Genetic Association Study of Alzheimer’s Disease with Whole-Genome and Whole-Exome Sequence Data
- PI
- Tzeng, Jung-Ying
- DAR ID
- 10203
- Initial Approval Date
- October 19, 2020
- Current DAR Status
- Active
- PI Institution
- North Carolina State University
- PI Institution Country
- United States
- PI Institution Type
- Non-Profit
Requested Datasets (1)
Research Use Statement
Objective of the proposed research: Recent studies have found that single nucleotide polymorphism (SNPs) and copy number variations (CNVs) can both play significant roles in missing heritability of Alzheimer’s Disease (AD). In this project, we propose to conduct a comprehensive investigation on both variant types and understand their contributions in AD risk.
Study design: We will use the whole-genome (WGS) and whole-exome (WES) sequence data in the Alzheimer's Disease Sequencing Project (ADSP) and conduct case-control association analyses of SNPs and CNVs.
Analysis plan: Using the ADSD sequence data, we will start with CNV 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 SNPs and CNVs (common and rare) using standard association methods and adjusting for population structure (PS) and ages of onset. We will also develop and apply new methods using kernel and regularized regressions to assure efficient modeling of joint SNP-CNV effects from common and rare variants. 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.
Collaborators: The team includes researchers from University of Pennsylvania (UPenn) and North Carolina State University (NCSU). UPenn researchers include Gerard Schellenberg (PI: Professor of Pathology and Laboratory Medicine), Li-San Wang (PI: Professor of Pathology and Laboratory Medicine), Wan-Ping Lee (PI: Research Assistant Professor of Pathology and Laboratory Medicine), Adam Naj (Assistant Professor of Biostatistics and Epidemiology) and Yuk Yee Leung (Research Assistant Professor of Pathology and Laboratory Medicine).
Study design: We will use the whole-genome (WGS) and whole-exome (WES) sequence data in the Alzheimer's Disease Sequencing Project (ADSP) and conduct case-control association analyses of SNPs and CNVs.
Analysis plan: Using the ADSD sequence data, we will start with CNV 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 SNPs and CNVs (common and rare) using standard association methods and adjusting for population structure (PS) and ages of onset. We will also develop and apply new methods using kernel and regularized regressions to assure efficient modeling of joint SNP-CNV effects from common and rare variants. 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.
Collaborators: The team includes researchers from University of Pennsylvania (UPenn) and North Carolina State University (NCSU). UPenn researchers include Gerard Schellenberg (PI: Professor of Pathology and Laboratory Medicine), Li-San Wang (PI: Professor of Pathology and Laboratory Medicine), Wan-Ping Lee (PI: Research Assistant Professor of Pathology and Laboratory Medicine), Adam Naj (Assistant Professor of Biostatistics and Epidemiology) and Yuk Yee Leung (Research Assistant Professor of Pathology and Laboratory Medicine).
Non-Technical Summary
Copy number variants (CNVs) are DNA regions that have gains (duplications) or losses (deletions). CNVs affect a considerable number of base pairs in the human genome. Unlike single-nucleotide polymorphisms (SNPs) that has been broadly studied in diseases, CNVs were not intensively discovered. The large-scale Alzheimer’s Disease Sequencing Project (ADSP) provides a systematic way to capture nearly all genomic variations and to study the genetic basis of Alzheimer’s Disease (AD). In this project, using the data of affected and unaffected samples from ADSP, we propose to conduct a comprehensive investigation on both variant types (SNPs and CNVs) and study their contributions in AD risk and etiology. We will start with CNV genotyping, followed by conducting standard association analysis of AD with SNPs and CNVs. We will also develop and apply new analytical methods for efficient modeling of joint SNP-CNV effects from common and rare variants. Finally, we will conduct functional annotation on identified risk variants to uncover possible biological mechanisms.
Reported Publications (9)
Publications shown are those reported by investigators through NIAGADS DAR reporting and renewal processes and may not represent a complete list of publications associated with the project.
- Copy Number Variation and Haplotype Analysis of 17q21.31 Reveals Increased Risk Associated with Progressive Supranuclear Palsy and Gene Expression Changes in Neuronal Cells. Wang H, et al. Movement disorders : official journal of the Movement Disorder Society. 2025. PMID: 40055946
- Exploring Random Forest in Genetic Risk Score Construction. Venkat V, et al. Genetic epidemiology. 2025. PMID: 41137733
- Structural variation detection and association analysis of whole-genome-sequence data from 16,543 Alzheimer's disease sequencing project subjects. Wang H, et al. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2025. PMID: 40528303
- CNV-Profile Regression: A New Approach for Copy Number Variant Association Analysis in Whole Genome Sequencing Data. Si Y, et al. bioRxiv : the preprint server for biology. 2024. PMID: 39651129
- Structural Variation Detection and Association Analysis of Whole-Genome-Sequence Data from 16,905 Alzheimer's Diseases Sequencing Project Subjects. Lee WP, et al. Research square. 2023. PMID: 37886469
- The prediction of Alzheimer's disease through multi-trait genetic modeling. Clark K, et al. Frontiers in aging neuroscience. 2023. PMID: 37577355
- Phylogeny-guided microbiome OTU-specific association test (POST). Huang C, et al. Microbiome. 2022. PMID: 35668471
- Copy Number Variation Identification on 3,800 Alzheimer's Disease Whole Genome Sequencing Data from the Alzheimer's Disease Sequencing Project. Lee WP, et al. Frontiers in genetics. 2021. PMID: 34804120
- SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-Based Gene-Environment Interaction Tests in Biobank Data. Chi JT, et al. Frontiers in genetics. 2021. PMID: 34795690