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: | Carlos Cruchaga |
| Institution: | Washington University School of Medicine |
| Department: | Psychiatry |
| Country: | |
| Proposed Analysis: | Identification of novel genes and variants associated with Alzheimer’s disease and other complex traits by using CSF and plasma analytes levels as quantitative traits Genome-wide association studies (GWAS) for Alzheimer’s Disease (AD) have identified common variants associated with risk for LOAD in 20 different loci. The genetic effect of these loci is approximately 50%, indicating that there are still more genes to be discovered. Additionally variants affecting disease onset, progression, or duration may not be identified by these studies. There is a clear need to develop and apply additional approaches if we are to solve the complete genetic architecture of AD. One alternative approach is to use intermediate quantitative traits as endophenotypes for genetic studies. Our previous research has successfully used cerebrospinal fluid (CSF) levels of APOE, tau, and Aβ to identify novel variants associated with the risk for AD as well as disease progression, tangle pathology and memory decline. The benefits of using endophenotypes like these to find variants associated with disease include the fact that endophenotypes are by definition causative, affected directly by genetic variation, and have greater statistical power. Here, we propose to use the ADNI CSF and plasma RBM analytes, as well the GWAS and whole-genome data to identify novel variants and genes associated with those protein levels. We also have RBM and GWAS data in CSF and plasma for 300 individuals from the Knight-ADRC. We will combine the ADNI and the Knight-ADRC data to increase the power of the study. Then, we will analyze whether the loci or variants associated with protein levels are also implicated on disease. We will prioritize proteins that are already known to be implicated in disease. As an example there are several CSF potential novel biomarkers for AD: complement 3 (C3), neuronal cell adhesion molecule (NrCAM), vascular endothelial growth factor (VEGF), clusterin (CLU), angiotension-converting enzyme (ACE), and visinin-like protein 1 (VILIP-1). APOE has been confirmed to be a potential endophenotype for AD. One study found that ACE levels in the CSF were reduced in AD1. In another study, researchers found that levels of VILIP-1 in CSF can predict rates of cognitive decline. Similarly, some plasma analytes may be important for stroke: Baseline matrix metalloproteinase-9 (MMP-9) plasma levels were found to be an independent predictor of hemorrhagic transformation after treatment with tPA—with an odds ratio (OR) of 9.62. Plasma C-reactive protein (CRP) levels proved to be a significant and independent predictor of mortality following tPA use and IL-6 levels predicted neurological deterioration after stroke. Similarly other analytes including in the RBM panel have been implicated in cancer and other immunologic diseases. We hypothesize that by studying novel endophenotypes we will find novel genes and variants implicated on AD and other complex traits. Specific Aim 1: To identify common genetic variants associated with CSF and plasma protein levels. We will use GWAS data to identify common variants (MAF > 2%) associated with CSF and plasma protein levels. For analytes linked to Alzheimer’s Disease (i.e CLU, APOE) we will analyze whether the identified variants/genes are also associated with AD risk, onset, or rate of progression. For analytes associated with other complex traits, we will use the NHGRI GWAS dabatase, to analyze whether the identified loci has shown association with that trait. Specific Aim 2: To identify low-frequency coding variants and genes associated with CSF plasma protein levels. We will use exome-chip and whole-genome data to identify coding variants and genes associated with CSF and plasma protein levels |
| Additional Investigators |
