NG00172 - GWAS Summary Statistics of SNVs/INDELs/SVs for Progressive Supranuclear Palsy (PSP)

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Description

Participants for this analysis were selected with whole-genome sequencing at 30x coverage from the Alzheimer’s Disease Sequencing Project dataset (ADSP, ng00067.v7). Included were 1,834 PSP cases and 128 controls from the PSP-NIH-CurePSP-Tau, PSP-CurePSP-Tau, PSP-UCLA, and AMPAD-MAYO cohorts as well as 3,008 controls from other cohorts in the ADSP dataset. For association analysis, linear mixed model implemented in R Genesis were used. Genetic relatedness matrix was obtained using KING. PCs were obtained by PC-AiR which accounts for sample relatedness. Sex and PC1-5 were adjusted in the linear mixed model. Age was not adjusted as more than half (1,159 of 1,718) of PSP cases had age missing.

Available Filesets

Name	Accession	Latest Release	Description
GWAS Summary Statistics of SNVs/INDELs/SVs for PSP: Full Summary Statistics (application needed)	fsa000114	NG00172.v1	Full Summary Statistics
GWAS Summary Statistics of SNVs/INDELs/SVs for PSP: p-values Only (open access)	fsa000115	NG00172.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 NG00172.

For investigators using Whole-genome sequencing analysis reveals new susceptibility loci and structural variants associated with progressive supranuclear palsy – Wang et al., 2024 (sa000064) data:

This work was supported by NIH 5UG3NS104095, the Rainwater Charitable Foundation, and CurePSP. HW and PLC are supported by RF1-AG074328, P30-AG072979, U54-AG052427 and U24-AG041689. TSC is supported by NIH K08AG065519 and the Larry L Hillblom Foundation 2021-A-005-SUP. KF was supported by CurePSP 685–2023-06-Pathway and K01 AG070326. MG is supported by P30 AG066511. BFG and KLN are supported by P30 AG072976 and R01 AG080001. TGB and GES are supported by P30AG072980. IR is supported by 2R01AG038791-06A, U01NS100610, R25NS098999, U19 AG063911-1 and 1R21NS114764-01A1. OR is support by U54 NS100693. DG is supported by P30AG062429. ALB is supported by U19AG063911, R01AG073482, R01AG038791, and R01AG071756. BLM is supported by P01 AG019724, R01 AG057234 and P0 544014. VMV is supported by P01-AG-066597, P01-AG-017586. HRM is supported by CurePSP, PSPA, MRC, and Michael J Fox Foundation. RDS is supported by CurePSP, PSPA, and Reta Lila Weston Trust. JFC is supported by R01 AG054008, R01 NS095252, R01 AG060961, R01 NS086736, R01 AG062348, P30 AG066514, the Rainwater Charitable Foundation / Tau Consortium, Karen Strauss Cook Research, and Scholar Award, Stuart Katz & Dr. Jane Martin. AMG is supported by the Tau Consortium and U54-NS123746. YYL is supported by U54-AG052427; U24-AG041689. LSW is supported by U01AG032984, U54AG052427, and U24AG041689. GUH was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy – ID 390857198); Deutsche Forschungsgemeinschaft (DFG, HO2402/18–1 MSAomics); German Federal Ministry of Education and Research (BMBF, 01KU1403A EpiPD; 01EK1605A HitTau; 01DH18025 TauTherapy). DHG is supported by 3UH3NS104095, Tau Consortium. WPL is supported by RF1-AG074328; P30-AG072979; U54-AG052427; U24-AG041689. Cases from Banner Sun Health Research Institute were supported by the NIH (U24 NS072026, P30 AG19610 and P30AG072980), the Arizona Department of Health Services (contract 211002, Arizona Alzheimer’s Research Center), the Arizona Biomedical Research Commission (contracts 4001, 0011, 05–901 and 1001 to the Arizona Parkinson's Disease Consortium) and the Michael J. Fox Foundation for Parkinson’s Research. The Mayo Clinic Brain Bank is supported through funding by NIA grants P50 AG016574, CurePSP Foundation, and support from Mayo Foundation.

Total number of approved DARs: 9

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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 Initial Approval:
June 9, 2021
Current Status:
Approved
Publications:
Show publications (13)
- Martin W. Interpretable artificial intelligence and exascale molecular dynamics simulations to reveal kinetics: Applications to Alzheimer's disease. Current opinion in structural biology. 2022 Feb. PubMed link
- Fang J. Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer's disease. Alzheimer's research & therapy. 2022 Jan 10. PubMed link
- Fang J. Endophenotype-based in silico network medicine discovery combined with insurance record data mining identifies sildenafil as a candidate drug for Alzheimer's disease. Nature aging. 2021 Dec. PubMed link
- Zhou Y. The Alzheimer's Cell Atlas (TACA): A single-cell molecular map for translational therapeutics accelerator in Alzheimer's disease. Alzheimer's & dementia (New York, N. Y.). 2022. PubMed link
- Zhang P. Population-based discovery and Mendelian randomization analysis identify telmisartan as a candidate medicine for Alzheimer's disease in African Americans. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2023 May. PubMed link
- Xu J. Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer's disease. Cell reports. 2022 Nov 29. PubMed link
- Hou Y. Microglial immunometabolism endophenotypes contribute to sex difference in Alzheimer's disease. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2024 Feb. PubMed link
- Qiu Y. Artificial intelligence for drug discovery and development in Alzheimer's disease. Current opinion in structural biology. 2024 Apr. PubMed link
- Cheng F. Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease. Cell reports. Medicine. 2024 Feb 20. PubMed link
- Gohel D. Sildenafil as a Candidate Drug for Alzheimer's Disease: Real-World Patient Data Observation and Mechanistic Observations from Patient-Induced Pluripotent Stem Cell-Derived Neurons. Journal of Alzheimer's disease : JAD. 2024. PubMed link
- Qiu Y. Systematic characterization of multi-omics landscape between gut microbial metabolites and GPCRome in Alzheimer's disease. Cell reports. 2024 May 28. PubMed link
- Cummings J. Alzheimer's disease drug development pipeline: 2024. Alzheimer's & dementia (New York, N. Y.). 2024 Apr-Jun. PubMed link
- Xu J. Single-microglia transcriptomic transition network-based prediction and real-world patient data validation identifies ketorolac as a repurposable drug for Alzheimer's disease. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2025 Jan. PubMed link
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 Initial Approval:
January 23, 2019
Current Status:
Approved
Publications:
Show publications (10)
- Cruchaga C. Polygenic risk score of sporadic late-onset Alzheimer's disease reveals a shared architecture with the familial and early-onset forms. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2018 Feb. PubMed link
- Fernández MV. Analysis of neurodegenerative Mendelian genes in clinically diagnosed Alzheimer Disease. PLoS genetics. 2017 Nov. PubMed link
- Ridge PG. Linkage, whole genome sequence, and biological data implicate variants in RAB10 in Alzheimer's disease resilience. Genome medicine. 2017 Nov 29. PubMed link
- Fernández MV. Evaluation of Gene-Based Family-Based Methods to Detect Novel Genes Associated With Familial Late Onset Alzheimer Disease. Frontiers in neuroscience. 2018. PubMed link
- Olive C. Examination of the Effect of Rare Variants in TREM2, ABI3, and PLCG2 in LOAD Through Multiple Phenotypes. Journal of Alzheimer's disease : JAD. 2020. PubMed link
- Kirola L. Lack of evidence supporting a role for DPP6 sequence variants in Alzheimer's disease in the European American population. Acta neuropathologica. 2021 Apr. PubMed link
- Moreno-Grau S. Long runs of homozygosity are associated with Alzheimer's disease. Translational psychiatry. 2021 Feb 24. PubMed link
- Cruchaga C. Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and informs causal proteins for Alzheimer's disease. Research square. 2023 Jun 9. PubMed link
- Wang L. Proteo-genomics of soluble TREM2 in cerebrospinal fluid provides novel insights and identifies novel modulators for Alzheimer's disease. Molecular neurodegeneration. 2024 Jan 3. PubMed link
- Patel M. Whole-genome sequencing reveals the impact of lipid pathway and APOE genotype on brain amyloidosis. Human molecular genetics. 2025 Apr 6. PubMed link
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:
Fernandez, Victoria
Institution:
ACE Alzheimer Center
Project Title:
GADIR
Date of Initial Approval:
February 10, 2026
Current Status:
Approved
Publications:
None Reported
Research Use Statements:
Show statements
Technical Research Use Statement:
The objective of this study is to contribute to our understanding of neurodegenerative diseases by examining the genetic contributors of major dementia neuropathological hallmarks (amyloid-β deposition, tau pathology, TDP-43, hippocampal sclerosis, Lewy body pathology, and cerebrovascular disease, among others. We will generate the largest Iberian database(N=3500) of neuropathologically curated brains (Aim 1) with a subset of those (N≈350) undergoing deep digital phenotyping (Aim 3). We will generate an associated genetic map (Aim 2) order to elucidate how common and rare genetic variants contribute to specific pathologies. We additionally aim to determine how polygenic risk scores (PRS) and pathway-specific PRS correspond to single and mixed neuropathological profiles, and to clarify the genetic architecture driving co-pathologies that frequently complicate clinical diagnosis. Eventually, we will replicate and fine-map our findings (Aim 4) leveraging available datasets at NIAGADS and other public repositories. Our analysis plan includes genome-wide association testing of ordinal, binary, and quantitative neuropathological traits; rare-variant burden analyses for coding and non-coding regions; PRS and pathway-PRS modeling across multiple dementia-related diseases; unsupervised clustering to identify variant sets defining specific endophenotypes; and pathway and network analyses to interpret significant signals. Colocalization and functional annotation approaches will integrate genomic findings with transcriptomic and proteomic resources. Data obtained from NIAGADS will be used to strengthen replication, broaden meta-analytic power, validate associations across independent neuropathology cohorts, and support functional interpretation using available genetic, expression, and multi-omic datasets. All analyses will use de-identified data in compliance with ethical and data-sharing standards.
Non-Technical Research Use Statement:
Dementia is an immensely challenging and prevalent condition, deeply impacting the lives of over 55 million individuals worldwide. While Alzheimer's disease stands as the most commonly recognized form of dementia, there exist other conditions that present comparable symptoms but distinct underlying pathological characteristics. To provide more effective support to patients and their families, we need to better understand the genetic causes associated to each of these brain pathologies, and to develop advanced tools for early classification and diagnosis. This grant proposal aims to tackle these challenges by establishing the largest Iberian (Spanish and Portuguese) database of dementia neuropathological cases, marked by a modernized and standardized neuropathological classification alongside comprehensive genomic data. Our goal is to delve further into the genetic architecture underpinning these pathological features and to refine existing risk assessment tools for more accurate diagnoses.
Investigator:
Goate, Alison
Institution:
Icahn School of Medicine at Mount Sinai
Project Title:
Study of Alzheimer's disease and other dementias (e.g. frontotemporal dementia) and related phenotypes
Date of Initial Approval:
September 17, 2018
Current Status:
Approved
Publications:
Show publications (26)
- Chia R. Genome sequencing analysis identifies new loci associated with Lewy body dementia and provides insights into its genetic architecture. Nature genetics. 2021 Mar. PubMed link
- Novikova G. Integration of Alzheimer's disease genetics and myeloid genomics identifies disease risk regulatory elements and genes. Nature communications. 2021 Mar 12. PubMed link
- Riaz M. Effect of APOE and a polygenic risk score on incident dementia and cognitive decline in a healthy older population. Aging cell. 2021 Jun. PubMed link
- Wu HM. Heterogeneous effects of genetic risk for Alzheimer's disease on the phenome. Translational psychiatry. 2021 Jul 23. PubMed link
- Huq AJ. Polygenic score modifies risk for Alzheimer's disease in APOE ε4 homozygotes at phenotypic extremes. Alzheimer's & dementia (Amsterdam, Netherlands). 2021. PubMed link
- Kapoor M. Multi-omics integration analysis identifies novel genes for alcoholism with potential overlap with neurodegenerative diseases. Nature communications. 2021 Aug 20. PubMed link
- McInerney TW. A globally diverse reference alignment and panel for imputation of mitochondrial DNA variants. BMC bioinformatics. 2021 Sep 1. PubMed link
- Farrell K. Genome-wide association study and functional validation implicates JADE1 in tauopathy. Acta neuropathologica. 2022 Jan. PubMed link
- Horgusluoglu E. Integrative metabolomics-genomics approach reveals key metabolic pathways and regulators of Alzheimer's disease. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2022 Jun. PubMed link
- Bowles KR. Dysregulated coordination of MAPT exon 2 and exon 10 splicing underlies different tau pathologies in PSP and AD. Acta neuropathologica. 2022 Feb. PubMed link
- Reyes-Dumeyer D. The National Institute on Aging Late-Onset Alzheimer's Disease Family Based Study: A resource for genetic discovery. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2022 Oct. PubMed link
- Xue D. Large-scale sequencing studies expand the known genetic architecture of Alzheimer's disease. Alzheimer's & dementia (Amsterdam, Netherlands). 2021. PubMed link
- Bellenguez C. New insights into the genetic etiology of Alzheimer's disease and related dementias. Nature genetics. 2022 Apr. PubMed link
- Tcw J. Cholesterol and matrisome pathways dysregulated in astrocytes and microglia. Cell. 2022 Jun 23. PubMed link
- Podlesny-Drabiniok A. BHLHE40/41 regulate macrophage/microglia responses associated with Alzheimer's disease and other disorders of lipid-rich tissues. bioRxiv : the preprint server for biology. 2023 Feb 13. PubMed link
- Andrews SJ. The complex genetic architecture of Alzheimer's disease: novel insights and future directions. EBioMedicine. 2023 Apr. PubMed link
- Podleśny-Drabiniok A. BHLHE40/41 regulate microglia and peripheral macrophage responses associated with Alzheimer's disease and other disorders of lipid-rich tissues. Nature communications. 2024 Mar 6. PubMed link
- Farrell K. Genetic, transcriptomic, histological, and biochemical analysis of progressive supranuclear palsy implicates glial activation and novel risk genes. Nature communications. 2024 Sep 9. PubMed link
- Nguyen L. CASP8 intronic expansion identified by poly-glycine-arginine pathology increases Alzheimer's disease risk. Proceedings of the National Academy of Sciences of the United States of America. 2025 Feb 18. PubMed link
- Humphrey J. Long-read RNA sequencing atlas of human microglia isoforms elucidates disease-associated genetic regulation of splicing. Nature genetics. 2025 Mar. 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
- Phillips JM. Novel modelling approaches to elucidate the genetic architecture of resilience to Alzheimer's disease. Brain : a journal of neurology. 2025 Aug 1. PubMed link
- Cruchaga C. GWAS meta-analysis of CSF Alzheimer's disease biomarkers 18,948 individuals reveal novel loci and genes regulating lipid metabolism, brain volume and autophagy. Research square. 2025 May 21. PubMed link
- Nicolas A. Transferability of European-derived Alzheimer's disease polygenic risk scores across multiancestry populations. Nature genetics. 2025 Jul. PubMed link
- Rajabli F. Multi-ancestry genome-wide meta-analysis of 56,241 individuals identifies known and novel cross-population and ancestry-specific associations as novel risk loci for Alzheimer's disease. Genome biology. 2025 Jul 17. PubMed link
- Chatterjee A. Evaluating the causal effect of mitochondrial dysfunction on Alzheimer's and Parkinson's disease using Polygenic Risk Scores and Mendelian Randomization. medRxiv : the preprint server for health sciences. 2025 Sep 27. PubMed link
Research Use Statements:
Show statements
Technical Research Use Statement:
Alzheimer's disease (AD) is the most common form of dementia but has no effective prevention or treatment. Developing a comprehensive picture of the genetic architecture of AD including a network level functional assessment of risk/resilience genes is essential to develop novel therapeutic targets. The overarching goals of this study are to use genetic and genomic approaches to: 1) identify genes and variants that are involved in the development of AD and related disorders; 2) identify functional networks enriched for AD or related disorder risk and protective loci; 3) determine how cellular function and physiology is impacted by these genetic factors in disease-relevant cell types and animal models. This study will use publicly available whole genome/exome sequence data generated by the Alzheimer’s Disease Sequencing Project (ADSP) and genome-wide association study (GWAS) data from the International Genomics of Alzheimer’s Project (IGAP) and others. We will apply a suite of case-control and family approaches to investigate genetic association with dichotomous and continuous disease traits. This study will not only further our understanding of the genetic architecture of AD but also provide key information regarding the molecular mechanisms, setting the stage for novel therapeutic development.
Non-Technical Research Use Statement:
Alzheimer’s disease (AD) is the only disease among the top ten killers in the U.S. without a disease modifying therapy. Genetic studies provide a powerful means to identify genes and pathways that are causally linked to disease etiology. We propose to use genomic and functional approaches to identify genes that alter the risk of AD and investigate how these genes disrupt cellular pathways leading to disease.
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:
Konermann, Silvana
Institution:
Arc institute
Project Title:
Modeling Alzheimer’s disease risk and associated molecular phenotypes
Date of Initial Approval:
August 8, 2025
Current Status:
Expired
Publications:
None Reported
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:
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:
Expired
Publications:
Show publications (6)
- 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
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:
Wainberg, Michael
Institution:
Sinai Health System
Project Title:
Uncovering the causal genetic variants, genes and cell types underlying brain disorders
Date of Initial Approval:
April 3, 2024
Current Status:
Approved
Publications:
None Reported
Research Use Statements:
Show statements
Technical Research Use Statement:
We propose a multifaceted approach to elucidate and interpret genetic risk factors for Alzheimer's disease. First, we propose to perform a whole-genome sequencing meta-analysis of the Alzheimer's Disease Sequencing Project with the UK Biobank and All of Us to associate rare coding and non-coding variants with Alzheimer's disease and related dementias. We will explore a variety of case definitions in the UK Biobank and All of Us, including those based on ICD codes from electronic medical records (inpatient, primary care and/or death), self-report of Alzheimer's disease or Alzheimer's disease and related dementias, and/or family history of Alzheimer's disease or Alzheimer's disease and related dementias. We will perform single-variant, coding-variant burden, and non-coding variant burden tests using the REGENIE genome-wide association study toolkit. Second, we propose to develop statistical and machine learning models that can effectively infer (“fine-map”) the causal gene(s), variant(s), and cell type(s) underlying each association we find, as well as associations from existing genome-wide association studies and other Alzheimer's- and aging-related cohorts found in NIAGADS. In particular, we propose to improve causal gene identification by incorporating knowledge of gene function as a complement to functional genomics. For instance, we plan to develop improved methods for inferring biological networks, particularly from single-cell data, and integrate these networks with the results of the non-coding associations from our first aim to fine-map causal genes. To fine-map causal variants and cell types, we plan to integrate the associations from our first aim with single-nucleus chromatin accessibility data from postmortem brain cohorts to simultaneously infer which variant(s) are causal for each discovered locus and which cell type(s) they act through.
Non-Technical Research Use Statement:
We have a comprehensive plan to understand and explain the genetic factors that contribute to Alzheimer's disease. Our approach involves two main steps. First, we'll analyze genetic information from large research databases to identify rare genetic changes associated with Alzheimer's and related memory disorders. We'll look at both specific changes in genes and other parts of the genetic code. We'll use data from different studies and combine them to get a clearer picture. Second, we'll create advanced computer models that can help us figure out which specific genes, genetic changes, and cell types are responsible for these associations. This will help us pinpoint the most important factors contributing to Alzheimer's disease. We'll also analyze data from previous studies to build a more complete understanding of these genetic links.

NG00172 – GWAS Summary Statistics of SNVs/INDELs/SVs for Progressive Supranuclear Palsy (PSP) – Wang et al., 2024

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Acknowledgment statement for any data distributed by NIAGADS:

For investigators using any data from this dataset:

For investigators using Whole-genome sequencing analysis reveals new susceptibility loci and structural variants associated with progressive supranuclear palsy – Wang et al., 2024 (sa000064) data:

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