Overview
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Within the application, add this dataset (accession NG00142) in the “Choose a Dataset” section.
Once approved, you will be able to log in and access the data within the DARM portal.
Description
This dataset includes 119 samples from healthy control participants without dementia, 65 samples from presymptomatic AD participants (CDR®=0 at draw and current clinical diagnostic of AD), 42 samples from early symptomatic AD participants (CDR®=0.5 at draw and current diagnostic of AD), and 50 samples from symptomatic AD (CDR®=1 at draw, diagnostic of AD at draw, and current diagnostic of AD).
Additionally, the dataset includes participants from other neurodegenerative diseases: 17 DLB participants, 16 FTD participants, and 92 PD participants.
Sample Summary per Data Type
Sample Set | Accession | Data Type | Number of Samples |
---|---|---|---|
Plasma Cell-Free RNA Transcriptomics | snd10085 | RNA sequencing | 400 |
Available Filesets
Name | Accession | Latest Release | Description |
---|---|---|---|
Plasma Cell-Free RNA Transcriptomics | fsa000118 | NG00142.v1 | Normalized gene counts, FASTQs, and Phenotype file |
View the File Manifest for a full list of files released in this dataset.
Sample information
For more demographic information about the subjects, navigate to the sample set below.
Sample Set | Accession Number | Number of Subjects | Number of Samples |
---|---|---|---|
Plasma Cell-Free RNA Transcriptomics | snd10085 | 378 | 400 |
Related Studies
- Plasma samples were obtained from the Knight-ADRC and the Movement Disorder Clinic (MDC) at Washington University in Saint Louis repositories. These are deeply phenotyped cohorts, both clinically and molecularly with…
Consent Levels
Consent Level | Number of Subjects |
---|---|
DS-ADRD-IRB-PUB | 270 |
GRU-IRB-PUB | 108 |
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 NG00142.
For investigators using Plasma Cell-Free RNA Transcriptomics for AD and Related Dementias (sa000052) data:
We thank all the participants and their families along with the institutions and all the staff who provided plasma tissue, without whom this study would not have been possible. 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.
Related Publications
Cisterna-Garcia A,. et al. Cell-free RNA signatures predict Alzheimer’s disease. iScience. 2023 Nov 23. doi: 10.1016/j.isci.2023.108534. Pubmed Link
Approved Users
- Investigator:Cruchaga, CarlosInstitution:Washington University School of MedicineProject Title:The Familial Alzheimer Sequencing (FASe) ProjectDate of Approval:March 18, 2025Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:The goal of this study is to identify new genes and mutations that cause or increase risk for Alzheimer disease (AD), as well as protective factors. Individuals and families were selected from the Knight-ADRC (Washington University) and the NIA-LOAD study. Only families with at least three first-degree affected individuals were included. Families with pathogenic variants in the known AD or FTD genes, or in which APOE4 segregated with disease were excluded. At least two cases and one control were selected per family. Cases had an age at onset (AAO) after 65 yo and controls had a larger age at last assessment than the latest AAO within the family. Whole exome (WES) and whole genome sequencing (WGS) was generated for 1,235 individuals (285 families) that together with data from our collaborators and the ADSP family-based cohort (3,449 individuals and 757 families) will provide enough statistical power to identify new genes for AD. Dr. Tanzi (Harvard Medical School) will provide WGS from 400 families from the NIMH Alzheimer disease genetics initiative study. We will perform single variant and gene-based analyses to identify genes and variants that increase risk for disease in AD families. Single variant analysis will consist of a combination of association and segregation analyses. We will run family-based gene-based methods to identify genes that show and overall enrichment of variants in AD cases. We will also look for protective and modifier variants. To do this we will identify families loaded with AD cases, that also include individuals with a high burden of known risk variants but that do not develop the disease (escapees). We will use the sequence data and the family structure to identify variants that segregate with the escapee phenotype. The most promising variants and genes will be replicated in independent datasets (ADSP case-control, ADNI, Knight-ADRC, NIA-LOAD ). We will perform single variant and gene-based analyses to replicate the initial findings, and survival analysis to replicate the protective variants. We will select the most promising variants/genes for functional studiesNon-Technical Research Use Statement:Family-based approaches led to the identification of disease-causing Alzheimer’s Disease (AD) variants in the genes encoding APP, PSEN1 and PSEN2. The identification of these genes led to the A?-cascade hypothesis and to the development of drugs that target this pathway. Recently, we have identified rare coding variants in TREM2, ABCA7, PLD3 and SORL1 with large effect sizes for risk for AD, confirming that rare coding variants play a role in the etiology of AD. In this proposal, we will identify rare risk and protective alleles using sequence data from families densely affected by AD. We hypothesize that these families are enriched for genetic risk factors. We already have sequence data from 695 families (2,462 individuals), that combined with the ADSP and the NIMH dataset will lead to a dataset of more than 1,042 families (4,684 individuals). Our preliminary results support the flexibility of this approach and strongly suggest that protective and risk variants with large effect size will be found, which will lead to a better understanding of the biology of the disease.
- Investigator:Pan, WeiInstitution:University of MinnesotaProject Title:Powerful and novel statistical methods to detect genetic variants associated with or putative causal to Alzheimer’s diseaseDate of Approval:March 25, 2025Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:We have been developing more powerful statistical methods to detect common variant (CV)- or rare variant (RV)-complex trait associations and/or putative causal relationships for GWAS and DNA sequencing data. Here we propose applying our new methods, along with other suitable existing methods, to the existing ADSP sequencing data and other AD GWAS data provided by NIA, hence requesting approval for accessing the ADSP sequencing and other related GWAS/genetic data. We have the following specific Aims: Aim 1. Association testing using the ADSP data. We'd like to detect CV- and RV-AD associations based on the ADSP data. Aim 2. Association testing under genetic heterogeneity: For complex traits, genetic heterogeneity, especially of RVs, is ubiquitous as well acknowledged in the literature, however there is barely any existing methodology to explicitly account for genetic heterogeneity in association analysis of RVs based on a single sample/cohort. We propose using secondary and other omic data, such as transcriptomic or metabolomic data, to stratify the given sample, then apply a weighted test to the resulting strata, explicitly accounting for genetic heterogeneity that causal RVs may be different (with varying effect sizes) across unknown and hidden subpopulations. Some preliminary analyses have confirmed power gains of the proposed approach over the standard analysis. Aim 3. Meta analysis of RV tests: Although it has been well appreciated that it is necessary to account for varying association effect sizes and directions in meta analysis of RVs for multi-ethnic cohorts, existing tests are not highly adaptive to varying association patterns across the cohorts and across the RVs, leading to power loss. We propose a highly adaptive test based on a family of SPU tests, which cover many existing meta-analysis tests as special cases. Our preliminary results demonstrated possibly substantial power gains. Aim 4. Multi-ancestry association analysis. We'd like to use both individual-level GWAS/WGS data and GWAS summary test for genetic associations with AD.Non-Technical Research Use Statement:We propose applying our newly developed statistical analysis methods, along with other suitable existing methods, to the existing ADSP sequencing data and other AD GWAS data to detect common or rare genetic variants associated with Alzheimer’s disease (AD). The novelty and power of our new methods are in two aspects: first, we consider and account for possible genetic heterogeneity with several subcategories of AD; second, we apply powerful meta-analysis methods to combine the association analyses across multiple subcategories of AD. The proposed research is feasible, promising and potentially significant to AD research. In addition, our proposed analyses of the existing large amount of ADSP sequencing data and other AD GWAS data with our developed new methods are novel, powerful and cost-effective.
Total number of samples: 378
- 2322 (5.8%)
- 242 (0.5%)
- 3390 (23.8%)
- 3486 (22.8%)
- 4411 (2.9%)
- NA167 (44.2%)
Neurological and Psychiatric Brain Disorders | ||
---|---|---|
Control | 119 | 31.5% |
Case | 281 | 74.3% |