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.
The current knowledge about the genetic underpinnings of the progression of late-onset Alzheimer's disease (AD) is limited due to the lack of computational tools that can relate longitudinal markers of AD to genetic varia- tion. This project will develop novel statistical methods and leverage large-scale longitudinal structural brain magnetic resonance imaging (MRI) scans, neuropsychological assessments and genomic data to systemati- cally investigate the genetic basis of AD progression.
