ADNI data is made available to researchers around the world. As such, there are many active research projects accessing and applying the shared ADNI data. To further encourage Alzheimer’s disease research collaboration, and to help prevent duplicate efforts, the list below shows the specific research focus of the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Guedj Mickael |
| Institution: | Pharnext |
| Department: | Bioinformatics and Biostatistics |
| Country: | |
| Proposed Analysis: | Revealing mechanisms underlying complex diseases poses great challenges to biologists. The increasing availability of dense Single Nucleotide Polymorphisms (SNPs) maps due to rapid improvements in Molecular Biology and genotyping technologies have recently led geneticists towards genome-wide association studies (GWAS) with hopes of encouraging results concerning our understanding of the genetic basis of complex diseases. The analysis of such high-throughput data has implied new statistical and computational problematic to face, which constitutes the main topic of our project. In both linkage and association studies, replication of initial findings in independent populations has been put forward as the gold standard for results validation in order to filter false positives from true signals. Historically, association has referred primarily to marker association, implicating the marker as the basis unit of the analysis. With the increasing marker density and the use of an indirect approach to association through Linkage Disequilibrium, association is now often considered at the haplotypic level. Consequently, the current tendency is to perform replications on the basis of the marker or the haplotype. However in practice, such replications are generally difficult to obtain. Among the different possible causes, lack of power, multiple-testing, genotyping-error, missing-values and population stratifications are often invoked. Beside these factors, inconsistent findings may also result from real biological differences between populations. The complex nature of etiologies under investigation including differences in allele frequencies, allele and locus heterogeneity as well as a high degree of variation in the strength of LD among populations of different origins, is a major challenge for the discovery of disease susceptibility loci. As a result, a given locus may have very different patterns of association across different populations. In this context, the marker or haplotype-based analysis can appear limited and we believe that this problem can be reduced by considering “Local Replications” (defined as the presence of a local accumulation of high statistics of association in a given genomic region, which is replicated among the different populations) instead of strict replications of markers or haplotypes. A statistics well adapted to such a problem is the Local Score, already proposed to identify associated genomic regions in GWAS. Then, our idea is to explore replication at a higher more complex level, by considering replication of gene-networks. We can extend the concept of Local Replication to “Gene-Networks Replication” interpreted as the accumulation of high statistics within a given set of genes which is replicated among the different populations. For this problem, we plan to investigate combinations of the Local Score with network-structure and gene-set-enrichment analyses. |
| Additional Investigators |