Elucidating the etiology of cryptogenic stroke with human genetics

用人类遗传学阐明隐源性中风的病因

• 批准号: 466957018
• 负责人: Dr. Marios Georgakis, Ph.D.
• 金额: --
• 依托单位: Institut für Schlaganfall- und Demenzforschung (ISD)
• 依托单位国家: 德国
• 项目类别: WBP Fellowship
• 财政年份: 2021
• 资助国家: 德国
• 起止时间: 2020-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/466957018?language=en
• 关键词: Elucidating; etiology; cryptogenic; stroke; human

Ischemic stroke is a major cause of disability and mortality. Following occurrence of an ischemic stroke event, efforts are focused on identifying the underlying etiology so as to apply targeted secondary prevention. Despite the advancements in diagnosis, the etiology of ischemic stroke remains unknown in up to one third of the events. Clinical trials have shown no benefits of treatments beyond aspirin for secondary prevention in patients with cryptogenic stroke. Thus, these patients remain without a specific prophylactic treatment, although they carry a similar risk of recurrence as patients with other stroke subtypes. Observational studies have proposed several mechanisms as potential explanations for cryptogenic stroke including occult non-stenotic atherosclerotic lesions and paroxysmal atrial fibrillation. Still, such studies are biased because of confounding and reverse causation. Human genetics provide a window for a deeper exploration of the etiology of cryptogenic stroke. Specifically, combining multiple sources of genomic data enables the exploration of correlations between two or more phenotypes at the genetic level, the discovery of novel genetic loci involved in the pathophysiology of multiple diseases, the assessment of causal associations between risk factors and disease outcomes, and the identification of novel drug targets. In previous studies we applied these methodologies to inform the etiology of stroke subtypes including large artery, cardioembolic, and small vessel stroke. The current application aims at providing novel insights into the etiology of cryptogenic stroke by leveraging large-scale human genetic data. Specifically, I will explore (i) the genetic overlap of cryptogenic stroke with other ischemic stroke subtypes; (ii) genes commonly predisposing to cryptogenic stroke and other ischemic stroke subtypes; (iii) the associations of genetic predisposition to carotid atherosclerosis, atrial fibrillation, and cerebral small vessel disease with cryptogenic stroke; (iv) the associations of conventional vascular risk factors with cryptogenic stroke; and (v) the effects of genetic proxies for anticoagulant drug targets on cryptogenic stroke. To achieve these aims, I will use human genetic data from 16,851 cases of ischemic stroke and 32,473 stroke-free controls in the SiGN project (Boston, USA) and combine them with novel data from 3,759 ischemic stroke cases from the Munich Stroke Cohort to maximize power. Several different computational methods will be used including GWAS analyses, LD regression analyses, polygenic risk scores, pairwise GWASs, and Mendelian randomization. This project with enhance our understanding of cryptogenic stroke by generating new hypotheses about its etiology and will inform the design of future trials testing the efficacy of new diagnostic approaches for identifying the cause of cryptogenic stroke, as well as trials testing secondary preventive approaches in patients with cryptogenic stroke.

缺血性中风是导致残疾和死亡的主要原因。缺血性中风事件发生后，重点是查明潜在病因，以便有针对性地进行二级预防。尽管诊断方面取得了进展，但多达三分之一的缺血性中风事件的病因仍然未知。临床试验表明，除了阿司匹林以外的治疗对于隐源性中风患者的二级预防没有任何益处。因此，这些患者仍然没有接受特定的预防性治疗，尽管他们与其他中风亚型患者具有相似的复发风险。观察性研究提出了多种机制作为隐源性中风的潜在解释，包括隐匿性非狭窄动脉粥样硬化病变和阵发性心房颤动。尽管如此，此类研究由于混杂和反向因果关系而存在偏见。人类遗传学为更深入探索隐源性中风的病因学提供了一个窗口。具体来说，结合多个来源的基因组数据可以在基因水平上探索两个或多个表型之间的相关性，发现涉及多种疾病病理生理学的新基因位点，评估危险因素和疾病结果之间的因果关系，以及识别新的药物靶点。在之前的研究中，我们应用这些方法来了解中风亚型的病因，包括大动脉、心源性卒中和小血管中风。当前的应用旨在通过利用大规模人类遗传数据，为隐源性中风的病因学提供新的见解。具体来说，我将探讨（i）隐源性中风与其他缺血性中风亚型的遗传重叠； (ii) 通常易患隐源性中风和其他缺血性中风亚型的基因； (iii) 颈动脉粥样硬化、心房颤动和脑小血管疾病的遗传倾向与隐源性中风的关联； (iv) 传统血管危险因素与隐源性中风的关联； (v) 抗凝药物靶标的遗传代理对隐源性中风的影响。为了实现这些目标，我将使用 SiGN 项目（美国波士顿）中 16,851 例缺血性中风病例和 32,473 例无中风对照的人类遗传数据，并将它们与来自慕尼黑中风队列的 3,759 例缺血性中风病例的新数据相结合，以最大限度地发挥功效。将使用几种不同的计算方法，包括 GWAS 分析、LD 回归分析、多基因风险评分、成对 GWAS 和孟德尔随机化。该项目通过产生关于隐源性中风病因的新假设来增强我们对隐源性中风的理解，并将为未来试验设计提供信息，这些试验测试用于确定隐源性中风病因的新诊断方法的有效性，以及测试隐源性中风患者二级预防方法的试验。