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: | Nianjun Liu |
| Institution: | Indiana University |
| Department: | Epidemiology and Biostatistics |
| Country: | |
| Proposed Analysis: | Although genome-wide association studies (GWAS) have successfully identified thousands of SNPs associated with complex diseases, the rich information provided by genome-wide data is still not fully utilized. Studies have shown that complex diseases may be affected by many variants that have small individual effects. Such group effects may explain some of the "missing heritability" from GWAS. We have developed statistical methods for testing the effects of groups of genetic variants. In addition, we have developed a pathway-based analysis method that efficiently combines signals from individual genes. Simulation studies have shown that our methods have higher power than their peers. In addition to the identification of genetic variants underlying diseases, the transformation of genetic discovery into practical use is also very important. We have developed a Bayesian method to predict an individual's phenotypes using genetic information. The methods have been used to some real data and show their promise. We request access to the individual-level data in order to detect new genetic variants that affect the phenotypes of Alzheimer’s disease (AD), and to build prediction models for these phenotypes. Specifically, we will conduct the following analyses: (1) Genetic association analysis. We will use novel analytic methods, including some of those developed by us, to analyze different types of phenotypes of AD, including imaging data as endophenotype, to identify genes associated with AD. To increase power, we will integrate genetic data with other information such as proteomic data and functional annotations. (2) Risk prediction. We will build prediction models for the onset and progression of AD, using various information such as demographics, genetics, clinical and imaging biomarkers. These models are expected to predict the risk of AD in clinical practice, particularly for early identification without clinical symptoms, and before rapid progression, to allow effective interventions to improve outcome. |
| Additional Investigators | |
| Investigator's Name: | Xiao Xu |
| Proposed Analysis: | Although genome-wide association studies (GWAS) have successfully identified thousands of SNPs associated with complex diseases, the rich information provided by genome-wide data is still not fully utilized. Studies have shown that complex diseases may be affected by many variants that have small individual effects. Such group effects may explain some of the "missing heritability" from GWAS. We have developed statistical methods for testing the effects of groups of genetic variants. In addition, we have developed a pathway-based analysis method that efficiently combines signals from individual genes. Simulation studies have shown that our methods have higher power than their peers. In addition to the identification of genetic variants underlying diseases, the transformation of genetic discovery into practical use is also very important. We have developed a Bayesian method to predict an individual's phenotypes using genetic information. The methods have been used to some real data and show their promise. We request access to the individual-level data in order to detect new genetic variants that affect the phenotypes of Alzheimer’s disease (AD), and to build prediction models for these phenotypes. Specifically, we will conduct the following analyses: (1) Genetic association analysis. We will use novel analytic methods, including some of those developed by us, to analyze different types of phenotypes of AD, including imaging data as endophenotype, to identify genes associated with AD. To increase power, we will integrate genetic data with other information such as proteomic data and functional annotations. (2) Risk prediction. We will build prediction models for the onset and progression of AD, using various information such as demographics, genetics, clinical and imaging biomarkers. These models are expected to predict the risk of AD in clinical practice, particularly for early identification without clinical symptoms, and before rapid progression, to allow effective interventions to improve outcome. |
| Investigator's Name: | Yu Zhang |
| Proposed Analysis: | Although genome-wide association studies (GWAS) have successfully identified thousands of SNPs associated with complex diseases, the rich information provided by genome-wide data is still not fully utilized. Studies have shown that complex diseases may be affected by many variants that have small individual effects. Such group effects may explain some of the "missing heritability" from GWAS. We have developed statistical methods for testing the effects of groups of genetic variants. In addition, we have developed a pathway-based analysis method that efficiently combines signals from individual genes. Simulation studies have shown that our methods have higher power than their peers. In addition to the identification of genetic variants underlying diseases, the transformation of genetic discovery into practical use is also very important. We have developed a Bayesian method to predict an individual's phenotypes using genetic information. The methods have been used to some real data and show their promise. We request access to the individual-level data in order to detect new genetic variants that affect the phenotypes of Alzheimer’s disease (AD), and to build prediction models for these phenotypes. Specifically, we will conduct the following analyses: (1) Genetic association analysis. We will use novel analytic methods, including some of those developed by us, to analyze different types of phenotypes of AD, including imaging data as endophenotype, to identify genes associated with AD. To increase power, we will integrate genetic data with other information such as proteomic data and functional annotations. (2) Risk prediction. We will build prediction models for the onset and progression of AD, using various information such as demographics, genetics, clinical and imaging biomarkers. These models are expected to predict the risk of AD in clinical practice, particularly for early identification without clinical symptoms, and before rapid progression, to allow effective interventions to improve outcome. |
