Identifying Alzheimer's Disease Genetic Risk Factors By Integrated Genomic and Proteomic Analysis
- PI
- Wingo, Thomas
- DAR ID
- 11491
- Initial Approval Date
- January 21, 2026
- Current DAR Status
- Active
- PI Institution
- University of California, Davis
- PI Institution Country
- United States
- PI Institution Type
- Non-Profit
Requested Datasets (34)
NG00067Alzheimer's Disease Sequencing Project Umbrella StudyNG00106LASI-DAD Genotyping Chip Data (Illumina Infinium GSA-24 v2.0), Imputed Data (1000 Genomes Phase 3 v5 and TOPMed r2)NG00114DNA Methylation in Alzheimer disease brainsNG00119Health and Retirement Study Genotype Data 2006-2012NG00121Alzheimer’s disease single-neuron whole-genome sequencing - Miller 2022NG00117NCRAD Families GWASNG00127A longitudinal study of Alzheimer's Disease and other dementing illnesses – KnightADRC GWASNG00129NCRAD Families Whole Exome SequencingNG00118AMP-AD SV WGS and SV-xQTLNG00130Large scale CSF proteogenomic atlasNG00131Large scale genetics study on CSF and brain metabolite levelsNG00102Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders
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NG00153Health and Retirement Study (HRS) DNA MethylationNG00022ADC1 - Alzheimer's Disease Center Dataset 1NG00023ADC2 - Alzheimer's Disease Center Dataset 2NG00024ADC3 - Alzheimer's Disease Center Dataset 3NG00068ADC4 - Alzheimer's Disease Center Dataset 4NG00069ADC5 - Alzheimer's Disease Center Dataset 5NG00070ADC6 - Alzheimer's Disease Center Dataset 6NG00071ADC7 - Alzheimer's Disease Center Dataset 7NG00136ADC8 - Alzheimer's Disease Center Dataset 8NG00137ADC9 - Alzheimer's Disease Center Dataset 9NG00138ADC10 - Alzheimer's Disease Center Dataset 10NG00139ADC11 - Alzheimer's Disease Center Dataset 11NG00140ADC12 - Alzheimer's Disease Center Dataset 12NG00149ADC13 - Alzheimer's Disease Center Dataset 13NG00150ADC14 - Alzheimer's Disease Center Dataset 14NG00151ADC15 - Alzheimer's Disease Center Dataset 15NG00165CHARGE Association results from the ADSP R1 WGS data setNG00162Oligodendrocytes single-cell whole genome and RNA sequencingNG00141The Sacramento Area Latino Study on AgingNG00174Whole Genome Sequencing (WGS) and SNP Array data for the SEA-AD Consortium CohortNG00177ADGC Multi-Ancestry GWAS Meta-Analysis – Rajabli et al., 2025NG00175Genome-wide association study of neuropathology endophenotypes
Research Use Statement
We aim to uncover new genetic risk variants for Alzheimer’s disease (AD), AD-related dementia (ADRD), and behavioral and psychiatric symptoms (BPS) associated with AD/ADRD. We expect to use whole-genome sequencing (WGS), whole-genome genotyping (WGG), and whole-exome sequencing (WES) data. Additionally, we will use the results of brain proteomic analysis to nominate genes and pathways for AD, ADRD, and dementia BPS. We plan to publish our findings to share them with the scientific community.
Outcomes that will be tested include: (1) clinical disease status, (2) pathologic characterization (e.g., measures of beta-amyloid, tau, etc.), (3) cognitive decline, (4) BPSD, and (5) outcomes related to AD/ADRD severity. For sequencing data, we will extract raw sequencing reads from CRAM/BAM (or equivalent encrypted files) and re-map those to hg38 build of the human genome using PEMapper. Bascalling will be performed using PECaller using default settings. Variant annotation will use Bystro and quality control will follow approaches to assess completeness and account for ancestry as is customary in our lab. For rare variants, we will a variety of kernel-based approaches and for common variants, use standard statistical modeling. For all analyses, we plan to control for population structure deriving principal components from the underlying sequencing or genotyping data.
Outcomes that will be tested include: (1) clinical disease status, (2) pathologic characterization (e.g., measures of beta-amyloid, tau, etc.), (3) cognitive decline, (4) BPSD, and (5) outcomes related to AD/ADRD severity. For sequencing data, we will extract raw sequencing reads from CRAM/BAM (or equivalent encrypted files) and re-map those to hg38 build of the human genome using PEMapper. Bascalling will be performed using PECaller using default settings. Variant annotation will use Bystro and quality control will follow approaches to assess completeness and account for ancestry as is customary in our lab. For rare variants, we will a variety of kernel-based approaches and for common variants, use standard statistical modeling. For all analyses, we plan to control for population structure deriving principal components from the underlying sequencing or genotyping data.
Non-Technical Summary
Our aim is to identify genetic variants that are associated with Alzheimer's Disease (AD) to uncover new genetic associations. We will examine the role of important risk factors for AD (e.g., age and sex) in our analyses. Separately, we will perform integration of genetic findings for AD with information about how genetic variants influence or are associated with gene expression in the brain, cerebrospinal fluid, or blood to uncover new pathways of disease. Our overarching aim is to use genetic discoveries to identify mechanisms of AD pathogenesis to help nominate new treatment targets.
Reported Publications
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.
No publications have been reported for this DAR.