To access this data, please log into DSS and submit an application.
Within the application, add this dataset (accession NG00113) in the “Choose a Dataset” section.
Once approved, you will be able to log in and access the data within the DARM portal.

Description

The identification of genetic risk factors for Alzheimer’s Disease (AD) provides additional to support that multiple pathways contribute to its onset and progression. However, the metabolomic and lipidomic profiles altered among carriers of distinct genetic risk factors is not fully understood. The study of the metabolome can provide a direct image of dysregulated patterns in an organism, providing information on direct targets for therapeutic treatments. High-throughput metabolomic and lipidomic data for 880 analytes was generated from parietal brain tissue from 423 AD donors and neuropathology free controls using the Metabolon Precision Metabolomics platform.

Sample Summary per Data Type

Sample SetAccessionData TypeNumber of Samples
Harari Metabolomics snd10024Metabolomic436

Available Filesets

NameAccessionLatest ReleaseDescription
Harari Metabolomicsfsa000017NG00113.v1Metabolomics Data, Phenotypes, etc.

View the File Manifest for a full list of files released in this dataset.

High-throughput metabolomic and lipidomic data for 880 analytes was generated from parietal brain tissue from 423 AD donors and neuropathology free controls using the Metabolon Precision Metabolomics platform.

Sample SetAccession NumberNumber of Subjects
Harari Metabolomics snd10024423
Consent LevelNumber of Subjects
DS-ADRD-IRB-PUB436

Visit the Data Use Limitations page for definitions of the consent levels above.

Total number of approved DARs: 1
  • Investigator:
    Pan, Wei
    Institution:
    University of Minnesota
    Project Title:
    Powerful and novel statistical methods to detect genetic variants associated with or putative causal to Alzheimer’s disease
    Date of Approval:
    June 8, 2022
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical 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 two specific Aims: Aim1. 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 2. 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.
    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.

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 NG00113.

For investigators using KnightADRC (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