Genetics of Alzheimer's Disease and Endophenotypes
- PI
- Kamboh, M. Ilyas
- DAR ID
- 11542
- Initial Approval Date
- January 7, 2025
- Current DAR Status
- Active
- PI Institution
- University of Pittsburgh
- PI Institution Country
- United States
- PI Institution Type
- Non-Profit
Requested Datasets (57)
NG00067Alzheimer's Disease Sequencing Project Umbrella StudyNG00108Profiling microglia expression profiles in AD using single-nuclei RNA-seqNG00105MiGA – Microglia Genomic AtlasNG00113Metabolomic and lipidomic signatures in Alzheimer disease brainsNG00114DNA Methylation in Alzheimer disease brainsNG00104Dataset from Clinical and Pathological Studies in the Oldest Old (90+ Study)NG00121Alzheimer’s disease single-neuron whole-genome sequencing - Miller 2022NG00127A longitudinal study of Alzheimer's Disease and other dementing illnesses – KnightADRC GWASNG00126A fast and robust strategy to remove variant level artifacts in Alzheimer’s Disease Sequencing Project data (Belloy et al. 2022)NG00118AMP-AD SV WGS and SV-xQTLNG00134National Health and Aging Trends Study (NHATS) GWASNG00130Large scale CSF proteogenomic atlas
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NG00144The Prediction of Alzheimer’s Disease through Multi-Trait Genetic ModelingNG00131Large scale genetics study on CSF and brain metabolite levelsNG00146Singapore Longitudinal study of Vascular Cognitive Impairment and DementiaNG00102Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disordersNG00154Dementia in Parkinson's Disease(PDD)NG00160Sex-specific genetic architecture of late-life memory performanceNG00022ADC1 - 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 15NG00159Longitudinal Change in Memory Performance as a Strong Endophenotype for Alzheimer’s Disease – Archer, et al, 2023NG00116Resolving mutations in challenging genomic regions to test association with disease phenotypesNG00157Sex-specific genetic predictors of Alzheimer's disease biomarkers – Deming, et al. 2018NG00158Sex differences in the genetic predictors of Alzheimer’s pathology – Dumitrescu, et al. 2019NG00161Sex differences in the genetic architecture of cognitive resilience to Alzheimer's disease – Eissman, et al. 2022NG00156Genetic variants and functional pathways associated with resilience to Alzheimer's diseaseNG00147Amyloid Neuroimaging and Genetics Initiative (ANGI) GWASNG00165CHARGE Association results from the ADSP R1 WGS data setNG00162Oligodendrocytes single-cell whole genome and RNA sequencingNG00142Plasma Cell-Free RNA Transcriptomics for AD and Related DementiasNG00172GWAS Summary Statistics of SNVs/INDELs/SVs for Progressive Supranuclear Palsy (PSP) – Wang et al., 2024NG00174Whole Genome Sequencing (WGS) and SNP Array data for the SEA-AD Consortium CohortNG00100Novel Alzheimer Disease Risk Loci and Pathways in African American Individuals Using the African Genome Resources Panel: A Meta-analysis. Kunkle et al. (2021)NG00035GWAS of CSF tau levels identifies risk variants for Alzheimer’s DiseaseNG00050GWAS of CLU, A potential endophenotype for Alzheimer's diseaseNG00178Multi-brain region mRNA-seq of sporadic ALS patients with pTDP-43 pathological stagingNG00075IGAP Rare Variant Summary Statistics- Kunkle et al. (2019)NG00177ADGC Multi-Ancestry GWAS Meta-Analysis – Rajabli et al., 2025NG00179AD GWAS in participants of African ancestry – Ray et al., 2024NG00175Genome-wide association study of neuropathology endophenotypesNG00167Brain Small RNA Transcriptomics for AD and Related DementiasNG00168Plasma Small RNA Transcriptomics for AD and Related DementiasNG00183Diverse somatic genomic alterations in single neurons in chronic traumatic encephalopathyNG00189GWAS summary statistics of neuropsychiatric symptoms in MCI and dementia – Vattathil et al., 2025
Research Use Statement
Objectives:
We are requesting access to the NIAGADS datasets to augment our ongoing studies on the genetics of Alzheimer’s disease (AD) and AD-related endophenotypes being carried out by Kamboh and his group since 1995. We are doing GWAS using array genotypes, whole-exome sequencing and whole-genome sequencing on datasets derived from University of Pittsburgh ADRC and ancillary population-based longitudinal studies on dementia and biomarkers. Different available phenotypes include AD and non-AD dementia, age-at-set, disease progression and survival, neuroimaging, cognitive decline, plasma biomarkers for the core ATN and non-ATN pathologies. We also plan to expand on gene-gene interaction and sex-stratified analyses which require the actual genotype data. The NIAGADS datasets will be used for replication and meta-analysis, and for gene-gene interaction and sex-stratified analyses.
Study Design:
A case-control design will incorporate a diverse cohort of individuals with AD and age-matched controls. For quantitative traits (neuroimaging and plasma biomarkers, cognitive performance measures, indicators of disease progression), linear regression analyses will be performed to identify genetic loci. To ensure the findings are robust and inclusive, participants from diverse demographic backgrounds will be included, enabling the exploration of potential genetic variations across populations.
Analysis Plan:
We will conduct GWAS and targeted analyses on candidate genes on different AD and AD-related phenotypes. Primary phenotypic variables include AD disease status, age-at-onset, last age for controls, APOE genotype, cognitive decline trajectories, sex, and race. Analyses will evaluate the influence of specific genetic variants on disease risk, cognitive performance, and biomarker levels, considering both individual and interactive effects of the APOE genotype. Results will be adjusted for potential confounders, such as demographic factors, to ensure valid associations. Detail analytical methods are described in our published papers for case-control (PMID: 32651314;35694926), quantitative traits (PMID: 30361487;37666928), and cognitive decline (PMID: 37089073; 30954325).
We are requesting access to the NIAGADS datasets to augment our ongoing studies on the genetics of Alzheimer’s disease (AD) and AD-related endophenotypes being carried out by Kamboh and his group since 1995. We are doing GWAS using array genotypes, whole-exome sequencing and whole-genome sequencing on datasets derived from University of Pittsburgh ADRC and ancillary population-based longitudinal studies on dementia and biomarkers. Different available phenotypes include AD and non-AD dementia, age-at-set, disease progression and survival, neuroimaging, cognitive decline, plasma biomarkers for the core ATN and non-ATN pathologies. We also plan to expand on gene-gene interaction and sex-stratified analyses which require the actual genotype data. The NIAGADS datasets will be used for replication and meta-analysis, and for gene-gene interaction and sex-stratified analyses.
Study Design:
A case-control design will incorporate a diverse cohort of individuals with AD and age-matched controls. For quantitative traits (neuroimaging and plasma biomarkers, cognitive performance measures, indicators of disease progression), linear regression analyses will be performed to identify genetic loci. To ensure the findings are robust and inclusive, participants from diverse demographic backgrounds will be included, enabling the exploration of potential genetic variations across populations.
Analysis Plan:
We will conduct GWAS and targeted analyses on candidate genes on different AD and AD-related phenotypes. Primary phenotypic variables include AD disease status, age-at-onset, last age for controls, APOE genotype, cognitive decline trajectories, sex, and race. Analyses will evaluate the influence of specific genetic variants on disease risk, cognitive performance, and biomarker levels, considering both individual and interactive effects of the APOE genotype. Results will be adjusted for potential confounders, such as demographic factors, to ensure valid associations. Detail analytical methods are described in our published papers for case-control (PMID: 32651314;35694926), quantitative traits (PMID: 30361487;37666928), and cognitive decline (PMID: 37089073; 30954325).
Non-Technical Summary
Our research group at the University of Pittsburgh (Pitt), has been working on the genetics of Alzheimer’s disease (AD) and AD-related endophenotypes for almost three decades, on data derived largely from the University of Pittsburgh Alzheimer’s Disease Research Center and ancillary dementia studies. We are requesting access to the NIAGADS genotype and phenotype datasets to augment our sample size to increase power to detect novel genetic associations with AD and related endophenotypes.
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.