Novel integrative imaging genetics analysis for Alzheimer's disease riskand progression

摘要 我们的首要目标是构建遗传性增强的多模式成像内表型，剖析其 相关的遗传基础，并描述其病理机制和临床特征。 阿尔茨海默病(AD)的易感性和进展通过一套统计上强大的，生物学上的 合理且计算高效的贝叶斯模型。AD是复杂的：其病因根本不清楚，也不可能 没有可用的治疗方法。遗传学在阿尔茨海默病中起主导作用。然而，全基因组关联研究基于 AD易感性只发现了少量独立的遗传因素。与.相比 分类诊断，成像数量性状(Qt)在捕捉疾病病因和诊断方面具有明显优势 它们与基因变异的关联已经产生了一些突出的发现。然而，现有的成像遗传学 研究存在以下问题：1)当前基于图谱的成像特征下的力量不足，2)基因缺失 跨不同大脑过程的基础和SNP-SNP相互作用的潜在作用，以及III)不清楚 遗传学、影像和临床表现之间的病理机制限制了它们对AD的进一步发展 研究。为了克服这些障碍，本项目提出了以下四个目标：1)构建小说 “脑遗传性分离”下的多模式成像内表型预测AD的风险和进展；2)确定 构建的多模式脑生物标志物下的多效性SNPs和SNP-SNP相互作用；3)研究 遗传变异与阿尔茨海默病多峰病理的基本病理机制 成像内表型；4)通过实际数据对所提出的方法进行系统评估 分析和模拟，并开发用户友好的分析管道。我们提出的方法是创新的 在AD生物医学研究的多个方面，包括a)构建新的大脑遗传性 B)整合多模式成像与遗传基础的关联，c)探索风险单核苷酸多态性- 以连贯和可扩展的方式与常见遗传变异一起进行SNP交互作用，d)考虑遗传 在精细作图框架下的多重营养，e)建立个体遗传之间的病理机制 变异和多基因图谱、脑活动和疾病症状，f)将拟议的方法扩展到 与AD进度相关的纵向设置，以及g)开发高效且用户友好的管道 我们的产品。这项建议的成功完成将有助于鉴定新的遗传变异 并通过构建的多模式成像生物标志物，表征它们在AD发病机制中的影响。 开发创新的统计方法和计算工具，并在 ADNI和耶鲁ADRC队列将为理解遗传和神经生物学提供巨大的潜力 机制和推进针对性防治，为AD的发展铺平道路 神经学和精神病学研究通常有益于公共卫生成果。

Abstract Our overarching goal is to construct heritability enhanced multimodal imaging endophenotypes, dissect their associated genetic underpinnings, and characterize the pathological mechanism along with clinical profiles for Alzheimer’s disease (AD) susceptibility and progression through a suite of statistically powerful, biologically plausible and computationally efficient Bayesian models. AD is complex: its etiology is in no way clear and with no available cure. Genetics play a dominate role in AD. However, genome wide association studies based on AD susceptibility have only discovered a small number of independent genetic factors. Compared with categorical diagnoses, imaging quantitative trait (QT) has distinct advantages to capture disease etiology and their association on genetic variants has yielded some prominent finding. However, existing imaging genetics studies suffer with i) inadequate power under current atlas-based imaging traits, ii) oversight of genetic underpinnings across different brain processes and potential role of SNP-SNP interactions, and iii) unclear pathological mechanism among genetics, imaging and clinical profiles, which limit their further advances to AD research. To overcome these barriers, this project proposes the following four aims: 1) construct novel multimodal imaging endophenotypes under “brain heritability parcellation'” for AD risk and progression; 2) identify pleiotropic SNPs and SNP-SNP interactions under constructed multimodal brain biomarkers; 3) investigate the fundamental pathological mechanism between genetic variations and AD pathology mediated by multimodal imaging endophenotypes; and 4) perform systematic evaluation of the proposed methods through real data analyses and simulations, and develop user-friendly analytical pipelines. Our proposed methods are innovative in multiple aspects for and beyond AD biomedical research including a) to construct a novel brain heritability parcellation, b) to integrate multimodal imaging on their association to genetic bases, c) to explore risk SNP- SNP interaction along with common genetic variants in a coherent and scalable way, d) to consider genetic pleiotrophy in a fine mapping framework, e) to establish pathological mechanism among individual’s genetic variants and polygenic profile, brain activities and disease symptoms, f) to extend the proposed methods to longitudinal settings with respect to AD progression, and g) to develop efficient and user friendly pipelines for our products. A successful completion of this proposal will facilitate the identification of novel genetic variants and characterize their impacts on AD pathogenesis, mediated by the constructed multimodal imaging biomarkers. The development of innovative statistical methods and computational tools, and their implementation on the ADNI and Yale ADRC cohort will offer a great potential for understanding the genetic and neurobiological mechanisms of AD and advancing targeted prevention and treatment, paving the way for the development of neurological and psychiatric research in general and benefit public health outcomes.