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: | Ryan Corces |
| Institution: | Gladstone Institutes |
| Department: | Institute of Neurological Disease |
| Country: | |
| Proposed Analysis: | We are requesting access to ADNI data because this is required to access WGS data through NIAGADS. We will use this data as described below. Objective: To nominate putatively functional rare noncoding variants in AD Study Design: We have developed a novel pipeline for noncoding variant prioritization that combines principles from statistical genetics, gene regulation, and machine learning. We have previously used this type of pipeline to prioritize noncoding variants within known genetic risk loci for AD (PMID: 33106633). In brief, this pipeline: 1. Identifies all known LD-expanded variants that have been significantly associated with AD 2. Filters these variants for those overlapping gene regulatory elements in specific cell types of the brain 3. Uses machine learning to predict which variants will have strong effects on transcription factor binding 4. Uses functional genomics technologies including massively parallel reporter assays and CRISPR-based genome editing to pinpoint which of the nominated variants have validated functional effects So far, this pipeline has only been applied to common variants identified by GWAS but we aim to apply this same methodology to nominate functional noncoding rare variants. With this in mind, we will: 1. Download all ADSP WGS datasets to identify all variants discovered in AD cases and controls 2. Annotate each variant with its frequency both within the ADSP cohorts and within the general population using resources such as gnomAD and TOPMED. 3. Input rare variants implicated in AD (i.e. either only observed in AD or observed more frequently in AD than in the general population) in the above described pipeline to functionally validate a subset of rare variants as putative noncoding drivers of disease. 4. Link any putative functional variants to their cell type-specific target genes The result of this work would be a list of variants with putative functional roles in AD and their putative target genes. In this study, we do not plan to associate any phenotypic characteristics other than AD vs Non-AD. No collaboration is anticipated. |
| Additional Investigators | |
| Investigator's Name: | Shreya Menon |
| Proposed Analysis: | Objective: To nominate putatively functional rare noncoding variants in AD Study Design: We have developed a novel pipeline for noncoding variant prioritization that combines principles from statistical genetics, gene regulation, and machine learning. We have previously used this type of pipeline to prioritize noncoding variants within known genetic risk loci for AD (PMID: 33106633). In brief, this pipeline: 1. Identifies all known LD-expanded variants that have been significantly associated with AD 2. Filters these variants for those overlapping gene regulatory elements in specific cell types of the brain 3. Uses machine learning to predict which variants will have strong effects on transcription factor binding 4. Uses functional genomics technologies including massively parallel reporter assays and CRISPR-based genome editing to pinpoint which of the nominated variants have validated functional effects So far, this pipeline has only been applied to common variants identified by GWAS but we aim to apply this same methodology to nominate functional noncoding rare variants. With this in mind, we will: 1. Download all ADSP WGS datasets to identify all variants discovered in AD cases and controls 2. Annotate each variant with its frequency both within the ADSP cohorts and within the general population using resources such as gnomAD and TOPMED. 3. Input rare variants implicated in AD (i.e. either only observed in AD or observed more frequently in AD than in the general population) in the above described pipeline to functionally validate a subset of rare variants as putative noncoding drivers of disease. 4. Link any putative functional variants to their cell type-specific target genes The result of this work would be a list of variants with putative functional roles in AD and their putative target genes. In this study, we do not plan to associate any phenotypic characteristics other than AD vs Non-AD. No collaboration is anticipated. |
