NG00189 – Summary statistics for GWAS of nine individual neuropsychiatric symptoms in mild cognitive impairment and dementia

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Description

Nine individual neuropsychiatric symptom domains (agitation or aggression, anxiety, apathy or indifference, delusions, depression or dysphoria, disinhibition, hallucinations, irritability or lability, nighttime behaviors) measured using the Neuropsychiatric Symptom Inventory Questionnaire were tested for association with single-nucleotide polymorphisms (SNPs) with minor allele frequency > 5% in a sample of 12,806 participants with mild cognitive impairment or dementia.

Participants were sampled from Alzheimer’s Disease Research Centers (ADRCs) throughout the United States and were of European genetic ancestry. For each NPS domain, a participant was classified as a “case” if they experienced the symptom in the month preceding any visit, and as a “control” otherwise. Parallel logistic regressions were performed for each NPS domain while controlling for sex (male v. female), batch (categorical with 15 levels), and the first 10 genetic PCs.

Available Filesets

Name	Accession	Latest Release	Description
GWAS for 9 Neuropsychiatric Symptoms: Full Summary Statistics (application needed)	fsa000159	NG00189.v1	Full Summary Statistics
GWAS for 9 Neuropsychiatric Symptoms: P-values only (open access)	fsa000160	NG00189.v1	P-values only

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

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 NG00189.

For investigators using GWAS links APOE to neuropsychiatric symptoms in mild cognitive impairment and dementia – Vattathil, et al. 2025 (sa000076) data:

The authors are grateful to the ADRC, ADNI, and BioVU participants who made this research

possible. This work was supported in full or in part by the following grants to the authors: R01

AG075827, R01 AG072120, IK4BX005219, I01BX005686; R01 AG079170; T32 HG008341;

1R01MH118233. The contents do not represent the views of the U.S. Department of Veterans

Affairs or the United States Government. The Alzheimer's Disease Genetics Consortium supported the collection of samples used in this study through National Institute on Aging (NIA) grants U01AG032984 and RC2AG036528. The NACC database is funded by NIA/NIH Grant U24 AG072122. VUMC BioVU is supported by numerous sources including the NIH funded Shared Instrumentation Grant S10RR025141 and CTSA grants UL1TR002243, UL1TR000445, and UL1RR024975. Data collection and sharing for this project were 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: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; 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.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of 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 Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Total number of approved DARs: 5

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Investigator:
Haines, Jonathan
Institution:
Case Western Reserve University
Project Title:
ADAPTT
Date of Initial Approval:
October 30, 2026
Current Status:
Approved
Publications:
None Reported
Research Use Statements:
Show statements
Technical Research Use Statement:
Objectives: The overall goal of the planned analyses for ADAPTT is to better characterize the known genetic loci that are associated with AD/ADRD to identify the most probable functional elements responsible for the association. Most of these known associations have not been assigned to specific genes or functional variants. We plan to analyze DNA variation from SNP array, whole exome and whole genome sequence data generated from participants that span the spectrum of dementia (with a focus on Alzheimer disease (AD) but also including MCI and related dementias [ADRD]) and participants without dementia (typically considered “controls”). Study Design: We will evaluate these known loci, including the many genes underlying these associations, by integrating available genetic data, genomic data, in silico annotations, and multi-omic data using multiple different computational approaches. Our approaches will be sensitive to the different study designs of the contributing datasets (e.g. cohort studies, case-control studies, family-based studies). We will also examine a small number of these loci, prioritized by our analyses, using molecular genomic methods to better characterize the molecular mechanisms underlying the associations. Collaboration: This is a collaboration with Drs. Martin, Pericak-Vance, and Vance at the University of Miami.
Non-Technical Research Use Statement:
We are attempting to identify the inherited elements that contribute to Alzheimer disease risk. To do this we will analyze DNA sequence data from participants with Alzheimer disease and related dementias along with subjects who are cognitively normal. There are many known regions of the human genome that are associated with AD/ADRD risk, but the actual DNA sequence variations that are responsible for those associations remain unknown. Our goal is to use statistical and computer methods to identify the underlying responsible variations. For a small number of these, we will directly examine the biological mechanisms that help explain how those variations impact AD/ADRD.
Investigator:
Kamboh, M. Ilyas
Institution:
University of Pittsburgh
Project Title:
Genetics of Alzheimer's Disease and Endophenotypes
Date of Initial Approval:
January 7, 2025
Current Status:
Approved
Publications:
None Reported
Research Use Statements:
Show statements
Technical Research Use Statement:
Objectives: We are requesting access to the NIAGADS datasets to augment our ongoing studies on the genetics of Alzheimer’s disease (AD) and AD-related endophenotypes being carried out by Kamboh and his group since 1995. We are doing GWAS using array genotypes, whole-exome sequencing and whole-genome sequencing on datasets derived from University of Pittsburgh ADRC and ancillary population-based longitudinal studies on dementia and biomarkers. Different available phenotypes include AD and non-AD dementia, age-at-set, disease progression and survival, neuroimaging, cognitive decline, plasma biomarkers for the core ATN and non-ATN pathologies. We also plan to expand on gene-gene interaction and sex-stratified analyses which require the actual genotype data. The NIAGADS datasets will be used for replication and meta-analysis, and for gene-gene interaction and sex-stratified analyses. Study Design: A case-control design will incorporate a diverse cohort of individuals with AD and age-matched controls. For quantitative traits (neuroimaging and plasma biomarkers, cognitive performance measures, indicators of disease progression), linear regression analyses will be performed to identify genetic loci. To ensure the findings are robust and inclusive, participants from diverse demographic backgrounds will be included, enabling the exploration of potential genetic variations across populations. Analysis Plan: We will conduct GWAS and targeted analyses on candidate genes on different AD and AD-related phenotypes. Primary phenotypic variables include AD disease status, age-at-onset, last age for controls, APOE genotype, cognitive decline trajectories, sex, and race. Analyses will evaluate the influence of specific genetic variants on disease risk, cognitive performance, and biomarker levels, considering both individual and interactive effects of the APOE genotype. Results will be adjusted for potential confounders, such as demographic factors, to ensure valid associations. Detail analytical methods are described in our published papers for case-control (PMID: 32651314;35694926), quantitative traits (PMID: 30361487;37666928), and cognitive decline (PMID: 37089073; 30954325).
Non-Technical Research Use Statement:
Our research group at the University of Pittsburgh (Pitt), has been working on the genetics of Alzheimer’s disease (AD) and AD-related endophenotypes for almost three decades, on data derived largely from the University of Pittsburgh Alzheimer’s Disease Research Center and ancillary dementia studies. We are requesting access to the NIAGADS genotype and phenotype datasets to augment our sample size to increase power to detect novel genetic associations with AD and related endophenotypes.
Investigator:
Kim, Jong Hun
Institution:
KOREA UNIVERSITY RESEARCH AND BUSINESS FOUNDATION
Project Title:
Discovery of APOE-Interacting Genes Through Trans-Ancestry and Sex-Stratified Analysis to Elucidate Alzheimer's Disease Risk Mechanisms and Stratify ARIA Risk Using Proxy Outcomes
Date of Initial Approval:
July 20, 2026
Current Status:
Approved
Publications:
None Reported
Research Use Statements:
Show statements
Technical Research Use Statement:
Objectives: This project identifies ancestry- and sex-specific APOE ε4 modifier genes—variants that amplify or attenuate APOE ε4’s effect on AD risk and ARIA susceptibility from anti-amyloid immunotherapy. Aim 1: Trans-ancestry sex-stratified GWIS to construct an APOE-Wide Epistasis Map. Aim 2: Mechanistic validation via eQTL/pQTL colocalization and epistasis network. Aim 3: Explainable AI (XAI) integrating modifier SNPs, multi-omics subtypes, and ARIA proxy outcomes to stratify pre-treatment ARIA risk. Study Design: Multi-cohort secondary analysis using NIAGADS-controlled ADSP data exclusively. Individual-level data from all 15 ADC cohorts (NG00022–NG00151) and multi-ancestry ADSP WGS (NG00067, NG00166) span European, African American, Hispanic/Latino, and South/East Asian ancestries. Functional datasets (eQTL/pQTL: NG00102, NG00118, NG00120, NG00130) support Aim 2; imaging and neuropathology datasets (NG00103, NG00147, NG00175) enable Aim 3 ARIA proxy development. No prospective recruitment. Multi-dataset rationale: GWIS requires 4–8× more samples than standard GWAS (Gauderman 2002); no single cohort is independently powered—all 15 ADC cohorts must be pooled. Trans-ancestry GWIS requires ancestry-matched datasets (NG00100/African, NG00106/South Asian, NG00141/Hispanic) because population-specific LD cannot be imputed from summary statistics. Functional datasets (eQTL, pQTL, methylation) are non-redundant—each covers a distinct regulatory layer for Aim 2. All datasets are AD-specific; non-AD neurodegeneration data are excluded. Analysis Plan: Phenotypes: AD case/control (primary); APOE ε4 × SNP interaction; lobar microbleed count (ARIA-H proxy); SVD score (WMH, lacunar infarcts, perivascular spaces); longitudinal cognitive decline. Covariates: age, sex, top 20 ancestry PCs, stratum. Methods: logistic GWIS; trans-ancestry meta-analysis (METAL/MR-MEGA); sex-stratified/X-chromosome analyses; eQTL/pQTL colocalization (COLOC2/SMR); XGBoost XAI with 5-fold CV and SHAP.
Non-Technical Research Use Statement:
Alzheimer’s disease affects tens of millions worldwide. Lecanemab, approved in 2024, slows Alzheimer’s progression by removing amyloid plaques—but causes dangerous brain side effects (ARIA: Amyloid-Related Imaging Abnormalities) especially in APOE ε4 carriers, who also most need treatment. Currently, doctors cannot predict which APOE ε4 carriers will benefit versus be harmed. Our research identifies modifier genes controlling how dangerous APOE ε4 is. We leverage the ADSP’s diverse dataset spanning 15+ cohorts across European, African American, Hispanic/Latino, and Asian ancestries—a scale statistically necessary because detecting gene–gene interactions requires 4–8× more samples than standard genetic studies. Population-specific patterns allow high-confidence modifier identification. MRI-based brain bleeds and vascular markers serve as validated ARIA surrogates available at scale. The result is an explainable AI tool that predicts—before treatment begins—which APOE ε4 patients face high ARIA risk and which will benefit from lecanemab, enabling precision Alzheimer’s therapy.
Investigator:
Pan, Wei
Institution:
University of Minnesota
Project Title:
Powerful and novel statistical methods to detect genetic variants associated with or putative causal to Alzheimer’s disease
Date of Initial Approval:
December 18, 2019
Current Status:
Approved
Publications:
Show publications (3)
- Knutson KA. MATS: a novel multi-ancestry transcriptome-wide association study to account for heterogeneity in the effects of cis-regulated gene expression on complex traits. Human molecular genetics. 2023 Apr 6. PubMed link
- He R. Enhancing nonlinear transcriptome- and proteome-wide association studies via trait imputation with applications to Alzheimer's disease. PLoS genetics. 2025 Apr. PubMed link
- Ren J. Large-Scale Genotype-Based Trait Imputation With Multi-Ancestry GWAS Data. Genetic epidemiology. 2026 Feb. PubMed link
Research Use Statements:
Show statements
Technical Research Use Statement:
We have been developing more powerful statistical methods to detect common variant (CV)- or rare variant (RV)-complex trait associations and/or putative causal relationships for GWAS and DNA sequencing data. Here we propose applying our new methods, along with other suitable existing methods, to the existing ADSP sequencing data and other AD GWAS data provided by NIA, hence requesting approval for accessing the ADSP sequencing and other related GWAS/genetic data. We have the following two specific Aims: Aim1. Association testing under genetic heterogeneity: For complex traits, genetic heterogeneity, especially of RVs, is ubiquitous as well acknowledged in the literature, however there is barely any existing methodology to explicitly account for genetic heterogeneity in association analysis of RVs based on a single sample/cohort. We propose using secondary and other omic data, such as transcriptomic or metabolomic data, to stratify the given sample, then apply a weighted test to the resulting strata, explicitly accounting for genetic heterogeneity that causal RVs may be different (with varying effect sizes) across unknown and hidden subpopulations. Some preliminary analyses have conﬁrmed power gains of the proposed approach over the standard analysis. Aim 2. Meta analysis of RV tests: Although it has been well appreciated that it is necessary to account for varying association effect sizes and directions in meta analysis of RVs for multi-ethnic cohorts, existing tests are not highly adaptive to varying association patterns across the cohorts and across the RVs, leading to power loss. We propose a highly adaptive test based on a family of SPU tests, which cover many existing meta-analysis tests as special cases. Our preliminary results demonstrated possibly substantial power gains. Aim 3. Inferring (putative) causal genes/proteins/metabolites for AD. We will apply TWA/PWAS/MWAS/xWAS methods to ADSP, AD GWAS and other omic data to identify (putative) causal genes, proteins, metabolites and other molecular traits for AD. These methods may be based on standard linear models or emerging ML/AI methods.
Non-Technical Research Use Statement:
We propose applying our newly developed statistical and computational methods, along with other suitable existing methods, to the existing ADSP sequencing data, other AD GWAS data and other omic data to detect common or rare genetic variants and other molecular traits, such as genes/proteins/metabolites, associated with and/pr (putative) causal to Alzheimer’s disease (AD). The novelty and power of our new methods are in three aspects: first, we consider and account for possible genetic heterogeneity with several subcategories of AD; second, we apply powerful meta-analysis methods to combine the association analyses across multiple subcategories of AD; third, we will develop and apply standard linear model- and emerging ML/AI-based causal inference methods to infer causal genes, proteins, metabolites and other traits for AD. In addition, our proposed analyses of the existing large amount of ADSP sequencing data and other AD GWAS data with our developed new methods are novel, powerful and cost-effective.
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 Initial Approval:
October 14, 2020
Current Status:
Approved
Publications:
Show publications (9)
- Bouzid H. Clonal hematopoiesis is associated with protection from Alzheimer's disease. Nature medicine. 2023 Jul. PubMed link
- Horimoto ARVR. Admixture mapping implicates 13q33.3 as ancestry-of-origin locus for Alzheimer disease in Hispanic and Latino populations. HGG advances. 2023 Jul 13. PubMed link
- Tin A. Identification of circulating proteins associated with general cognitive function among middle-aged and older adults. Communications biology. 2023 Nov 3. PubMed link
- Wang Y. Key variants via the Alzheimer's Disease Sequencing Project whole genome sequence data. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2024 May. PubMed link
- Lee WP. Association of common and rare variants with Alzheimer's disease in more than 13,000 diverse individuals with whole-genome sequencing from the Alzheimer's Disease Sequencing Project. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2024 Dec. PubMed link
- Wang D. Frequency of variants in Mendelian Alzheimer's disease genes within the Alzheimer's Disease Sequencing Project. Journal of Alzheimer's disease : JAD. 2025 Apr. PubMed link
- Wang D. Application of the STAAR framework in detecting rare variant associations with Alzheimer disease and related dementias: Insights and implications. HGG advances. 2026 Apr 9. PubMed link
- Contreras AG. Genetic modifiers of APOE-ε4-associated cognitive decline. Nature communications. 2026 Feb 20. PubMed link
- Peloso GM. Whole genome sequencing analysis of over 3500 individuals dementia-free over 85 years old. Journal of Alzheimer's disease : JAD. 2026 Jun. PubMed link
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, UT Health Science Center at Houston; Sudha Seshadri, UT Health Science Center at San Antonio; Ellen Wijsman, University 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.

NG00189 – Summary statistics for GWAS of nine individual neuropsychiatric symptoms in mild cognitive impairment and dementia – Vattathil, et al. 2025

Overview

Description

Available Filesets

Related Studies

Acknowledgement

Acknowledgment statement for any data distributed by NIAGADS:

For investigators using any data from this dataset:

For investigators using GWAS links APOE to neuropsychiatric symptoms in mild cognitive impairment and dementia – Vattathil, et al. 2025 (sa000076) data:

Publications

Approved Users

Total number of participants: 0

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