Exploring the genetic basis of AD progression

项目总结/摘要 晚发性阿尔茨海默病（AD）具有多变的病程和复杂的遗传基础 可能涉及数百个位点。然而，由于缺乏计算工具， 我们目前对AD进展的遗传基础的了解， 是非常有限的迄今为止，大多数AD的遗传学研究是横断面的，其治疗诊断、认知测量、 或AD的神经影像学生物标志物作为静止。然而，AD的病理生理过程是动态的， 疾病是动态和渐进的，个体以不同的速度经历疾病阶段。现有遗传 纵向AD标记物的研究通常只涉及跨越短时期的少量数据点 这些方法不能完全描述疾病的轨迹，或者在以下情况下采用次优的统计方法： 处理串行测量，这会产生有偏估计并降低统计功效。这个项目 旨在通过分析脑纵向结构，系统研究AD进展的遗传基础 磁共振成像（MRI）扫描，神经心理学评估和基因组数据，从两个大的- 规模独立资助的研究：阿尔茨海默病神经影像学倡议（ADNI）和哈佛 大脑老化研究（HABS）。将制定和实施创新的统计方法， AD的三维基因组数据和纵向标志物（平均约5个时间点/受试者，跨越3+ 年），表现出受试者内的序列依赖性，缺失数据点，脱落和不规则间隔 跨学科的测量。该项目将提供关键的分析工具来模拟认知的轨迹， 随着时间的推移，AD生物标志物的活性下降和变化，并剖析AD进展的遗传基础。亲- 该项目将建立在Ge博士强大的定量背景和神经影像统计学方面的经验基础上， 成像遗传学在获奖期间，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，认知老化和AD，这将帮助他发展成为一个独立的调查员，并推出一个 在成像科学，基因组学，统计学和阿尔茨海默氏症的交叉多学科研究计划 research.

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.