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: | Jung-Ying Tzeng |
| Institution: | North Carolina State University |
| Department: | Statistics |
| Country: | |
| Proposed Analysis: | We propose to develop statistical methods for studying gene and gene-environment (GxE) effects on multivariate, potentially of high dimension, outcomes, including repeatedly measured phenotypes, imaging data, and endo-phenotypes of a disorder. The statistical framework will be developed based on mixed effects modeling of genetic and GxE effects of multiple variants, which will identify important G and GxE factors via kernel-based methods and regularization approaches. Numerical studies based on ANDI genetic data will be used to evaluate the performance of the methods, including to design simulations, generate pseudo-data, develop computer programs and address specific issues arisen from real practice. Once validated in simulation studies, the proposed methods will be applied on the ANDI data to examine its ability in detecting new and existing associated variants. |
| Additional Investigators | |
| Investigator's Name: | Wenbin Lu |
| Proposed Analysis: | We propose to develop statistical methods for studying gene and gene-environment (GxE) effects on multivariate, potentially of high dimension, outcomes, including repeatedly measured phenotypes, imaging data, and endo-phenotypes of a disorder. The statistical framework will be developed based on mixed effects modeling of genetic and GxE effects of multiple variants, which will identify important G and GxE factors via kernel-based methods and regularization approaches. Numerical studies based on ANDI genetic data will be used to evaluate the performance of the methods, including to design simulations, generate pseudo-data, develop computer programs and address specific issues arisen from real practice. Once validated in simulation studies, the proposed methods will be applied on the ANDI data to examine its ability in detecting new and existing associated variants. |
| Investigator's Name: | Arnab Maity |
| Proposed Analysis: | We propose to develop statistical methods for studying gene and gene-environment (GxE) effects on multivariate, potentially of high dimension, outcomes, including repeatedly measured phenotypes, imaging data, and endo-phenotypes of a disorder. The statistical framework will be developed based on mixed effects modeling of genetic and GxE effects of multiple variants, which will identify important G and GxE factors via kernel-based methods and regularization approaches. Numerical studies based on ANDI genetic data will be used to evaluate the performance of the methods, including to design simulations, generate pseudo-data, develop computer programs and address specific issues arisen from real practice. Once validated in simulation studies, the proposed methods will be applied on the ANDI data to examine its ability in detecting new and existing associated variants. |
