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: | Wan-Ping Lee |
| Institution: | University of Pennsylvania |
| Department: | Pathology and Laboratory Medicine |
| Country: | |
| Proposed Analysis: | We propose to conduct genetic variant association analysis on Alzheimer's Disease (AD) by using genomes that were collected and sequenced by the Alzheimer's Disease Sequencing Project (ADSP). We will use phenotype and genetic data in ADSP. The data is de-identified and accessible via the National Institute on Aging Genetics of Alzheimer's Disease (NIAGADS) Data Storage Site with approvals of data access applications. We will use this de-identified dataset from ADSP and will not work on any project to identify individuals by using the data. As to the research plan, we will start with copy number variation (CNV) and structural variation (SV) detection and characterization of CNV sequence features (e.g., microhomology, non-template insertions, and segmental duplications) to understand potential mechanisms of CNV formation. Next, we will study the association of AD status with CNVs using standard association methods and adjusting for population structure (PS) and ages of onset. Since the data was collected multi-ethnic, we will perform ethnic-specific and ethnic-combined association analyses. We will use principle-component-based methods to adjust for PS but also explore the efficacy of other PS adjustment methods. Finally, we will conduct biological annotation on identified risk variants. We would appreciate your informed review and approval of the project. If you have any questions or concerns, do not hesitate to contact me at (412) 880-8674. I will serve as the contact person for this project. We look forward to your comments and approval. |
| Additional Investigators | |
| Investigator's Name: | Hui Wang |
| Proposed Analysis: | We analyzed whole-genome sequencing data from the Alzheimer’s Disease Sequencing Project (ADSP, N=16,905 subjects) and identified 400,234 (168,223 high-quality) SVs. Laboratory PCR validation of 95 SVs yielded a sensitivity of 82. For copy number variants (CNVs), we found a burden of heterozygous singletons (OR=1.12, P=0.0002) and homozygous (OR=1.10, P<0.0004) events in individuals with AD. Particularly, there is a significant burden of ultra-rare CNVs at AD genes (P=0.004), including protein-altering CNVs in ABCA7, APP, PLCG2, and SORL1. Fourteen CNVs are in linkage disequilibrium (LD) with known AD-risk variants, including a deletion (chr2:105731359-105736864) in complete LD (R2=0.99) with a rare variant, rs143080277, in NCK2. Through association analysis, we identified 16 SVs associated with AD and 13 SVs associated with AD-related endophenotypes with a false discovery rate < 0.2. Our findings demonstrate the broad impact of SVs on AD genetics. We will extend the analysis to ADSP R4 36K dataset. |
| Investigator's Name: | Po-Liang Cheng |
| Proposed Analysis: | We analyzed whole-genome sequencing data from the Alzheimer’s Disease Sequencing Project (ADSP, N=16,905 subjects) and identified 400,234 (168,223 high-quality) SVs. Laboratory PCR validation of 95 SVs yielded a sensitivity of 82. For copy number variants (CNVs), we found a burden of heterozygous singletons (OR=1.12, P=0.0002) and homozygous (OR=1.10, P<0.0004) events in individuals with AD. Particularly, there is a significant burden of ultra-rare CNVs at AD genes (P=0.004), including protein-altering CNVs in ABCA7, APP, PLCG2, and SORL1. Fourteen CNVs are in linkage disequilibrium (LD) with known AD-risk variants, including a deletion (chr2:105731359-105736864) in complete LD (R2=0.99) with a rare variant, rs143080277, in NCK2. Through association analysis, we identified 16 SVs associated with AD and 13 SVs associated with AD-related endophenotypes with a false discovery rate < 0.2. Our findings demonstrate the broad impact of SVs on AD genetics. We will extend the analysis to ADSP R4 36K dataset. |
