NG00174-Whole Genome Sequencing (WGS) and SNP Array data for the SEA-AD Consortium Cohort

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Description

This dataset contains whole genome sequencing (WGS) and genotyping SNP array data for the Seattle Alzheimer’s Disease Brain Cell Atlas (SEA-AD) consortium’s 84 donor cohort.

Whole genome sequencing data includes 84 samples that were sent to The American Genome Center for sequencing on the Illumina NovaSeq and then to the Genome Center for Alzheimer’s Disease (GCAD) for processing. Files provided in this dataset include CRAMs, gVCFs, and a “preview” joint genotype-called project level VCF.

80 samples from the SEA-AD cohort were genotyped by the Center for Applied Genomics at the Children’s Hospital of Philadelphia using the Illumina Infinium Global Screening Array (GSA-24v3-0_A1). Raw genotypes are provided in the PLINK binary format on GRCh38.

There are no phenotypes provided within this dataset, but instead are available via SEA-AD.org as open access here: SEA-AD Data Access

Sample Summary per Data Type

Sample Set	Accession	Data Type	Number of Samples
SEA-AD WGS	snd10108	Whole Genome Sequencing	84
SEA-AD Array data	snd10109	Genotyping SNP Array	80

Available Filesets

Name	Accession	Latest Release	Description
SEA-AD WGS and Array data	fsa000117	NG00174.v1	PLINK files, CRAMs, gVCFs, and pVCFs

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

For more demographic information about the subjects, navigate to the sample set below.

Sample Set	Accession Number	Number of Subjects	Number of Samples
SEA-AD WGS	snd10108	84	84
SEA-AD Array data	snd10109	80	80

Consent Level	Number of Subjects
DS-ADRDAGE-IRB-PUB	84

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 NG00174.

For investigators using The Seattle Alzheimer’s Disease Brain Cell Atlas (SEA-AD) (sa000065) data:

The SEA-AD consortium is supported by a National Institute on Aging (NIA). We thank the participants of the ADRC and the ACT study for the data they have provided and the many ADRC and ACT investigators and staff who steward that data. You can learn more about the UW ADRC at https://depts.washington.edu/mbwc/adrc and ACT at https://actagingstudy.org/. We thank members of the Allen Institute team who contributed to the development of the Seattle Alzheimer’s Disease Brain Cell Atlas Consortium’s web portal at SEA-AD.org.

The Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) consortium is supported by the National Institute on Aging (NIA) grant U19AG060909. The study data were generated from postmortem brain tissue donated to the University of Washington BRaIN laboratory and Precision Neuropathology Core, which is supported by the UW ADRC (NIA grant no. P30AG066509, previously no. P50AG005136), the ACT study (NIA grant no. U19AG066567) and U24AG072458, U24NS135561, U24NS133945, U24NS133949, RF1AG065406, R01NS105984, R01AG60942 and UM1MH130981. Additionally, ACT data collection for this work was supported, in part, by prior funding from the NIA (no. U01AG006781) and the Nancy and Buster Alvord Endowment (to C. Dirk Keene). The Alzheimer’s Disease Genetics Consortium (ADGC grant U01AG032984) funded whole genome sequencing and genotyping of the samples. The Center for Applied Genomics at the Children’s Hospital of Philadelphia Research Institute performed genotyping of samples. The American Genome Center at the Uniformed Services University of the Health Sciences (U01AG057659) performed the sequencing. The Genome Center for Alzheimer’s Disease (GCAD grant U54AG052427) processed the data.

Total number of approved DARs: 2

Investigator:
Cruchaga, Carlos
Institution:
Washington University School of Medicine
Project Title:
The Familial Alzheimer Sequencing (FASe) Project
Date of Approval:
March 18, 2025
Request status:
Approved
Research use statements:
Show statements
Technical 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 studies
Non-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:
Zhao, Zhongming
Institution:
University of Texas Health Science Center at Houston
Project Title:
AIM-AI: an Actionable, Integrated and Multiscale genetic map of Alzheimer's disease via deep learning
Date of Approval:
March 27, 2025
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
Objectives: The objective of our study is to advance our understanding of the genetic basis of Alzheimer’s Disease (AD) through the analysis of comprehensive genomic datasets such as Whole Exome Sequencing (WES), Whole Genome Sequencing (WGS), single-nuclei RNA sequencing, and Genome-Wide Association Studies (GWAS), as well as the related phenotype. We aim to identify genetic variants that are integral to the development and progression of AD.Study Design: Our approach involves a detailed multi-omics analysis focusing on both coding and non-coding regions within these datasets. We will develop new analytical variables from existing data, ensuring that our research adheres to the established data use limitations and contributes meaningfully to the field of genetic research in AD.Analysis Plan: The plan centers on investigating the correlation between genetic variants and AD, exploring how these variants influence the disease at a genetic level. We will employ cutting-edge computational methods to analyze interactions between these genetic markers and their potential role in AD pathogenesis. The integration of data from multiple sources will be carefully executed to maintain compliance with data use agreements, emphasizing the scientific exploration of AD.
Non-Technical Research Use Statement:
Our research is dedicated to unraveling the genetic components of Alzheimer’s Disease. By analyzing genetic sequences and variations through various genomic datasets, we seek to deepen the scientific understanding of how these genetic elements contribute to AD. The outcomes of this study will be shared with the public, enhancing general knowledge of Alzheimer’s Disease and supporting the global research community in its ongoing efforts to decode this complex condition.

Total number of samples: 84

Female 51 60.7 %

Male 33 39.3 %

AD
Control	17	20.2%
Case	147	175.0%

NG00174-Whole Genome Sequencing (WGS) and SNP Array data for the SEA-AD Consortium Cohort

Overview

Description

Sample Summary per Data Type

Available Filesets

Sample information

Related Studies

Cohorts

Consent Levels

Acknowledgement

Acknowledgment statement for any data distributed by NIAGADS:

For investigators using any data from this dataset:

For investigators using The Seattle Alzheimer’s Disease Brain Cell Atlas (SEA-AD) (sa000065) data:

Related Publications

Approved Users

Total number of samples: 84