Genetic Contributors to White Matter Microstructure in Aging and Alzheimers Disease

项目摘要 虽然内侧颞叶（MTL）白色物质束微结构的减少被认为是 在衰老和阿尔茨海默病（AD）的纵向认知下降中起着核心作用，目前尚不清楚 遗传因素导致这些减少。本提案的目的是使用MTL白色物质束模板 结合全基因组分析，以确定白色物质束微结构的遗传驱动因素。 该提案将利用几个老化数据集，包括阿尔茨海默病神经成像倡议 （n=525），巴尔的摩老龄化纵向研究（n=295），宗教秩序研究/记忆和老龄化 项目/少数民族衰老研究（n=414），范德比尔特记忆与衰老项目（n=319），威斯康星州 阿尔茨海默病研究中心（n=488）和威斯康星州阿尔茨海默病预防登记处（n=468）， 进行所有分析，共计2，509名参与者。此外，所有结果的验证将使用 数据来自一项成熟的寿命研究（UK Biobank（n= 14，701）），数据来自AD Genetics 财团核心假设是MTL白色物质束微结构由基因驱动， 与髓鞘形成、轴突运输和衰老和AD中的神经炎症相关的通路。基于此 假设，这项建议的主要目的将采取多层次的方法来了解哪些基因和 途径导致MTL白色物质的微结构，通过使用：（1）候选基因的方法，以确定与 AD风险基因与MTL白色物质微结构相关，（2）全基因组方法识别 有助于MTL白色物质微结构并量化与其他MTL基因的遗传重叠的新变体。 性状，以及（3）全基因组方法来确定基因表达如何与MTL白色物质相关 引导微观结构并将信号定位到相关生物通路。补充培训计划将 装备我必要的技能，过渡到一个独立的职业生涯专注于成像遗传学， 强调以下培训目标：（a）扩大计算遗传学的专业知识，（B）获得 对AD的病理生理学和临床表现的实际理解，以及（c）提高我在以下方面的技能 数据协调和大数据分析技术。指导团队由以下领域的专家组成： 这些领域，他们的培训将通过正规课程，跨学科培训， 范德比尔特记忆和阿尔茨海默氏症中心，以及尖端的计算和基因组资源，可在 范德比尔特大学医学中心这些实践和智力资源共同提供了理想的 培训环境，我的主要导师，蒂莫西·霍曼博士，有一个资金充足的实验室，将 为职业过渡提供一切必要的资源。这些资源将使我能够100% 作为一名助理教授，他致力于研究和职业发展。这将确保我 可以在建议的授予期内竞争独立资金（R 01）。

PROJECT SUMMARY While reductions in medial temporal lobe (MTL) white matter tract microstructure have been suggested to have a central role in longitudinal cognitive decline in aging and Alzheimer’s disease (AD), it is unknown what genetic factors drive these reductions. The objective of this proposal is to use MTL white matter tract templates in conjunction with genome-wide analyses to identify the genetic drivers of white matter tract microstructure. This proposal will leverage several aging datasets, including the Alzheimer’s Disease Neuroimaging Initiative (n=525), Baltimore Longitudinal Study of Aging (n=295), Religious Orders Study/Memory and Aging Project/Minority Aging Research Study (n=414), Vanderbilt Memory & Aging Project (n=319), Wisconsin Alzheimer’s Disease Research Center (n=488), and Wisconsin Registry for Alzheimer’s Prevention (n=468) to conduct all analyses, totaling in 2,509 participants. Moreover, validation of all results will be conducted using data from a well-established lifespan study (UK Biobank (n=14,701)) and data from the AD Genetics Consortium. The central hypothesis is that MTL white matter tract microstructure is driven by genes and pathways related to myelination, axonal transport, and neuroinflammation in aging and AD. Based on this hypothesis, the primary aims of this proposal will take a multi-level approach to understand which genes and pathways lead to MTL white matter microstructure by using: (1) a candidate gene approach to determine with AD-risk genes are associated with MTL white matter microstructure, (2) a genome-wide approach to identify novel variants which contribute to MTL white matter microstructure and quantify genetic overlap with other traits, and (3) a genome-wide approach to identify how gene expression is associated with MTL white matter tract microstructure and localize signals to relevant biologic pathways. The complementary training plan will equip me with the skills necessary to transition to an independent career focused on imaging genetics by emphasizing the following training objectives: (a) expand expertise in computational genetics, (b) acquire a practical understanding of the pathophysiology and clinical manifestation of AD, and (c) enhance my skillset in data harmonization and big data analytical techniques. The mentoring team is made up of experts in each of these areas, and their training will be augmented through formal coursework, interdisciplinary training at the Vanderbilt Memory & Alzheimer’s Center, and cutting-edge computational and genomic resources available at the Vanderbilt University Medical Center. Together, these practical and intellectual resources provide the ideal training environment, and my primary mentor, Dr. Timothy Hohman, has a well-funded laboratory which will provide all the necessary resources for career transition. These resources will allow me to dedicate 100% protected effort as an Assistant Professor to focus on research and career development. This will ensure that I can competitively compete for independent funding (R01) over the course of the proposed award period.