Imaging Genetics for Neuropathology

This project will develop integrative algorithms that use neuroimaging, genetics, and genomics to assess the impact of genetic variations on neurodegenerative diseases, such as Parkinson’s disease (PD) and Alzheimer’s disease (AD). Identification of disease genetic variants is one of the central challenges in neurodegenerative diseases. Traditionally, this is done through genomoe-wide association studies (GWAS) on a large cohort by checking the correlation between a genetic marker and a phenotypic variable, such as the incidence of the disease or a clinical variable like the UPDRS score used in PD. However, some variants may have a weak but cumulative effect that cannot be identified by traditional GWAS analysis due to the limited statistical power of such test on small collections of samples. With the recent availability of large public datasets with genetics, imaging, and clinical information of patients with Parkinson’s disease (PPMI) and Alzhemer’s (ADNI), we now are able to utilize the imaging data and bridge the gap between genetics and disease symptoms via intermediate imaging features or markers. With the aid of imaging data, we will be able to find potential disease mutations or genes and identify putative pathways that correlate with the important imaging markers that are useful for patient stratification. In addition, there is an urgent need for developing computational tools to mine such a dataset (PPMI) to understand and diagnose PD.