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

针对阿尔茨海默病风险和进展的新型综合成像遗传学分析

• 批准号: 10210551
• 负责人: Yize Zhao
• 金额: $ 187.49万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10210551
• 关键词: Address; Affect; Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer' s disease risk; Alzheimer’s disease biomarker; American; Atlases; Base of the Brain; Bayesian Modeling; Behavior; Biological; Biological Markers; Biomedical Research; Brain; Brain imaging; Categories; Clinical; Complex; Data Analyses; Development; Diagnosis; Disease; Disease Progression; Disease model; Disease susceptibility; Dissection; Etiology; Evaluation; Genetic; Genetic Diseases; Genetic Markers; Genetic Transcription; Genetic Variation; Genetic study; Goals; Heritability; Image; Individual; Investigation; Link; Mediating; Mediation; Methods; Multimodal Imaging; Neurologic; Outcome; Pathogenesis; Pathologic; Pathway interactions; Play; Prevention; Process; Public Health; Research; Risk; Role; Signal Transduction; Statistical Methods; Symptoms; analytical method; base; biomarker discovery; biomarker identification; clinical Diagnosis; cohort; computerized tools; drug development; endophenotype; genetic analysis; genetic association; genetic variant; genome wide association study; imaging biomarker; imaging genetics; imaging modality; innovation; insight; model development; multimodality; nervous system disorder; neurobiological mechanism; neuroimaging; novel; pleiotropism; quantitative imaging; simulation; trait; user-friendly

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.

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，剖析它们的 相关的遗传基础，并表征病理机制沿着临床概况， 阿尔茨海默病（AD）的易感性和进展，通过一套统计学上强大的，生物学上 合理且计算效率高的贝叶斯模型。AD是复杂的：其病因不清楚， 没有有效的治疗方法。遗传因素在AD发病中起主导作用。然而，全基因组关联研究基于 AD的易感性目前只发现了少数独立的遗传因素。较 在分类诊断中，成像定量性状（QT）在捕获疾病病因和 它们与遗传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 研究遭受i）在当前基于图谱的成像特征下的不足的功率，ii）遗传学的疏忽， 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、影像学和临床特征之间的病理机制，限制了它们在AD中的进一步发展 research.为了克服这些障碍，本项目提出了以下四个目标：1）构建新颖的 根据“脑遗传性分组”进行AD风险和进展的多模式成像内表型; 2）识别 在构建的多模式脑生物标志物下的多效性SNP和SNP-SNP相互作用; 3）研究 遗传变异与AD病理之间的基本病理机制 通过真实的数据对所提出的方法进行系统评价 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）构建一种新的大脑遗传性 包裹，B）整合多模态成像与遗传基础的关联，c）探索风险SNP- SNP相互作用沿着以连贯和可扩展的方式与常见的遗传变异，d）考虑遗传 e）在个体遗传结构中建立病理机制， f）将所提出的方法扩展到 关于AD进展的纵向设置，以及g）开发高效和用户友好的管道， 我们的产品.该提案的成功完成将有助于识别新的遗传变异 并通过构建的多模态成像生物标志物来表征它们对AD发病机制的影响。 开发创新的统计方法和计算工具，并在 ADNI和耶鲁ADRC队列将为了解遗传和神经生物学提供巨大潜力 研究AD的机制，推进有针对性的预防和治疗，为发展 神经学和精神病学的研究，并有利于公共卫生的结果。

项目成果

Consistency of Graph Theoretical Measurements of Alzheimer's Disease Fiber Density Connectomes Across Multiple Parcellation Scales.

阿尔茨海默病纤维密度连接体在多个分区尺度上的图论测量的一致性。

• DOI: 10.1109/bibm55620.2022.9995657
• 发表时间: 2022
• 期刊: Proceedings. IEEE International Conference on Bioinformatics and Biomedicine
• 作者: Xu,Frederick;Garai,Sumita;Duong-Tran,Duy;Saykin,AndrewJ;Zhao,Yize;Shen,Li;ADNI
• 通讯作者: ADNI