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: | Su-In Lee |
| Institution: | University of Washington |
| Department: | Computer Science & Engineering, Genome Sciences |
| Country: | |
| Proposed Analysis: | In a GWAS, we use genotype data of case-control individuals to test hypotheses about the relationship between each (or a combination of) SNP(s) and a phenotype. A critical limitation is the enormous number of such hypotheses, giving rise to spurious correlations with the phenotype. In recent years, there has been a growing availability of expression quantitative trait loci (eQTL) data reflecting changes in the cell's transcriptional regulatory network induced by genetic variants. This problem is particularly severe in problems related to neurodegenerative diseases, where individual SNPs explain only a small fraction of the variance. In this study, we propose to develop a computational method for leveraging these data to improve our ability to detect causal SNPs, and to use it for improving the identification of disease genes in Alzheimer’s disease (AD). Our approach is based on the assumption that a SNP that is more likely to be causal relative to gene expression may also have a higher chance of playing a causal role for phenotypes. In our recent study (Lee et al. PLoS Genet 09), we proposed a method for estimating the “regulatory potential” of SNPs based on their various properties. The key idea is to use the regulatory potential learned from eQTL data to narrow down hypotheses in GWAS. We propose several ways of incorporating a priori probability on the relevance of each SNP based on its regulatory potential into the detection of causal SNPs for diseases. This idea allows eQTL data from model organisms to be used to increase the power in human disease studies, where expression data from relevant tissues are not readily available. We plan to utilize a prior learned from a mouse brain eQTL dataset. We also plan to explore other sources for obtaining prior knowledge concerning the potential relevance of different SNPs. We will then use the requested ADNI GWAS data to measure the statistical performance of our method in selecting SNPs relevant to the disease. We note that since each SNP can explain only a small portion of the phenotypic variation, we need to use a multivariate regression model. Thus, we need the individual-level genotype data. |
| Additional Investigators |