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: | Maria E Saez |
| Institution: | Caebi |
| Department: | Genomics |
| Country: | |
| Proposed Analysis: | Introduction Apolipoprotein E (APOE) ε4 allele is the most prominent risk factor for sporadic Alzheimer’s disease (AD). At least one copy of the APOE ε4 allele is found in approximately 60% of AD cases, with one ε4 allele conferring a threefold increased risk and two ε4 alleles conferring a twelvefold increased risk of developing the disease. Conversely, APOE ε2 allele protects against the disease. Despite these evidences, the molecular bases of ApoE4 pathology are still unknown. Objective Our objective is to identify allele specific ApoE signatures to gain insight into APOE-alleles mechanism of action. Methodology We will perform a two-stage meta-analysis on AD susceptibility using genome wide association study (GWAS) results from different datasets stratified by ApoE genotype in three groups: ε2 (ε2/ε2 and ε2/ε3), ε3 (ε3/ε3) and ε4 (ε3/ε4 and ε4/ ε4). Unadjusted single-locus allelic (1 df) association analysis within each independent GWAS sample will be carried out using Plink software. The SNPs included in the meta-GWAS will be mapped on the genome and linked genes will be ranked according to the evidences of association with AD. We will also take advantage of other layers of information through the generation of ranked gene lists from other OMICS data sets (mainly transcriptomics, epigenomics and proteinomics). The ranked gene lists generated from the independent analyses of the multiple OMICS data sets will be compared and combined using data driven methods. Top ranked genes resulting from the integration of the different datasets will be explored using systems biology procedures to identify specific pathways triggered by APOE protective, neutral and deleterious genotypes. The predictive performance of candidate pathways, genes and biomarkers derived from the integrative analysis will be tested in datasets with longitudinal data available. At this stage, data from AIBL and ADNI datasets and in particular, from MCI patients, will be highly valuable for testing these candidates related to clinical and neuroimaging changes associated with disease progression. This project is part of the ADAPTED IMI2 project (https://www.imi.europa.eu/content/adapted). |
| Additional Investigators |
