Ongoing Investigations

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: ZAID ALKHALEDI
Institution: UNIVERSITY OF KENTUCKY
Department: STATISTICS
Country:
Proposed Analysis: Serial Testing for Detection of Multilocus Genetic Interactions with Application in Alzheimer's Disease. We proposed a new algorithm, Ordered-Combinatorial Quantitative Multifactor-Dimensionality Reduction (OQMDR), which is an extension of the Multifactor-Dimensionality Reduction (MDR) method, to analyze data sets with continuous phenotypes that overcome some of the drawbacks in the Quantitative MDR (QMDR) algorithm. The proposed algorithm utilizes the concept of the Ordered Combinatorial Partition (OCP) method to data sets with quantitative traits to perform a series of t-tests to capture the genetic predisposition. The significance of the proposed model is tested by estimating the Generalized Extreme Value Distribution (GEVD) of the testing t-score to reduce the computation burden. In the applied part of the research, we consider analyzing Alzheimer's disease data set using the OQMDR algorithm, where only interactions that involve the APOE gene are considered (rather than all possible two-way interactions among a set of genetic factors). This simplification will greatly reduce computation time, as fewer interactions will be considered. In addition, higher order interactions involving APOE may also be considered.
Additional Investigators  
Investigator's Name: Richard Charnigo
Proposed Analysis: Serial Testing for Detection of Multilocus Genetic Interactions with Application in Alzheimer's Disease. We proposed a new algorithm, Ordered-Combinatorial Quantitative Multifactor-Dimensionality Reduction (OQMDR), which is an extension of the Multifactor-Dimensionality Reduction (MDR) method, to analyze data sets with continuous phenotypes that overcome some of the drawbacks in the Quantitative MDR (QMDR) algorithm. The proposed algorithm utilizes the concept of the Ordered Combinatorial Partition (OCP) method to data sets with quantitative traits to perform a series of t-tests to capture the genetic predisposition. The significance of the proposed model is tested by estimating the Generalized Extreme Value Distribution (GEVD) of the testing t-score to reduce the computation burden. In the applied part of the research, we consider analyzing Alzheimer's disease data set using the OQMDR algorithm, where only interactions that involve the APOE gene are considered (rather than all possible two-way interactions among a set of genetic factors). This simplification will greatly reduce computation time, as fewer interactions will be considered. In addition, higher order interactions involving APOE may also be considered.