Elucidating the etiology of cryptogenic stroke with human genetics

缺血性中风是致残和死亡的主要原因。在发生缺血性卒中事件后，工作重点是确定潜在的病因，以便应用有针对性的二级预防。尽管在诊断方面取得了进步，但在多达三分之一的事件中，缺血性中风的病因仍不清楚。临床试验表明，除了阿司匹林外，对于隐源性中风患者的二级预防，没有其他治疗方法的好处。因此，这些患者仍然没有特殊的预防性治疗，尽管他们与其他中风亚型患者具有相似的复发风险。观察性研究已经提出了几种可能的机制来解释隐源性卒中，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，由于混淆和反向因果关系，这类研究是有偏见的。人类遗传学为深入探索隐源性卒中的病因提供了一个窗口。具体地说，结合多个基因组数据来源，能够在遗传水平上探索两个或更多表型之间的相关性，发现与多种疾病的病理生理学有关的新的遗传位点，评估风险因素与疾病结果之间的因果关系，以及确定新的药物靶标。在以前的研究中，我们应用这些方法来告知卒中亚型的病因，包括大动脉、心源性栓子和小血管卒中。目前的应用旨在通过利用大规模的人类基因数据，为隐源性中风的病因学提供新的见解。具体地说，我将探讨(I)隐源性卒中与其他缺血性卒中亚型的遗传重叠；(Ii)隐源性卒中和其他缺血性卒中亚型的常见易感基因；(Iii)颈动脉粥样硬化、心房颤动和脑小血管疾病的遗传易感性与隐源性卒中的关联；(Iv)常规血管危险因素与隐源性卒中的关联；以及(V)抗凝药物靶标的遗传替代物对隐源性卒中的影响。为了实现这些目标，我将使用SIGN项目(美国波士顿)中16,851例缺血性中风患者和32,473例非中风对照组的人类基因数据，并将它们与慕尼黑中风队列中3,759例缺血性中风患者的新数据结合起来，以最大限度地提高功率。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。该项目将通过产生关于其病因的新假说来加强我们对隐源性中风的理解，并将为未来试验的设计提供信息，以测试新诊断方法的有效性以确定隐源性中风的原因，以及测试隐源性中风患者的二级预防方法的试验。

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.