Exploring the genetic basis of AD progression

探索 AD 进展的遗传基础

• 批准号: 10058453
• 负责人: Tian Ge
• 金额: $ 24.41万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2022-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10058453
• 关键词: Alzheimer' s Disease; Alzheimer’s disease biomarker; Award; Brain; Cognitive; Cognitive aging; Complex; Data; Dementia; Dependence; Diagnosis; Disease; Disease Marker; Disease Progression; Drops; Elderly; Exhibits; Funding; Genetic; Genetic Markers; Genetic Risk; Genetic Variation; Genetic study; Genomics; Heritability; Image; Impaired cognition; Individual; Interdisciplinary Study; Knowledge; Late Onset Alzheimer Disease; Longitudinal Studies; MRI Scans; Magnetic Resonance Imaging; Measurement; Memory; Methods; Modeling; Names; Neuropsychology; Phase; Phenotype; Process; Publishing; Research; Research Personnel; Science; Speed; Statistical Methods; Structure; Testing; Time; Training; Variant; aging brain; base; case control; cognitive change; cohort; computerized tools; data modeling; executive function; experience; genome wide association study; genome-wide; genomic data; genomic locus; high dimensionality; image processing; imaging biomarker; imaging genetics; innovation; insight; longitudinal analysis; longitudinal dataset; multiple datasets; neuroimaging; neuroimaging marker; novel; pre-clinical; programs; rate of change; risk variant; serial imaging; statistics; tool; user friendly software

PROJECT SUMMARY / ABSTRACT Late-onset Alzheimer's disease (AD) is known to have a variable temporal course and a complex genetic basis that likely involves hundreds of loci. However, due to the lack of computational tools that can relate longitudinal markers of AD to genetic variation, our current knowledge about the genetic underpinnings of AD progression is very limited. To date, most genetic studies of AD are cross-sectional, which treat diagnosis, cognitive meas- urements, or neuroimaging biomarkers of AD as stationary. Yet, the pathophysiological process of AD is dy- namic and progressive, and individuals advance through disease stages at variable speeds. Existing genetic studies of longitudinal AD markers often involve only a smaller number of data points spanning a short period of time, which do not fully characterize the disease trajectory, or employ suboptimal statistical methods when handling serial measurements, which produces biased estimates and reduces statistical power. This project aims to systematically investigate the genetic basis of AD progression by analyzing longitudinal structural brain magnetic resonance imaging (MRI) scans, neuropsychological assessments and genomic data from two large- scale independently funded studies: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Harvard Aging Brain Study (HABS). Innovative statistical methods will be developed and implemented to integrate high- dimensional genomic data and longitudinal markers of AD (on average ~5 time points per subject spanning 3+ years) that exhibit serial dependence within subjects, missing data points, drop-outs and irregularly spaced measurements across subjects. This project will offer critical analysis tools to model the trajectories of cogni- tive decline and change in AD biomarkers over time, and dissect the genetic basis of AD progression. The pro- ject will build on Dr. Ge's strong quantitative background and prior experience in neuroimaging statistics and imaging genetics. During the award period, Dr. Ge will receive formal training in statistical genetics and ge- nomics, and cognitive aging and AD, which will help him develop into an independent investigator and launch a multidisciplinary research program at the intersection of imaging sciences, genomics, statistics and Alzheimer's research.

项目概要/摘要 众所周知，迟发性阿尔茨海默病 (AD) 具有可变的时间过程和复杂的遗传基础 这可能涉及数百个基因座。然而，由于缺乏可以关联纵向的计算工具 AD 标记与遗传变异，我们目前对 AD 进展遗传基础的了解 是非常有限的。迄今为止，大多数 AD 遗传学研究都是横断面的，涉及诊断、认知测量 AD 的尿酸盐或神经影像生物标志物为固定的。然而，AD 的病理生理过程是动态的。 神经性的和进展性的，个体以不同的速度经历疾病阶段。现有遗传 纵向 AD 标记的研究通常只涉及短时间内的少量数据点 时间，不能完全表征疾病轨迹，或者在以下情况下采用次优统计方法： 处理串行测量，这会产生有偏差的估计并降低统计功效。这个项目 旨在通过分析大脑的纵向结构来系统地研究 AD 进展的遗传基础 磁共振成像（MRI）扫描、神经心理学评估和来自两个大型研究机构的基因组数据 规模独立资助的研究：阿尔茨海默病神经影像计划 (ADNI) 和哈佛大学 大脑老化研究（HABS）。将开发和实施创新的统计方法，以整合高 AD 的维度基因组数据和纵向标记（每个受试者平均约 5 个时间点，跨越 3 个以上 年），表现出受试者内部的序列依赖性、数据点缺失、退出和间隔不规则 跨学科的测量。该项目将提供关键分析工具来模拟认知轨迹 随着时间的推移，AD 生物标志物的下降和变化，并剖析 AD 进展的遗传基础。亲 该项目将建立在葛博士强大的定量背景和神经影像统计方面的先前经验的基础上 影像遗传学。获奖期间，葛博士将接受统计遗传学和遗传学方面的正式培训 经济学、认知衰老和AD，这将帮助他发展成为一名独立研究者并发起一项研究 影像科学、基因组学、统计学和阿尔茨海默病交叉领域的多学科研究项目 研究。