There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Jingjing Liang |
| Institution: | Case Western Reserve University |
| Department: | Department of Pathology |
| Country: | |
| Proposed Analysis: | Imaging genetics is the integrated research that uses neuroimaging phenotypes to assess the impact of genetic variation on brain function and structure. Challenges in imaging genetic studies are the high dimensionality, multi-modality and high noise data with relative small sample size in conjunction with increased evidence of polygene and pleiotropy. Multivariate and network approaches, which can accommodate correlated traits, are powerful to identify potential weak genetic effects that bury in high dimensional datasets and are able to construct independent components or modules for both imaging and genetic data analysis. We propose to use the pairwise genetic correlations among multiple neuroimaging traits to build brain imaging genetic correlation network to identify groups of neuroimaging traits with high pleiotropy and further conduct multivariate GWAS to increase power to detect genes associated with traits in each identified ROIs modules. Our study mainly has two directions. One direction is to use the pairwise genetic correlations among volumes of structural MRI regions of interest (ROIs) to build brain imaging genetic correlation network. We plan to perform clustering analysis to identify modules of ROIs with high pleiotropy and further conduct multivariate GWAS to increase power to detect genes associated with brain volume atrophy for ROIs in each identified module. Based on this analysis, we will develop method and analysis pipeline to conduct multivariate genome-wide association analysis of neuroimaging traits via a genetic correlation network modular analysis. We hypothesize that multivariate analysis of genetic correlated brain imaging traits will improve statistical power in genetic association analysis. We will divide brain imaging traits into modules according to the genetic correlations and evaluate the aggregated association evidence between genetic variants and modules through a cross phenotype association (CPASSOC) method. The other direction is to interpret and functional analyze the identified genes from the first direction and other reported Alzheimer’s Disease (AD) associated genes. Studies have identified robust and predictive biomarkers for AD including levels of tau, amyloid-β peptides in cerebrospinal fluid (CSF), selected measures of brain atrophy using MRI and imaging of glucose hypometabolism and amyloid using positron emission tomography (PET), which are all available in ADNI data. We plan to perform association analysis of all identified AD locus with these phenotypes to further classify the identified AD susceptibility genes. We also plan to conduct genetic correlation network analysis of multiple traits for biomarkers measuring AD and MCI associated gross brain atrophy, accumulation of amyloid plaques and intracellular neurofibrillary tangles and the decline in memory and other cognitive functions. |
| Additional Investigators |
