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

利用生物标志物遗传学和贝叶斯收缩预测来识别巨细胞动脉炎及其并发症的遗传因素

• 批准号: 2605865
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2605865
• 关键词: Using; genetics; biomarkers; Bayesian; shrinkage

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.

在过去的十年里，在确定许多常见疾病的遗传基础方面取得了重大进展。基因发现主要是由大型联盟推动的，它们结合研究队列进行全基因组关联研究，并增加了统计能力。对于罕见疾病和疾病进展等有用的临床措施，很难或不可能收集大量样本，从而减缓了基因发现的速度。其中一种疾病是巨细胞性动脉炎(GCA)。越来越多的证据指向GCA的遗传易感性，但缺乏对导致疾病风险的遗传变异的强有力的表征[1，2]。GCA是最常见的成人脉管炎。它只发生在50岁以上的人，特征是大血管发炎。脉管炎会导致缺血性并发症，包括不可逆转的视力丧失。尽管接受了及时的治疗，但仍有19%的英国患者出现这种情况。以肌肉疼痛和僵硬为特征的风湿性多发性肌痛(PMR)出现在50%的GCA患者中。大多数患者开始糖皮质激素单一治疗，一旦临床症状减轻，这种治疗就会逐渐减少。然而，GCA和PMR都有很高的复发率，2-3年后仍有50%的糖皮质激素依赖，导致显著的毒性和不良事件。我们将使用最大的GCA队列集合之一来研究GCA的遗传成分，这些队列包含全基因组的基因分型数据和临床表型数据，包括PMR和缺血并发症。为了提高统计能力，我们提出了两种新的方法来缩小假设空间的大小：1.特征工程：将全基因组的遗传数据压缩为相关中间性状的较少数量的遗传替代度量(特定于位点的遗传风险分数)，包括免疫和血管生物标记物和临床特征。2.先进的统计方法：应用贝叶斯稀疏预测模型来了解哪些遗传得分最具信息量。评估对遗传评分(例如，基于生物路径)或GCA患者(例如，基于PMR发生)分组的分级收缩先验分布的可能扩展。最近发表的两篇论文表明，在样本规模相对较小的重要临床领域，拟议的方法比传统的GWAS方法更具优势[3，4]。目的本项目的目的是利用基因分型数据阐明GCA的发病机制和相关的临床表型(PMR/缺血性并发症)，并为后续的翻译研究确定新的治疗靶点。主要目标是：1.确定相关的中间性状(免疫和血管性状、细胞因子、蛋白质水平、基因表达)并计算遗传风险分数。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多个地理信息系统的结果，以及计算遗传分数的功能。应用和扩展贝叶斯稀疏预测模型，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，将识别的分数映射到生物途径，并评估其他途径分子的分数是否也与GCA相关。4.使用化学信息学数据库，如DrugBank，确定识别的分数是否与已知的药物靶标相对应，并评估药物重新定位的可能性。5.通过生成和分析现有生物样本中的蛋白质组或转录组数据或使用其他队列，如英国生物库，评估跟踪新目标的可能性。