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: | Timothy Chang |
| Institution: | University of California, Los Angeles |
| Department: | Neurology |
| Country: | |
| Proposed Analysis: | The objectives of this proposal are to identify rare genetic risk factors for Alzheimer’s disease. Whole genome sequencing from ADNI subjects will be incorporated as part of the Alzheimer’s disease Sequencing Project whole genome sequencing initiative. Traditional rare variant analyses have limited power due to the large number of variants and small variant effect size. Although one solution is to group variants into genes, genes do not act in isolation, but rather interact with one another in networks. Grouping variants in a network can improve power. Additionally, since most genetic risk lies in large noncoding regions of the genome, focusing analyses on noncoding regulatory regions should further increase power. We hypothesize that incorporating network connectivity in rare variant statistical tests and prioritizing functional noncoding variants will identify rare genetic risk factors in Alzheimer’s disease by overcoming deficiencies in traditional methods. |
| Additional Investigators | |
| Investigator's Name: | Daniel Geschwind |
| Proposed Analysis: | The objectives of this proposal are to identify rare genetic risk factors for Alzheimer’s disease. Whole genome sequencing from ADNI subjects will be incorporated as part of the Alzheimer’s disease Sequencing Project whole genome sequencing initiative. Traditional rare variant analyses have limited power due to the large number of variants and small variant effect size. Although one solution is to group variants into genes, genes do not act in isolation, but rather interact with one another in networks. Grouping variants in a network can improve power. Additionally, since most genetic risk lies in large noncoding regions of the genome, focusing analyses on noncoding regulatory regions should further increase power. We hypothesize that incorporating network connectivity in rare variant statistical tests and prioritizing functional noncoding variants will identify rare genetic risk factors in Alzheimer’s disease by overcoming deficiencies in traditional methods. |
