Description
Data Available
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Within the application, add this dataset (accession NG00134) in the “Choose a Dataset” section.
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Description
A dried blood spot (DBS) collection in Round 7 (2017) of NHATS provided the biological material for genotyping. Samples were genotyped at Erasmus Medical Center in Rotterdam, Netherlands on the Illumina Infinium Global Screening Array v3.0. The array contains clinical and rare variants ideal for multiethnic populations. After quality control steps removing variants with high (>5%) missingness and individuals with high missingness (>5%), a total of 700,009 variants and 4,006 samples were included in the NHATS genetic dataset. Quality control was performed at the Arking Lab at the Johns Hopkins University and validated independently at the University of Michigan. We include genotyped data (build hg19/GRCh37 plink format), TOPMed imputed data (build GRCh38, vcf format), ancestry-specific analytic groups, as well as recommended sample filtering information. Within ancestry principal components are available from the NHATS study by request.
Self-reported primary race/ethnicity with missing values assigned the modal category indicated 729 non-Hispanic Black, 2,962 non-Hispanic White, 223 Hispanic, and 92 other race/ethnicity samples (see population breakdown below). For detail about each self-reported race/ethnicity group see the NHATS User Guide [https://nhats.org/researcher/nhats/methods-documentation?id=user_guide]. To request phenotype data for participants in this study, apply at https://www.nhats.org/researcher/data-access/sensitive-data-files?id=restricted_data_files
Male | Female | Total | |
---|---|---|---|
Non-Hispanic White | 1,261 | 1,701 | 2,962 |
Non-Hispanic Black | 279 | 450 | 729 |
Other* | 44 | 48 | 92 |
Hispanic | 87 | 136 | 223 |
Total | 1,671 | 2,335 | 4,006 |
Sample Summary per Data Type
Sample Set | Accession | Data Type | Number of Samples |
---|---|---|---|
National Health & Aging Trends Study (NHATS) GWAS | snd10042 | GWAS | 4,006 |
Available Filesets
Name | Accession | Latest Release | Description |
---|---|---|---|
NHATS GWAS: Genotype data | fsa000044 | NG00134.v1 | Genotype data |
NHATS GWAS: TOPMed imputation data | fsa000045 | NG00134.v1 | TOPMed imputation data |
View the File Manifest for a full list of files released in this dataset.
Subject Information
Provided in this dataset is a set of GWAS files that underwent a process of quality control measures by the Arking Lab at the Johns Hopkins University, as well as imputed genotypes from the TOPMed reference panel. 4,006 subjects were genotyped at the Erasmus Medical Center in Rotterdam, Netherlands on the Illumina Infinium Global Screening Array v3.0, which captures genotype data on 700,009 genomic SNPs.
Sample Set | Accession Number | Number of Subjects |
---|---|---|
National Health & Aging Trends Study (NHATS) GWAS | snd10042 | 4,006 |
Related Studies
Consent Levels
Consent Level | Number of Subjects |
---|---|
GRU-IRB-PUB-NPU | 4,006 |
Visit the Data Use Limitations page for definitions of the consent levels above.
Approved Users
- Investigator:Zhao, JinyingInstitution:University of FloridaProject Title:Identifying novel biomarkers for human complex diseases using an integrated multi-omics approachDate of Approval:November 21, 2023Request status:ApprovedResearch use statements:Show statementsTechnical Research Use Statement:GWAS, WES and WGS have identified many genes associated with Alzheimer’s Dementia (AD) and its related traits. However, the identified genes thus far collectively explain only a small proportion of disease heritability, suggesting that more genes remained to be identified. Moreover, there is a clear gender and ethnic disparity for AD susceptibility, but little research has been done to identify gender- and ethnic-specific variants associated with AD. Of the many challenges for deciphering AD pathology, lacking of efficient and power statistical methods for genetic association mapping and causal inference represents a major bottleneck. To tackle this challenge, we have developed a set of novel statistical and bioinformatics approaches for genetic association mapping and multi-omics causation inference in large-scale ethnicity-specific epidemiological studies. The goal of this project is to leverage the multi-omics and clinical data archived by the ADSP, ADNI, ADGC as well as other AD-related data repositories to identify novel genes and molecular markers for AD. Specifically, we will (1) validate our novel methods for identifying novel risk and protective genomic variants and multi-omics causal pathways of AD; (2) identify novel ethnicity- and gender-specific genes and molecular causal pathways of AD. We will share our results, statistical methods and computational software with the scientific community.Non-Technical Research Use Statement:Although many genes have been associated with Alzheimer’s Dementia (AD), these genes altogether explain only a small fraction of disease etiology, suggesting more genes remained to be identified. Of the many challenges for deciphering AD pathology, lacking of power statistical methods represents a major bottleneck. To tackle this challenge, we have developed a set of novel statistical and bioinformatics approaches for genetic association mapping and multi-omics causation inference in large-scale ethnicity-specific epidemiological studies. The goal of this project is to leverage the rich genetic and other omic data along with clinical data archived by the ADSP, ADNI, ADGC as well as other AD-related data repositories to identify novel genes and molecular markers for AD. Such results will enhance our understanding of AD pathogenesis and may also serve as biomarkers for early diagnosis and therapeutic targets.