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: Kaarin Anstey
Institution: Australian National University
Department: Centre for Research on Ageing, Health and Wellbein
Country:
Proposed Analysis: Genome-Wide Association Studies have identified a number of novel genetic variants of moderate effects sizes that are associated with Alzheimer’s disease. The identification of these variants allows for the development of personalized approaches in estimating genetic risk of developing Alzheimer’s disease. A popular method estimating genetic risk is to incorporate multiple genetic risk variants into a summary genetic risk score (GRS). Current methods for constructing GRS, however, utilize an additive framework, in which the genetic risk score is composed of the additive effects of the individuals SNPs. As such these models fail to take into account potential epistatic effects between genetic variants. As such, inclusion of gene-gene interactions into a GRS for Alzheimer’s disease may increase the predictive ability of GRS. The aim of this analysis would be to construct a GRS that includes gene-gene interactions and assess the association of the GRS with the development of Alzheimer’s disease using the clinical assessments and genetic data collected from ADNI participants. The GRS will be constructed using a novel method proposed by Dai et al 2013 (see BioData Mining, 6(1), 1. http://doi.org/10.1186/1756-0381-6-1), Aggregated-Multifactor Dimensionality Reduction (A-MDR), an extension Multifactor Dimensionality Reduction which is a popular method of identifying potential gene-gene interactions. A-MDR has been applied to a dataset consisting of Juvenile Idiopathic Arthritis patients where it was able to distinguish between individuals who responded successfully to treatment with methotrexate with 82% accuracy.
Additional Investigators  
Investigator's Name: Shea Andrews
Proposed Analysis: Genome-Wide Association Studies have identified a number of novel genetic variants of moderate effects sizes that are associated with Alzheimer’s disease. The identification of these variants allows for the development of personalized approaches in estimating genetic risk of developing Alzheimer’s disease. A popular method estimating genetic risk is to incorporate multiple genetic risk variants into a summary genetic risk score (GRS). Current methods for constructing GRS, however, utilize an additive framework, in which the genetic risk score is composed of the additive effects of the individuals SNPs. As such these models fail to take into account potential epistatic effects between genetic variants. As such, inclusion of gene-gene interactions into a GRS for Alzheimer’s disease may increase the predictive ability of GRS. The aim of this analysis would be to construct a GRS that includes gene-gene interactions and assess the association of the GRS with the development of Alzheimer’s disease using the clinical assessments and genetic data collected from ADNI participants. The GRS will be constructed using a novel method proposed by Dai et al 2013 (see BioData Mining, 6(1), 1. http://doi.org/10.1186/1756-0381-6-1), Aggregated-Multifactor Dimensionality Reduction (A-MDR), an extension Multifactor Dimensionality Reduction which is a popular method of identifying potential gene-gene interactions. A-MDR has been applied to a dataset consisting of Juvenile Idiopathic Arthritis patients where it was able to distinguish between individuals who responded successfully to treatment with methotrexate with 82% accuracy.