NG00035 – GWAS of CSF tau levels identifies risk variants for Alzheimer’s Disease

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Description

Cerebrospinal fluid (CSF) tau, tau phosphorylated at threonine 181 (ptau), and Aβ₄₂ are established biomarkers for Alzheimer’s disease (AD) and have been used as quantitative traits for genetic analyses. This is the largest genome-wide association study for cerebrospinal fluid (CSF) tau/ptau levels published to date (n = 1,269). Imputed data consists of 5,815,690 SNPS using HapMap release 22 CEU (build 36) as a reference panel.

This dataset is comprised of 769 individuals (506 Washington University, 208 University of Washington, 55 University of Pennsylvania). An additional 395 ADNI samples were used in this study but are not part of this dataset. Contact ADNI to apply for access to their GWAS data at adni.loni.usc.edu.

This dataset is part of the Knight ADRC Collection. Other datasets in this collection can be found at: https://www.niagads.org/knight-adrc-collection.

This dataset was originally published on the NIAGADS archive site on 03/17/2014 and was moved to DSS on 02/20/2025.

Sample Summary per Data Type

Sample Set	Accession	Data Type	Number of Samples
Knight ADRC GWAS of CSF	snd10111	GWAS-Imputation	769

Available Filesets

Name	Accession	Latest Release	Description
Knight GWAS of CSF: HapMap imputation and phenotypes	fsa000121	NG00035.v1	HapMap GWAS, imputation and phenotpyes

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

Data Dictionary Files

Phenotype Data Dictionary

Cerebrospinal fluid (CSF) tau, tau phosphorylated at threonine 181 (ptau), and Aβ₄₂ are established biomarkers for Alzheimer's disease (AD) and have been used as quantitative traits for genetic analyses. We performed the largest genome-wide association study for cerebrospinal fluid (CSF) tau/ptau levels published to date (n = 1,269), identifying three genome-wide significant loci for CSF tau and ptau: rs9877502 (p = 4.89 × 10⁻⁹ for tau) located at 3q28 between GEMC1 and OSTN, rs514716 (p = 1.07 × 10⁻⁸ and p = 3.22 × 10⁻⁹ for tau and ptau, respectively), located at 9p24.2 within GLIS3 and rs6922617 (p = 3.58 × 10⁻⁸ for CSF ptau) at 6p21.1 within the TREM gene cluster, a region recently reported to harbor rare variants that increase AD risk. In independent data sets, rs9877502 showed a strong association with risk for AD, tangle pathology, and global cognitive decline (p = 2.67 × 10⁻⁴, 0.039, 4.86 × 10⁻⁵, respectively) illustrating how this endophenotype-based approach can be used to identify new AD risk loci.

Sample Set	Accession Number	Number of Subjects	Number of Samples
Knight ADRC GWAS of CSF	snd10111	769	769

Consent Level	Number of Subjects
DS-ADRD-IRB-PUB	497
DS-NEURO-IRB-PUB	87
GRU-IRB-PUB	130
HMB-IRB-PUB	55

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 NG00035.

For investigators using Charles F. and Joanne Knight Alzheimer’s Disease Research Center (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.

See below for additional dataset specific acknowledgments:

For use of the ADSP-PHC harmonized phenotypes deposited within dataset, ng00067, use the following statement:

The Memory and Aging Project at the Knight-ADRC (Knight-ADRC), supported by NIH grants R01AG064614, R01AG044546, RF1AG053303, RF1AG058501, U01AG058922 and R01AG064877 to Carlos Cruchaga. The recruitment and clinical characterization of research participants at Washington University was supported by NIH grants P30AG066444, P01AG03991, and P01AG026276. Data collection and sharing for this project was supported by NIH grants RF1AG054080, P30AG066462, R01AG064614 and U01AG052410. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, the Neurogenomics and Informatics Center (NGI: https://neurogenomics.wustl.edu/) and the Departments of Neurology and Psychiatry at Washington University School of Medicine.

For use of ng00050 and ng00052, use the following statement:
This work was supported by Pfizer and grants from the National Institutes of Health (R01-AG044546, P01-AG003991), and the Alzheimer's Association (NIRG-11–200110). This research was conducted while Carlos Cruchaga was a recipient of a New Investigator Award in Alzheimer's disease from the American Federation for Aging Research. Carlos Cruchaga is a recipient of a BrightFocus Foundation Alzheimer's Disease Research Grant (A2013359S). The recruitment and clinical characterization of research participants at Washington University were supported by NIHP50 AG05681, P01 AG03991, and P01 AG026276. Some of the samples used in this study were genotyped by the ADGC and GERAD. ADGC is supported by grants from the NIH (#U01AG032984) and GERAD from the Wellcome Trust (GR082604MA) and the Medical Research Council (G0300429). Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen Idec; Bristol-Myers Squibb Company; Eisai; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Medpace; Merck; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Rev December 5, 2013 Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Total number of approved DARs: 3

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:
Pathak, Gita
Institution:
Institute for Genomic Health, Genetics and Genomic Sciences at Mount Sinai
Project Title:
Multi-modal analysis of psychiatric and dementia outcomes
Date of Approval:
August 12, 2025
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
a. Objectives of the Proposed Research This study aims to investigate the relationship between psychiatric traits and age-related cognitive decline, addressing a critical knowledge gap in understanding how mental health influences aging outcomes. b. Study Design The study employs a multi-level investigative approach combining epidemiological, genetic, and molecular methodologies. The design incorporates three complementary components: first, identification of phenotypic associations between psychiatric traits and MCI/AD through comprehensive clinical assessment; second, investigation of genetic architecture through analysis of coding and non-coding variants, genetic correlation assessments, polygenic scoring, and Mendelian randomization for causal inference; and third, examination of molecular mechanisms through genetically regulated epigenetic and proteomic processes. The study design enables stratified analyses by sex and ethnicity while controlling for demographic and lifestyle confounders, providing a comprehensive framework for understanding the psychiatric-cognitive decline relationship across multiple biological levels. c. Analytical Plan The analytical approach will proceed in sequential phases, beginning with statistical modeling to identify psychiatric traits significantly associated with MCI and AD outcomes while adjusting for demographic and lifestyle factors. Genetic analyses will employ polygenic risk scores and Mendelian randomization techniques to establish causal relationships between psychiatric conditions (particularly depression and alcohol use disorder) and cognitive outcomes. Molecular analyses will focus on identifying shared genetic loci between psychiatric and cognitive phenotypes, followed by investigation of genetically regulated methylation and proteomic markers as potential mediators. The analysis plan includes development of molecular weights to aid causal inference analyses and determination of effect directionality, with stratified results reported by sex and ethnicity to identify population-specific risk patterns and potential intervention targets.
Non-Technical Research Use Statement:
This research examines how mental health conditions like depression and anxiety may increase the risk of memory problems and Alzheimer's disease as people age. Using genetic data and biological markers, we'll study whether psychiatric conditions directly cause cognitive decline or if they share common underlying causes. The study will identify which mental health factors pose the greatest risk for dementia, particularly looking at differences between men and women and various ethnic groups. Results could help better predict and prevent cognitive decline by addressing mental health early in life, potentially improving outcomes for millions facing both psychiatric and age-related brain conditions.
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.

Total number of samples: 769

Female 419 54.5 %

Male 343 44.6 %

AD
Control	506	65.8%
Case	256	33.3%
Unknown	7	0.9%

NG00035 – GWAS of CSF tau levels identifies risk variants for Alzheimer’s Disease

Overview

Description

Sample Summary per Data Type

Available Filesets

Data Dictionary Files

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 Charles F. and Joanne Knight Alzheimer’s Disease Research Center (sa000008) data:

Related Publications

Approved Users

Total number of samples: 769