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: 8

Investigator:
Blue, Elizabeth
Institution:
University of Washington
Project Title:
Genetic modifiers of Alzheimer's disease
Date of Approval:
July 15, 2025
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
The objective of the proposed research is to identify new genes involved in Alzheimer's disease (AD) by identifying alleles contributing to increased risk for or protection against the disease, providing insight as to why individuals with risk factors develop or escape from AD, and ultimately identifying potential avenues for therapeutic approaches and prevention of the disease. Our study design will use phenotypic (ex., AD diagnosis, age-at-onset, APOE genotype) land genomic data (ex., WGS, array, imputed genotypes) from NIAGADS studies to investigate genotype-phenotype associations. Strategies include association testing and haplotype- and family-based approaches, including estimates of relatedness and population genetics analyses as needed to perform the association testing (ex. control for population structure). NIAGADS data will not be used to investigate individual identity. Consent type and other Data Use Limitations (DUL) for each study will be respected in all analyses. Data from an individual with disease-specific consent will not be used in analyses outside of that restriction, including indirect uses such as imputation reference panels or variant summary statistics. When an individual’s DUL prohibits investigation of population genetics, population history or related issues, their data will be excluded from studies that address those issues. We intend to publish or otherwise broadly share any findings from this study with the scientific community. As such, genomic summary results from datasets with a “sensitive” designation will only be shared through publications to support study’s conclusions and through NIH-funded data repositories which maintain restricted access (ex. NIAGADS). Data from NIAGADS may be combined with non-NIAGADS data from the same or other studies (obtained from dbGaP or other sources), to improve the power for novel genetic discoveries, while respecting the consent of all participants. We expect that this activity creates no additional risks to participants. Data will be shared only among Internal Collaborators at the University of Washington. We do not plan to collaborate with External Collaborators at other institutions.
Non-Technical Research Use Statement:
We propose to identify new genes involved in Alzheimer's disease (AD) by identifying alleles contributing to increased risk for or protection against the disease, providing insight as to why individuals with risk factors develop or escape from AD, and ultimately identifying potential avenues for therapeutic approaches and prevention of the disease. We will combine phenotype and genotype data using association testing and haplotype- and family-based approaches to identify associations and refine those signals with fine-mapping tools and external data.
Investigator:
Cheng, Feixiong
Institution:
Cleveland Clinic
Project Title:
A Multimodal Infrastructure for Alzheimer’s MultiOme Data Repurposing: Artificial Intelligence, Network Medicine, and Therapeutics Discovery
Date of Approval:
September 4, 2025
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
We propose to develop capable and intelligent computer-based toolboxes that enable searching, sharing, visualizing, querying, and analyzing genetics, genomics, multi-omics, and clinical data for AD. The central unifying hypothesis of this project (1U01AG073323-01 [pending for Council meeting at May/2021) is that a genome-wide, multimodal artificial intelligence (AI) framework to identify novel risk genes and networks from human WGS/WES and multi-omics findings will offer drug targets for targeted therapeutic development in AD. Aim 1 will identify rare coding variant-based risk genes using a sequence and structure-based deep learning model. Aim 2 will identify rare non-coding variant-based risk genes using a multiple kernel learning approach. Aim 3 will test whether GWAS common variants linked to AD pathobiology and endophenotypes are enriched in gene regulatory networks in a cell-type specific manner using a Bayesian framework. These analyses will leverage variants from ethnically diverse WGS/WES and clinical data (i.e., imaging, biomarkers, and cognitive measures) from Alzheimer's Disease Sequencing Project (ADSP), and publicly available chromatin interactomic data from NIH RoadMap, FANTOM5, and NIH 4D Nucleome. We will validate our findings using WGS/WES data and protein expression data from our existing cohorts: The Cleveland Clinic Lou Ruvo Center for Brain Health Aging and Neurodegenerative Disease Biobank (CBH-Biobank) and the Cleveland Alzheimer's Disease Research Center (CADRC). We will compile information for clinical data harmonization, including functional imaging, AD biomarkers, and cognitive measures for all integrative analyses. There are no any PHI information will collected or used in the data analysis. We don’t have the planned collaboration with researchers outside Cleveland Clinic in the current analytic plans.
Non-Technical Research Use Statement:
It is estimated that more than 16 million people with AD live in the United States by 2050 and the predisposition to AD involves a complex, polygenic, and pleiotropic genetic architecture. This project will develop intelligent computer-based network medicine and systems biology tools, capable of identifying and validating human genome sequencing findings for novel risk gene discoveries and targeted therapeutic development in AD. The innovative network-based, artificial intelligence toolboxes and novel risk genes and biologically relevant targeted therapeutic approaches developed in this proposal will prove to be novel and effective ways to improve outcomes in long-term brain care for the rapidly growing AD population, an essential goal of AD precision medicine.
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:
Fardo, David
Institution:
University of Kentucky
Project Title:
Genetic Architecture of Pure Alzheimer's Disease and Mixed Pathology
Date of Approval:
September 17, 2025
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
Objectives Alzheimer's disease and related dementias (ADRD) present challenges to healthcare systems worldwide, affecting millions of individuals. We aim to unravel mechanisms driving ADRD. Integrating data from diverse omics offers a comprehensive view of the molecular landscape associated with ADRD. This holistic understanding could revolutionize diagnosis, treatment, and prevention. In particular, longitudinal omics analysis has unique characteristics that merit evaluating current and novel methods.Study design & analysis plan We will perform genome-wide association studies on ADRD and also examine previously associated loci associated with clinical phenotypes and/or neuropathologies. We will examine how associations are driven by particular neuropathological features, furthering our understanding of underlying mechanisms. The corresponding transcriptome-wide data from Seattle Alzheimer’s Disease Brain Cell Atlas (SEA-AD) will be used to infer possible mechanisms by integrating biological knowledge.We will explore models to harmonize extensive multi-omics data, including transcriptome, epigenome, genome, and clinical data, to capture informative factors and associations for ADRD. We will harness data generated by SEA-AD to characterize the molecular changes linked to phenotypes at cell-type resolution. We will perform abundance analyses, differential gene expression, differential chromatin accessibility, eQTL in each cell type across multiple brain regions to help identify biological drivers of brain pathology.
Non-Technical Research Use Statement:
Alzheimer’s disease and related dementias affect millions of people worldwide. Our research aims to better understand the underlying causes of these diseases by looking closely at how genes and other biological factors contribute to their development.We will analyze a wide range of biological information—including data from DNA and RNA—collected from multiple brain regions of research participants / donors. We hope to discover new ways to identify the disease earlier, understand how it progresses, and find more effective treatments.This research is a collaborative effort with scientists from the Allen Institute for Brain Science and the University of Washington. Together, we aim to bring new insights that could lead to improved care and hope for those affected by Alzheimer’s and related dementias.
Investigator:
Konermann, Silvana
Institution:
Arc institute
Project Title:
Modeling Alzheimer’s disease risk and associated molecular phenotypes
Date of Approval:
August 8, 2025
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
The objective of the proposed research is to determine the relationship between Alzheimer’s disease (AD) genetic risk and associated molecular phenotypes. Genotype data will be used to compute a polygenic risk score (PRS) for disease-affected and control (non-disease-affected) participants. Statistical regression and mediation analyses will be used to model variation of molecular phenotypes with respect to PRS and, where available, pathology stage or cognitive impairment. Molecular phenotypes to be analyzed include bulk/single-cell/single-nucleus transcriptome, epigenome, proteome, metabolome, lipidome, amyloid, and tau. Molecular phenotypes of participants, including controls, will be matched with molecular phenotypes of in vitro cellular models, informing the design of in vitro perturbation experiments that recapitulate the genetic drivers of AD risk.
Non-Technical Research Use Statement:
Our goal is to determine the relationship between human genetic profiles associated with Alzheimer’s disease (AD) risk and specific measurable characteristics of human cells. Using multiple statistical analysis methods, we will build quantitative models that describe how those characteristics vary as a function of AD genetic risk. The models we build will help us design in vitro cellular systems that reflect different levels of AD risk, enabling experiments that inform new strategies for treating or preventing AD.
Investigator:
Seshadri, Sudha
Institution:
Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX
Project Title:
Therapeutic target discovery in ADSP data via comprehensive whole-genome analysis incorporating ethnic diversity and systems approaches
Date of Approval:
August 12, 2025
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
Objective: Utilize ADSP data sets to identify genes & specific genetic variants that confer risk for or protection from Alzheimer disease. Aim 1: Using combined WGS/WES across the ADSP Discovery, Disc-Ext, and FUS Phases, including single nucleotide variants, small insertion/deletions, and structural variants. We will: Aim 1a. Perform whole genome single variant and rare variant case/control association analyses of AD using ADSP and other available data; Aim 1b. Target protective variant identification via association analysis using selected controls within the ADSP data and performing meta analysis across association results based on selected controls from non-ADSP data sets. Aim 1c. Perform endophenotype analyses including cognitive function measures, hippocampal volume and circulation beta-amyloid ADSP data in subjects for which these measures are available. Meta analysis will be conducted across ADSP and non-ADSP analysis results. Aim 2: To leverage ethnically-diverse and admixed populations to identify AD variants we will: Aim 2a. Estimate and account for global and local ancestry in all analyses; Aim 2b. Perform admixture mapping in samples of admixed ancestry; and Aim 2c. Perform ethnicity-specific and trans-ethnic meta-analyses. Aim 3: To identify putative therapeutic targets through functional characterization of genes and networks via bioinformatics, integrative ‘omics analyses. We will: Aim 3a. Annotate variants with their functional consequences using bioinformatic tools and publicly available “omics” data. Aim 3b. Prioritize results, group variants with shared function, and identify key genes functionally related to AD via weighted association analyses and network approaches. Analyses will be performed in coordination with the following PIs. Coordination will involve sharing expertise, analysis plans or analysis results. No individual level data will be shared across institutions. Philip De Jager, Columbia University; Eric Boerwinkle & Myriam Fornage, U of Texas Health Science Center, Houston; Sudha Seshadri, U of Texas, San Antonio; Ellen Wijsman, U of Washington. William Salerno, Baylor College of Medicine
Non-Technical Research Use Statement:
This proposal seeks to analyze existing genetic sequencing data generated as part of the Alzheimer’s Disease Sequencing Project (ADSP) including the ADSP Follow-up Study (FUS) with the goal of identifying genes and specific changes within those genes that either confer risk for Alzheimer’s Disease or provide protection from Alzheimer’s Disease. Analytic challenges include analysis of whole genome sequencing data, appropriately accounting for population structure across European ancestry, Hispanic, and African American participants, and interpreting results in the context of other genomic data available.
Investigator:
Yokoyama, Jennifer
Institution:
University of California, San Francisco
Project Title:
Investigating the immunogenetic mechanisms of Alzheimer's disease
Date of Approval:
September 5, 2025
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
Objectives: The primary aim of this study is to compare how the abundance and expression of immune cells, in the brains of Alzheimer's disease (AD) patients and healthy individuals change stratified on genotype at immune-related genes using the SEA-AD dataset. Secondly, in both SEA-AD and ADSP datasets, we aim to associate SNPs in immune-related genes with clinical and pathological measures.Study Design: This study will combine the publicly available single-cell sequencing data and WGS data from the SEA-AD dataset, comprising samples from both AD patients and healthy individuals. We will also assess WGS and cognitive data from the ADSP study. We will stratify patients by diagnosis (AD, control). Immune-associated genotypes will be extracted from whole genome sequencing data; gene expression will be quantified from bulk and single-cell transcriptomic data available in the SEA-AD cohort.Analysis Plan: Using the immune cell data from the SEA-AD datasets, we will identify and characterize different immune cell populations present in the brain samples. Gene expression profiles of immune cells will be analyzed to identify differentially expressed genes (DEGs) between genotypes. This analysis will be conducted using methods such as DESeq2 or edgeR, with adjustments for multiple testing. DEGs will be functionally annotated and subjected to pathway and functional enrichment analysis to elucidate biological processes and pathways associated with immune cell function. We will compare the abundance of immune cell types between individuals with different genotypes using statistical tests such as t-tests or Wilcoxon rank-sum tests and adjust for cofactors through the use of generalized linear models. To assess associations between genetic variants and cognitive outcomes, we will leverage data from ADSP and SEA-AD cohorts, employing linear-mixed effects regression models that control for demographic (race, sex, age, education) and clinical (APOE status) characteristics.
Non-Technical Research Use Statement:
This study aims to understand how immune cells in the brain differ between Alzheimer's disease (AD) patients and healthy individuals, focusing on genetic differences in immune-related genes. Additionally, we will analyze single-cell sequencing and whole genome sequencing (WGS) data to compare immune cell abundance, gene expression, and genetic variation. We will also investigate how specific genetic variations (SNPs) in immune-related genes are linked to clinical and pathological features of AD.
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