Overview
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Description
Plasma samples were obtained from the Knight-ADRC at Washington University in Saint Louis repositories. This is a deeply phenotyped cohort, both clinically and molecularly with longitudinal data and samples available. We included 32 samples from healthy non-demented control participants, 45 samples from presymptomatic AD participants and 53 samples from symptomatic AD participants. All AD participants were required to have evidence of Aβ deposition (CSF Aβ<500ng/L), positive PET scan and/or evidence of clinical worsening measured by CDR® from the time at draw to the last clinical visit. We also included participants from other neurodegenerative diseases: 20 DLB participants and 18 FTD participants. All participants were diagnosed in accordance with clinical criteria that are embodied in the Uniform Data Set (UDS), the standard clinical data set that is collected in all participants who are enrolled in all of the 37-federally funded ADRCs. This research was conducted in accordance with the recommended protocols. Written informed consent was obtained from all participants or their family members. The dataset consists of raw sRNA sequencing data and a supporting phenotype file.
Sample Summary per Data Type
| Sample Set | Accession | Data Type | Number of Samples |
|---|---|---|---|
| Plasma Small RNA Transcriptomics ADRD | snd10127 | Small RNA Sequencing | 168 |
Available Filesets
| Name | Accession | Latest Release | Description |
|---|---|---|---|
| Plasma Small RNA Transcriptomics ADRD | fsa000150 | NG00168.v1 | Fastq Files, Phenotypes, Readme |
View the File Manifest for a full list of files released in this dataset.
Participant Information
For more demographic information about the subjects, navigate to the sample set below.
| Sample Set | Accession Number | Number of Participants | Number of Samples |
|---|---|---|---|
| Plasma Small RNA Transcriptomics ADRD | snd10127 | 164 | 168 |
Related Studies
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The search for novel risk factors for Alzheimer disease relies on access to accurate and deeply phenotyped datasets. The Memory and Aging Project at the Knight-ADRC (Knight ADRC-MAP) collects plasma,…
Cohorts
| Cohort | Number of Participants | Number of Samples |
|---|---|---|
| Knight Alzheimer’s Disease Research Center (KGAD) | 164 | 168 |
Consent Levels
| Consent Level | Number of Participants | Number of Samples |
|---|---|---|
| DS-ADRD-IRB-PUB | 164 | 168 |
Visit the Data Use Limitations page for definitions of the consent levels above.
Acknowledgement
Acknowledgment statement for any data distributed by NIAGADS:
Data for this study were prepared, archived, and distributed by the National Institute on Aging Alzheimer's Disease Data Storage Site (NIAGADS) at the University of Pennsylvania (U24-AG041689), funded by the National Institute on Aging.
Use the study-specific acknowledgement statements below (as applicable):
For investigators using any data from this dataset:
Please cite/reference the use of NIAGADS data by including the accession NG00168.
For investigators using Charles F. and Joanne Knight Alzheimer’s Disease Research Center (Knight ADRC) (sa000008) data:
This work was supported by grants from the National Institutes of Health (R01AG044546, P01AG003991, RF1AG053303, R01AG058501, U01AG058922, RF1AG058501 and R01AG057777). The recruitment and clinical characterization of research participants at Washington University were supported by NIH P50 AG05681, P01 AG03991, and P01 AG026276. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, and the Departments of Neurology and Psychiatry at Washington University School of Medicine.
We thank the contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, and the Departments of Neurology and Psychiatry at Washington University School of Medicine.
See below for additional dataset specific acknowledgments:
For use of the ADSP-PHC harmonized phenotypes deposited within dataset, ng00067, use the following statement:
The Memory and Aging Project at the Knight-ADRC (Knight-ADRC), supported by NIH grants R01AG064614, R01AG044546, RF1AG053303, RF1AG058501, U01AG058922 and R01AG064877 to Carlos Cruchaga. The recruitment and clinical characterization of research participants at Washington University was supported by NIH grants P30AG066444, P01AG03991, and P01AG026276. Data collection and sharing for this project was supported by NIH grants RF1AG054080, P30AG066462, R01AG064614 and U01AG052410. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, the Neurogenomics and Informatics Center (NGI: https://neurogenomics.wustl.edu/) and the Departments of Neurology and Psychiatry at Washington University School of Medicine.
For use of ng00050 and ng00052, use the following statement:
This work was supported by Pfizer and grants from the National Institutes of Health (R01-AG044546, P01-AG003991), and the Alzheimer's Association (NIRG-11–200110). This research was conducted while Carlos Cruchaga was a recipient of a New Investigator Award in Alzheimer's disease from the American Federation for Aging Research. Carlos Cruchaga is a recipient of a BrightFocus Foundation Alzheimer's Disease Research Grant (A2013359S). The recruitment and clinical characterization of research participants at Washington University were supported by NIHP50 AG05681, P01 AG03991, and P01 AG026276. Some of the samples used in this study were genotyped by the ADGC and GERAD. ADGC is supported by grants from the NIH (#U01AG032984) and GERAD from the Wellcome Trust (GR082604MA) and the Medical Research Council (G0300429). Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen Idec; Bristol-Myers Squibb Company; Eisai; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Medpace; Merck; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Rev December 5, 2013 Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
Approved Users
- Investigator:Kamboh, M. IlyasInstitution:University of PittsburghProject Title:Date of Initial Approval:January 7, 2025Current Status:ApprovedPublications:None ReportedResearch Use Statements:Show statementsTechnical 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).Non-Technical Research Use Statement: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.
- Investigator:Seshadri, SudhaInstitution:Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TXDate of Initial Approval:October 14, 2020Current Status:ApprovedPublications:Show publications (9)
- Bouzid H. Clonal hematopoiesis is associated with protection from Alzheimer's disease. Nature medicine. 2023 Jul. PubMed link
- Horimoto ARVR. Admixture mapping implicates 13q33.3 as ancestry-of-origin locus for Alzheimer disease in Hispanic and Latino populations. HGG advances. 2023 Jul 13. PubMed link
- Tin A. Identification of circulating proteins associated with general cognitive function among middle-aged and older adults. Communications biology. 2023 Nov 3. PubMed link
- Wang Y. Key variants via the Alzheimer's Disease Sequencing Project whole genome sequence data. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2024 May. PubMed link
- Lee WP. Association of common and rare variants with Alzheimer's disease in more than 13,000 diverse individuals with whole-genome sequencing from the Alzheimer's Disease Sequencing Project. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2024 Dec. PubMed link
- Wang D. Frequency of variants in Mendelian Alzheimer's disease genes within the Alzheimer's Disease Sequencing Project. Journal of Alzheimer's disease : JAD. 2025 Apr. PubMed link
- Wang D. Application of the STAAR framework in detecting rare variant associations with Alzheimer disease and related dementias: Insights and implications. HGG advances. 2026 Apr 9. PubMed link
- Contreras AG. Genetic modifiers of APOE-ε4-associated cognitive decline. Nature communications. 2026 Feb 20. PubMed link
- Peloso GM. Whole genome sequencing analysis of over 3500 individuals dementia-free over 85 years old. Journal of Alzheimer's disease : JAD. 2026 Jun. PubMed link
Research Use Statements:Show statementsTechnical Research Use Statement:Objective: Utilize ADSP data sets to identify genes & specific genetic variants that confer risk for or protection from Alzheimer disease. Aim 1: Using combined WGS/WES across the ADSP Discovery, Disc-Ext, and FUS Phases, including single nucleotide variants, small insertion/deletions, and structural variants. We will: Aim 1a. Perform whole genome single variant and rare variant case/control association analyses of AD using ADSP and other available data; Aim 1b. Target protective variant identification via association analysis using selected controls within the ADSP data and performing meta analysis across association results based on selected controls from non-ADSP data sets. Aim 1c. Perform endophenotype analyses including cognitive function measures, hippocampal volume and circulation beta-amyloid ADSP data in subjects for which these measures are available. Meta analysis will be conducted across ADSP and non-ADSP analysis results. Aim 2: To leverage ethnically-diverse and admixed populations to identify AD variants we will: Aim 2a. Estimate and account for global and local ancestry in all analyses; Aim 2b. Perform admixture mapping in samples of admixed ancestry; and Aim 2c. Perform ethnicity-specific and trans-ethnic meta-analyses. Aim 3: To identify putative therapeutic targets through functional characterization of genes and networks via bioinformatics, integrative omics analyses. We will: Aim 3a. Annotate variants with their functional consequences using bioinformatic tools and publicly available “omics” data. Aim 3b. Prioritize results, group variants with shared function, and identify key genes functionally related to AD via weighted association analyses and network approaches. Analyses will be performed in coordination with the following PIs. Coordination will involve sharing expertise, analysis plans or analysis results. No individual level data will be shared across institutions. Philip De Jager, Columbia University; Eric Boerwinkle & Myriam Fornage, UT Health Science Center at Houston; Sudha Seshadri, UT Health Science Center at San Antonio; Ellen Wijsman, University of Washington; William Salerno, Baylor College of Medicine.Non-Technical Research Use Statement:This proposal seeks to analyze existing genetic sequencing data generated as part of the Alzheimer’s Disease Sequencing Project (ADSP) including the ADSP Follow-up Study (FUS) with the goal of identifying genes and specific changes within those genes that either confer risk for Alzheimer’s disease or provide protection from Alzheimer’s disease. Analytic challenges include analysis of whole genome sequencing data, appropriately accounting for population structure across European ancestry, Hispanic, and African American participants, and interpreting results in the context of other genomic data available.
Total number of participants: 164
| White | 164 |
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NA164 (100.0%)
| Alzheimer's Disease and Related Dementias (ADRD) | ||
|---|---|---|
| Control | 32 | 19.5% |
| Case | 132 | 80.5% |