NG00169 - Progressive Supranuclear Palsy (PSP) Summary Statistics

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Description

For a majority of the cases included in the study, inclusion criteria were a neuropathological diagnosis of Progressive Supranuclear Palsy (PSP; n=2,595), with the exception of a small number of cases, both living and deceased, that only had a neurological diagnosis (n=184). PSP subjects with comorbid pathological features of other neurodegenerative disorders were not excluded from the study including AD-like features, Lewy bodies, and TDP-43 as prevalence of these comorbid features. The controls had no clinical evidence of cognitive impairment or a movement disorder (n=5,584) and neuropathologically could only have age-related pathological changes. A full list of the institutions where the material was collected can be found in our full text publication and it should be noted many of the samples included here were contained in previous studies (Höglinger et. al., 2011; Chen et. al, 2018; Sanchez-Contreras et. al., 2018).

PSP cases and controls were genotyped at three different institutions (University of Pennsylvania, Icahn School of Medicine at Mount Sinai, and the University of California Los Angeles) on three genotyping platforms (Illumina Human660W, Illumina OmniExpress 2.5, and Illumina Global Screening Array) in 10 total batches. The cases and controls were genotyped at each of the respective institutions, merged, and harmonized to contain the same variants and single nucleotide polymorphism (SNP) and sample level quality control was performed followed by imputation. The process was repeated by combining the data from the three centers and the overlapping variants were again harmonized.

PLINK v1.9 was used to perform quality control. SNP exclusion criteria included minor allele frequency < 1%, genotyping call-rate filter less than 95%, and Hardy–Weinberg threshold of 1 × 10−6. Individuals with discordant sex, non-European ancestry, genotyping failure of > 5%, or relatedness of > 0.1 were excluded. A principal component analysis (PCA) was performed to identify population substructure using EIGENSTRAT v6.1.4 and the 1000 genomes reference panel. Samples were excluded if they were six standard deviations away from the European population cluster. Each dataset was imputed on the Trans-Omics for Precision Medicine (TOPMed) Imputation Server (TIS) using the multi-ancestry release 5 (R5) reference panel which includes data on from 97,256 participants with 308,107,085 SNPs observed on 194,512 haplotypes.

Phasing was performed on the TIS using EAGLE with subsequent imputation using Minimac. Imputed variants were filtered using a conservative quality threshold, R 2≥0.8, to assure high quality of variants, and additional filtering on variants overlapping all genotype sets with MAF>0.01 was performed prior to analysis. Single-variant genome-wide association analyses was performed jointly on all imputed datasets using a score-based logistic regression under an additive model with covariate adjustment for sex, the first three PC eigenvectors for population substructure, and indicator variables for genotyping platform to mitigate potential batch effects. All association analyses were performed using the program SNPTEST 63. After analysis, variants with regression coefficient of |β|>5 and any erroneous estimates (negative standard errors or P-values equal to 0 or 1) were excluded from further analysis.

Available Filesets

Name	Accession	Latest Release	Description
PSP Summary Statistics - 2024: Full Summary Statistics (application needed)	fsa000111	NG00169.v1	Full Summary Statistics
PSP Summary Statistics - 2024: P-values only (open access)	fsa000112	NG00169.v1	p-values Only

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

Consent Level	Number of Subjects
DS-ADRD-IRB-PUB-NPU	NA

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 NG00169.

For investigators using Genetic, transcriptomic, histological, and biochemical analysis of progressive supranuclear palsy implicates glial activation and novel risk genes – Farrell et al., 2024 (sa000054) data:

The authors would like to acknowledge the following tissue repositories for providing the materials necessary to conduct the study: University of Louisville, Australian Brain Bank Network and Flinders University, Barcelona Biobanc and The University of Barcelona, Brain-Net Germany and Neurobiobank Munich, Emory University, Harvard Brain Tissue Resource Center, McLean Brain Bank, Indiana University School of Medicine, Johns Hopkins University, London brain bank, Los Angeles Veterans Association hospital brain bank, Ludwig-Maximilians- Universität München, German Center for Neurodegenerative Diseases (DZNE), Madrid (Universidad Autónoma de Madrid Spain), Massachusetts General Institute for Neurodegenerative Disease, Mayo Clinic Jacksonville, Netherlands Brain Bank and Erasmus University, New York Brain Bank, Columbia University, University of Paris, Southern Texas University, Sun Health Research Institute, University College London Queen Square Institute of NeurologyQueen Square Brain Bank for Neurological Disorders, University of California San Diego, University of California San Francisco Memory and Aging Center, University of Antwerp, University of Michigan, University of Navarra, University of Saskatchewan, University of Southern California, University of Toronto, University of Washington, University of Würzburg, Victorian Brain Bank, Boston University, Emory University, Netherlands Brain Bank and Erasmus University, Oregon Health Sciences University, University of Pittsburgh, University of Miami, University of Washington, University of California Irvine and the NIH Neurobiobank

Crary/Farrell Labs: [R01 AG054008, R01 NS095252, R01 AG060961, R01 NS086736, and R01 AG062348 P30 AG066514 to J.F.C. K01 AG070326 and CurePSP 685-2023-06-Pathway to K.F.], the Rainwater Charitable Foundation / Tau Consortium, Karen Strauss Cook Research Scholar Award, Stuart Katz & Dr. Jane Martin. Penn/Lee/Naj/Wang/Schellenberg Labs: [P01 AG017586, U54 NS100693, and UG3 NS104095; RF1 AG074328-01, and P30 AG072979; CurePSP Consortium; Controls were drawn from the ADGC (U01 AG032984, RC2 AG036528), and included samples from the National Cell Repository for Alzheimer’s Disease (NCRAD), which receives government support under a cooperative agreement grant (U24 AG021886) awarded by the National Institute on Aging (NIA). We thank contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible; Control 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); additional salary and analytical support were provided by NIA grants R01 AG054060 and RF1 AG061351] Raj/Humphrey/Ravi: [R56-AG055824, U01-AG068880 U54-NS123743 to J.H., A.R., and T.R.] Goate Lab: [Rainwater Charitable Foundation, NS123746] UCLA/Geschwind lab: [K08AG065519, 3UH3NS104095, Larry L Hillblom Foundation, Tau Consortium] Ross/Dickson: U54 NS100693, P50 AG016574, CurePSP Foundation, Mayo Foundation Hardy lab: The Dolby Foundation Höglinger Lab: 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), DFG (HO2402/18-1 MSAomics), the German Federal Ministry of Education and Research (BMBF, 01KU1403A EpiPD; 01EK1605A HitTau); Niedersächsisches Ministerium für Wissenschaft und Kunst / VolkswagenStiftung (Niedersächsisches Vorab), Petermax-Müller Foundation (Etiology and Therapy of Synucleinopathies and Tauopathies)

Total number of approved DARs: 8

Investigator:
Belloy, Michael
Institution:
Washington University in St Louis
Project Title:
Elucidating sex-specific risk for Alzheimer's disease through state-of-the-art genetics and multi-omics
Date of Approval:
January 6, 2025
Request status:
Expired
Research use statements:
Show statements
Technical Research Use Statement:
• Objectives: In this project, we seek to holistically investigate the genetic and molecular drivers of sex dimorphism in Alzheimer’s disease across ancestries. • Study design: This study integrates large-scale population genetics with multi-omics and endophenotype analyses. We are integrating all data available from ADGC and ADSP, together with other data from AMP-AD and biobanks such as UKB, FinnGen, and MVP to conduct large-scale multi-ancestry GWAS, rare-variant gene aggregation analyses, QTL studies, PWAS, TWAS, etc. We also particularly focus on X chromosome association studies. The study design also interrogates interactions with ancestry, hormone exposures, and with APOE*4, as well as comparisons to non-stratified GWAS/XWAS of Alzheimer’s disease. Further, we will also employ genetic correlation analyses, mendelian randomization, colocalization, and pleiotropy analyses, to interrogate overlap with other complex traits to better understand the mechanisms underlying sex dimorphism in Alzheimer’s disease. • Analysis plan, including the phenotypic characteristics that will be evaluated in association with genetic variants: Our phenotypes will include Alzheimer’s disease risk, conversion risk, various endophenotypes (including amyloid/tau biomarkers, brain imaging metrics, etc.) as well as molecular traits. As noted above, we will conduct large-scale multi-ancestry GWAS, XWAS, rare-variant gene aggregation analyses, QTL studies, PWAS, TWAS, etc. Specific aims include interrogating these question and analyses on (1) the autosomes, (2) the X chromosome, and (3) leveraging sex stratified QTL studies to drive discovery of risk genes.
Non-Technical Research Use Statement:
Alzheimer’s disease (AD) manifests itself differently across men and women, but the genetic and molecular factors that drive this remain elusive. AD is the most common cause of dementia and till today remains largely untreatable. It is thus crucial to study the genetics of AD in a sex-specific manner, as this will help the field gain important insights into disease pathophysiology, identify novel sex-specific risk factors relevant to personalized genetic medicine, and uncover potential new AD drug targets that may benefit both sexes. This project uses large-scale genomics and multi-omics to elucidate novel sex agnostic and sex-specific AD risk genes. We will interrogate sex dimorphism for AD risk on the autosomes and the sex chromosomes. We similarly interrogate sex dimorphism in the genetic regulation of gene expression and protein levels, which we will integrate with genetic risk for Alzheimer’s disease to further discovery risk genes. Throughout, we will also interrogate how sex-specific risk for AD interactions with hormone exposures, ancestry, and the APOE*4 risk allele.
Investigator:
Cruchaga, Carlos
Institution:
Washington University School of Medicine
Project Title:
The Familial Alzheimer Sequencing (FASe) Project
Date of Approval:
January 21, 2026
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
The goal of this study is to identify new genes and mutations that cause or increase risk for Alzheimer disease (AD), as well as protective factors. Individuals and families were selected from the Knight-ADRC (Washington University) and the NIA-LOAD study. Only families with at least three first-degree affected individuals were included. Families with pathogenic variants in the known AD or FTD genes, or in which APOE4 segregated with disease were excluded. At least two cases and one control were selected per family. Cases had an age at onset (AAO) after 65 yo and controls had a larger age at last assessment than the latest AAO within the family. Whole exome (WES) and whole genome sequencing (WGS) was generated for 1,235 individuals (285 families) that together with data from our collaborators and the ADSP family-based cohort (3,449 individuals and 757 families) will provide enough statistical power to identify new genes for AD. Dr. Tanzi (Harvard Medical School) will provide WGS from 400 families from the NIMH Alzheimer disease genetics initiative study. We will perform single variant and gene-based analyses to identify genes and variants that increase risk for disease in AD families. Single variant analysis will consist of a combination of association and segregation analyses. We will run family-based gene-based methods to identify genes that show and overall enrichment of variants in AD cases. We will also look for protective and modifier variants. To do this we will identify families loaded with AD cases, that also include individuals with a high burden of known risk variants but that do not develop the disease (escapees). We will use the sequence data and the family structure to identify variants that segregate with the escapee phenotype. The most promising variants and genes will be replicated in independent datasets (ADSP case-control, ADNI, Knight-ADRC, NIA-LOAD ). We will perform single variant and gene-based analyses to replicate the initial findings, and survival analysis to replicate the protective variants. We will select the most promising variants/genes for functional studies
Non-Technical Research Use Statement:
Family-based approaches led to the identification of disease-causing Alzheimer’s Disease (AD) variants in the genes encoding APP, PSEN1 and PSEN2. The identification of these genes led to the A?-cascade hypothesis and to the development of drugs that target this pathway. Recently, we have identified rare coding variants in TREM2, ABCA7, PLD3 and SORL1 with large effect sizes for risk for AD, confirming that rare coding variants play a role in the etiology of AD. In this proposal, we will identify rare risk and protective alleles using sequence data from families densely affected by AD. We hypothesize that these families are enriched for genetic risk factors. We already have sequence data from 695 families (2,462 individuals), that combined with the ADSP and the NIMH dataset will lead to a dataset of more than 1,042 families (4,684 individuals). Our preliminary results support the flexibility of this approach and strongly suggest that protective and risk variants with large effect size will be found, which will lead to a better understanding of the biology of the disease.
Investigator:
Fernandez, Victoria
Institution:
ACE Alzheimer Center
Project Title:
GADIR
Date of Approval:
February 10, 2026
Request status:
Approved
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:
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:
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 Approval:
March 25, 2025
Request status:
Approved
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.
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.
Investigator:
Seshadri, Sudha
Institution:
Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX
Project Title:
Therapeutic target discovery in ADSP data via comprehensive whole-genome analysis incorporating ethnic diversity and systems approaches
Date of Approval:
August 12, 2025
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
Objective: Utilize ADSP data sets to identify genes & specific genetic variants that confer risk for or protection from Alzheimer disease. Aim 1: Using combined WGS/WES across the ADSP Discovery, Disc-Ext, and FUS Phases, including single nucleotide variants, small insertion/deletions, and structural variants. We will: Aim 1a. Perform whole genome single variant and rare variant case/control association analyses of AD using ADSP and other available data; Aim 1b. Target protective variant identification via association analysis using selected controls within the ADSP data and performing meta analysis across association results based on selected controls from non-ADSP data sets. Aim 1c. Perform endophenotype analyses including cognitive function measures, hippocampal volume and circulation beta-amyloid ADSP data in subjects for which these measures are available. Meta analysis will be conducted across ADSP and non-ADSP analysis results. Aim 2: To leverage ethnically-diverse and admixed populations to identify AD variants we will: Aim 2a. Estimate and account for global and local ancestry in all analyses; Aim 2b. Perform admixture mapping in samples of admixed ancestry; and Aim 2c. Perform ethnicity-specific and trans-ethnic meta-analyses. Aim 3: To identify putative therapeutic targets through functional characterization of genes and networks via bioinformatics, integrative ‘omics analyses. We will: Aim 3a. Annotate variants with their functional consequences using bioinformatic tools and publicly available “omics” data. Aim 3b. Prioritize results, group variants with shared function, and identify key genes functionally related to AD via weighted association analyses and network approaches. Analyses will be performed in coordination with the following PIs. Coordination will involve sharing expertise, analysis plans or analysis results. No individual level data will be shared across institutions. Philip De Jager, Columbia University; Eric Boerwinkle & Myriam Fornage, U of Texas Health Science Center, Houston; Sudha Seshadri, U of Texas, San Antonio; Ellen Wijsman, U of Washington. William Salerno, Baylor College of Medicine
Non-Technical Research Use Statement:
This proposal seeks to analyze existing genetic sequencing data generated as part of the Alzheimer’s Disease Sequencing Project (ADSP) including the ADSP Follow-up Study (FUS) with the goal of identifying genes and specific changes within those genes that either confer risk for Alzheimer’s Disease or provide protection from Alzheimer’s Disease. Analytic challenges include analysis of whole genome sequencing data, appropriately accounting for population structure across European ancestry, Hispanic, and African American participants, and interpreting results in the context of other genomic data available.
Investigator:
Wainberg, Michael
Institution:
Sinai Health System
Project Title:
Uncovering the causal genetic variants, genes and cell types underlying brain disorders
Date of Approval:
February 3, 2026
Request status:
Approved
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.
Investigator:
Yokoyama, Jennifer
Institution:
University of California, San Francisco
Project Title:
Developing a novel tau-based polygenic score (TPGS)
Date of Approval:
February 4, 2025
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
The objective of this study is to develop a novel tau polygenic score (TPGS). We hypothesize that variation in genes associated with tauopathies—which are genes related to tau-metabolism or differentially expressed in comparative studies of iPSC-derived neurons, astrocytes, and microglia generated from carriers of MAPT pathogenic variants vs. isogenic controls—is associated with greater risk of sporadic primary and secondary tauopathy.Study Design: We will utilize summary statistics from large genome-wide association studies of tauopathies, selecting relevant variants that are flanking genes implicated by functional studies. Data requested from NG00169 will be analyzed in parallel with other publicly-available GWAS summary statistics as well as internal data generated at UCSF. To our knowledge, we do not anticipate that combining these datasets will increase risk of participant re-identification due to these datasets deriving from non-overlapping cohorts.Analysis Plan: We will assess multiple methods for generating the TPGS, and test whether they are associated with primary (FTLD-tau, PSP) and secondary (AD) tauopathy risk and endophenotypes of tauopathy (e.g., tau pathological burden, measures of tau in CSF). First, we will classify genetic variants using machine learning methods. We hypothesize that machine learning can be used to improve biologically driven predictions for diagnoses, pathology, and biomarkers. We will also leverage summary statistics from NG00169 to generate polygenic scores from a preselected, MAPT-related marker list. We will apply to our internal whole genome sequencing discovery dataset. The TPGS will be further validated in a separate internal cohort.Collaboration: Data obtained through this application will not be shared with researchers at other institutions.
Non-Technical Research Use Statement:
Tauopathies are a class of neurodegenerative diseases characterized by the abnormal accumulation of the tau protein. Tauopathies, including Alzheimer’s disease (AD), frontotemporal dementia (FTD), and progressive supranuclear palsy (PSP), are influenced by the combination of genetic variants that are present within an individual. Polygenic scores (PGS) can be used to estimate an individual’s genetic susceptibility for developing a trait. In this study, we will be generating a new tau polygenic score (TPGS) to predict the likelihood that a person will develop traits associated with tauopathies, such as increased tau in blood plasma. We will use summary statistics generated from genome-wide association studies in order to calculate the risk that each genetic variant carriers. Then, we will calculate individual risk scores in a cohort of UCSF research participants in order to validate the scoring algorithm.

NG00169 – Progressive Supranuclear Palsy (PSP) Summary Statistics – Farrell et al., 2024

Overview

Description

Available Filesets

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Acknowledgement

Acknowledgment statement for any data distributed by NIAGADS:

For investigators using any data from this dataset:

For investigators using Genetic, transcriptomic, histological, and biochemical analysis of progressive supranuclear palsy implicates glial activation and novel risk genes – Farrell et al., 2024 (sa000054) data:

Related Publications

Approved Users

Total number of samples: 0