的临床表型（PMR/缺血性并发症），并为后续的转化研究确定新的治疗靶点。主要目标是：1.识别相关的中间性状（免疫和血管性状、细胞因子、蛋白质水平、基因表达）并计算遗传风险评分。我们的GENOSCORES平台提供了一个精选的数据库，其中包含来自20，000多个GWAS的结果，以及计算遗传评分的功能。2.应用并扩展贝叶斯稀疏预测模型，以评估候选遗传评分对GCA易感性（病例/对照分析）和缺血性并发症（生存分析）的影响。3.使用生物信息学数据库，如Reactome，将识别的评分映射到生物学途径，并评估其他途径分子的评分是否也与GCA相关。4.使用化学信息学数据库，如DrugBank，以确定确定的分数是否对应于已知的药物靶点，并评估药物重新定位的可能性。5.通过生成和分析可用生物样本中的蛋白质组学或转录组学数据或使用其他队列（如英国生物样本库），评估跟踪新靶点的可能性。

Over the last decade, significant progress has been made in characterising the genetic basis of many common diseases. Genetic discovery has been mainly driven by large consortia combining study cohorts to perform genome wide association studies with increased statistical power. For rare diseases and for useful clinical measures such as disease progression, large sample sizes are difficult or impossible to collect, slowing the rate of genetic discoveries. One of these diseases is giant cell arteritis (GCA). Accumulating evidence points to a genetic predisposition for GCA but lacks robust characterization of the genetic variants contributing to disease risk [1, 2]. GCA is the most common adulthood vasculitis. It occurs exclusively in people over 50 years of age and is characterised by inflammation of large blood vessels. Vasculitis leads to ischaemic complications, including irreversible vision loss. These occur in 19% of UK patients, despite prompt treatment. Polymyalgia rheumatica (PMR), which is characterised by muscle pain and stiffness, is present in 50% of GCA patients. Most patients commence glucocorticoid monotherapy, which is gradually reduced once clinical symptoms have abated. However, both GCA and PMR have a high relapse rate, with 50% remaining glucocorticoid dependent 2-3 years later, leading to significant toxicity and adverse events. We will study the genetic component of GCA using one of the largest collections of GCA cohorts with genome-wide genotypic data and clinical phenotype data, including PMR and ischemic complications. To increase statistical power, we propose to reduce the size of the hypothesis space in two novel ways 1. Feature engineering: Collapse the genome-wide genetic data into a smaller number of genetic proxy measures (locus-specific genetic risk scores) for relevant intermediate traits, including immune and vascular biomarkers and clinical traits. 2. Advanced statistical methods: Apply Bayesian sparse prediction models to learn which genetic scores are most informative. Evaluate possible extensions to the hierarchical shrinkage prior distribution that group genetic scores (e.g. based on biological pathways) or GCA patients (e.g. based on PMR occurrence). Two recent publications demonstrate the strength of the proposed methodology over conventional GWAS in important clinical areas, where sample sizes are relatively small [3, 4]. Aims The aim of this project is to use genotypic data to elucidate the pathogenesis of GCA and related clinical phenotypes (PMR/ischaemic complications) and to identify new treatment targets for subsequent translational research. The key objectives are: 1. Identify relevant intermediate traits (immune and vascular traits, cytokines, protein levels, gene expressions) and compute genetic risk scores. A curated database with results from over 20,000 GWAS, as well as functionality for computing genetic scores, is available through our GENOSCORES platform.2. Apply and extend Bayesian sparse prediction models to evaluate the effect of candidate genetic scores on GCA susceptibility (case/control analysis) and ischaemic complications (survival analysis). 3. Use bioinformatics databases, such as Reactome, to map identified scores to biological pathways, and assess if scores for other pathway molecules are also associated with GCA. 4. Use cheminformatics databases, such as DrugBank, to establish if identified scores correspond to known drug targets and evaluate the potential for drug repositioning. 5. Evaluate the possibility of following up novel targets by generating and analysing proteomic or transcriptomic data in available biosamples or using additional cohorts, such as the UK Biobank.