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Within the application, add this dataset (accession NG00132) in the “Choose a Dataset” section.
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The APOE and Serotonin Transporter Alleles data product includes data for the APOE isoform, directly genotyped using a TaqMan allelic discrimination SNP assay, where available, or imputed from preexisting genotype array data otherwise. This file also includes human serotonin transporter (5HTTLPR) short and long alleles measured using polymerase chain reaction (PCR). In total, there are 19,193 HRS participants in the data file: 17,237 with directly genotyped data for APOE and 1,956 additional participants with imputed data. There are 17,364 participants with valid values for 5HTTLPR.
Sample Summary per Data Type
|Sample Set||Accession||Data Type||Number of Samples|
|HRS APOE||snd10040||Targeted genotyping||19,193|
|HRS APOE: Targeted genotype data||fsa000040||NG00132.v1||Targeted genotype data|
View the File Manifest for a full list of files released in this dataset.
Provided in this dataset is a csv file containing APOE genotypes and HTTLPR calls for 19,193 total subjects. 17,237 subjects’ APOE genotypes were collected directly using a TaqMan allelic discrimination SNP assay at the Center for Inherited Disease Research (CIDR) Genetic Resources Core Facility (GRCF) and Fragment Analysis Facility (FAF) at Johns Hopkins University. 1,956 subjects’ APOE genotypes were imputed from a preexisting genotype array to the 1000G (Phase3 v5) reference panel. 17,364 subjects’ HTTLPR short and long alleles were measured using PCR.
|Sample Set||Accession Number||Number of Subjects|
|Consent Level||Number of Subjects|
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- 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.
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 NG00132.
For investigators using Health and Retirement Study (sa000021) data:
HRS is supported by the National Institute on Aging (NIA U01AG009740). The genotyping was partially funded by separate awards from NIA (RC2 AG036495 and RC4 AG039029). Our genotyping was conducted by the NIH Center for Inherited Disease Research (CIDR) at Johns Hopkins University. Genotyping quality control and final preparation were performed by the Genetics Coordinating Center at University of Washington (Phases 1-3) and the University of Michigan (Phase 4).