Overview
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The p-value only files are available in the “Open Access Dataset” tab.
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.
Related Studies
- Progressive supranuclear palsy (PSP) is a rare neurodegenerative disease characterized by the accumulation of aggregated tau proteins in astrocytes, neurons, and oligodendrocytes. Previous genome-wide association studies for PSP were based…
Consent Levels
| Consent Level | Number of Subjects |
|---|---|
| DS-ADRD-IRB-PUB-NPU | NA |
Visit the Data Use Limitations page for definitions of the consent levels above.
Acknowledgement
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.
Related Publications
Wang, H., et al. Whole-genome sequencing analysis reveals new susceptibility loci and structural variants associated with progressive supranuclear palsy. Mol Neurodegeneration. 2024 Aug. doi: 10.1186/s13024-024-00747-3 PubMed link
Approved Users
- Investigator:Cruchaga, CarlosInstitution:Washington University School of MedicineProject Title:The Familial Alzheimer Sequencing (FASe) ProjectDate of Approval:March 18, 2025Request status:ApprovedResearch use statements:Show statementsTechnical 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 studiesNon-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:Pan, WeiInstitution:University of MinnesotaProject Title:Powerful and novel statistical methods to detect genetic variants associated with or putative causal to Alzheimer’s diseaseDate of Approval:March 25, 2025Request status:ApprovedResearch use statements:Show statementsTechnical 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 specific Aims: Aim 1. Association testing using the ADSP data. We'd like to detect CV- and RV-AD associations based on the ADSP data. Aim 2. 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 3. 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 4. Multi-ancestry association analysis. We'd like to use both individual-level GWAS/WGS data and GWAS summary test for genetic associations with AD.Non-Technical Research Use Statement:We propose applying our newly developed statistical analysis methods, along with other suitable existing methods, to the existing ADSP sequencing data and other AD GWAS data to detect common or rare genetic variants associated with Alzheimer’s disease (AD). The novelty and power of our new methods are in two 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. The proposed research is feasible, promising and potentially significant to AD research. 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.