MRI-GENetics Interface Exploration_MRI-GENIE_Study

 描述（由申请人提供）：中风的影响是毁灭性的那些谁遭受它，他们的家庭，和整个社会。此外，中风事件本身是 脑血管疾病的总负担只是一个粗略的指标， 残疾在很大程度上取决于脑中发生的伴随变化，例如白色高信号（WMH）和急性脑梗死面积，这可以通过脑MRI可靠地评估。卒中相关脑病变的更详细表征可以提供强有力的生物学相关底物，以检查新的疾病途径和药物靶点，从而改善卒中后结局和二级卒中预防。遗传学是发展中风诊断和治疗的新策略的前沿;然而，中风的遗传发现受到缺乏精确表型表征和统计能力不足的阻碍。具有临床意义的MRI特征，如WMH负荷和弥散加权成像（DWI）上的急性梗死面积，与卒中风险和结局密切相关，也具有高度遗传性。我们建议在来自NINDS卒中遗传学网络（SiGN）的3，385例精确表型化的卒中病例中表征这些新的MRI衍生性状与卒中亚型的关系，然后使用已经生成的基因型数据鉴定这些性状的遗传决定因素。MRI数据将使用一种新型的多模式高通量基于图像的分析管道获得，然后详细描述卒中表型、其与卒中后结局的相关性及其潜在的遗传结构。该提案利用了正在进行的大规模、多中心、NIH资助的卒中神经学家、遗传学家和神经影像学分析师社区内的合作，他们提供了广泛的专业知识和独特的贡献，以确保该提案的可行性和科学严谨性。本研究的成功执行将为临床相关的脑血管MRI表型分析创建一个管道，该管道将提供给更广泛的研究社区，并将加快遗传发现的步伐，并推进卒中风险和结局预测的临床应用的开发。

 DESCRIPTION (provided by applicant): The impact of stroke is devastating to those who suffer from it, their families, and the society as a whole. Furthermore, the stroke event itself is only a crude indicator of the total burden of cerebrovascular disease, and the overall functionally disability depends largely on the accompanying changes that occur in the brain, such as white matter hyperintensity (WMH) and acute cerebral infarct size, which can be assessed reliably by brain MRI. A more detailed characterization of stroke-related brain lesions can provide a powerful and biologically relevant substrate to examine novel disease pathways and drug targets for improving post-stroke outcomes and secondary stroke prevention. Genetics are at the cutting-edge of these novel strategies for developing stroke diagnostics and therapeutics; however, genetic discovery in stroke has been hindered by the lack of precise phenotype characterization and insufficient statistical power. Clinically meaningful MRI traits, such as WMH burden and acute infarct size on diffusion-weighted imaging (DWI), which are strongly related to stroke risk and outcomes, are also highly heritable. We propose to characterize the relation of these novel MRI- derived traits to stroke subtypes in 3,385 exquisitely phenotyped stroke cases from the NINDS Stroke Genetics Network (SiGN), and then to identify genetic determinants of these traits using already generated genotype data. The MRI data will be obtained using a novel, multimodal high-throughput image-based analysis pipeline, followed by detailed characterization of the stroke phenotypes, their association with post-stroke outcomes, and their underlying genetic architecture. This proposal takes advantage of the ongoing large-scale, multi- center, NIH-funded collaboration within the community of stroke neurologists, geneticists, and neuroimaging analysts, who provide a broad spectrum of expertise and unique contributions to ensure feasibility and scientific rigor of this proposal. Successful execution of this study will create a pipeline for the clinically relevant cerebrovascular MRI phenotype analysis that will be made available to the broader research community and that will accelerate the pace of genetic discoveries and advance the development of clinical applications in risk and outcome prediction in stroke.