{"data":[{"dar_ID":11386,"expiration_date":"2027-03-31 17:02:38","approval_date":"2026-03-31 17:02:38","initial_approval_date":"2025-01-06 18:33:03","project_name":"Elucidating sex-specific risk for Alzheimer's disease through state-of-the-art genetics and multi-omics","status_name":"Approved","first_name":"Michael","last_name":"Belloy","institution":"Washington University in St Louis","dataset_accessions":["NG00169"],"publications":["41588232","41437896","41404291","41282793"],"dar_publications":["41588232","41437896","41404291","41282793"],"researchSTMT":"•\tObjectives: In this project, we seek to holistically investigate the genetic and molecular drivers of sex dimorphism in Alzheimer’s disease across ancestries. \n•\tStudy 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. \n•\tAnalysis 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.\n","ntResearchSTMT":"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."},{"dar_ID":10796,"expiration_date":"2027-03-16 17:53:39","approval_date":"2026-03-16 17:53:39","initial_approval_date":"2023-03-08 18:23:07","project_name":"Trajectories of Cognition in Middle Age: Implications for Alzheimer's Disease and Related Dementias in the U.S.","status_name":"Approved","first_name":"Rebecca","last_name":"Brown","institution":"University of Pennsylvania","dataset_accessions":["NG00169"],"publications":["41348855"],"dar_publications":["41348855"],"researchSTMT":"Polygenic risk scores (PRS) for dementia and aging-related conditions are known to be associated with cognitive outcomes in older age, but little is known about their relationship to mid-life cognitive decline. We plan to use raw genetic data to derive novel PRS from GWAS sources (including Lambert Alzheimer’s disease PRS, with and without APOE; aPRS for coronary artery disease; a longevity PRS) and evaluate their predictive accuracy for cognitive outcomes in middle age relative to existing PRS. Specifically, we want to create a measure of genetic risk associated with three outcomes: age-related cognition; telomere shortening; and methylation/epigenetic clocks. To achieve this, we will combine the HRS Genotype data with other HRS datasets (Harmonized Cognitive Assessment Protocol (HCAP) (2016 Early V1.0); 2008 Telomere Data; Epigenetic Clocks; 2016 Venous Blood Study (VBS)) to which we already have access. Once we have approved NIAGADS genomics data access, we will additionally request access to the HRS-NIAGADS Cross-Reference File (Genotype Data v3,2006-2012) to link the genomics and HRS datasets. In our ongoing analyses, we would like to update our PRS models by incorporating the most recent GWAS summary statistics. For Alzheimer's disease, this requires access to the full summary statistics from the Kunkle et al., 2019 GWAS. We also would like access to the full summary statistics from the Farrell et al., 2024 GWAS and the Rajabli et al., 2025 GWAS to identify genetic modifiers of tauopathy by comparing progressive supranuclear palsy GWAS results with cross-ancestry Alzheimer’s disease GWAS results.  ","ntResearchSTMT":"There is evidence to suggest that differences in people’s genetic code might contribute to differences in age-associated cognitive changes. For example, some people develop memory problems in middle age, and other people experience no changes in memory. Researchers think this may be partially explained by differences in people’s genetic code. We might be able to predict who could experience age-related cognitive changes based on their DNA sequence. If we know which people have experienced memory problems, we can see what their DNA has in common compared to the DNA of people who don’t have any memory problems. Then, we can test this by looking at the DNA of a different group of people; evaluating if their DNA has the same things in common as the group of people with memory problems (vs. no memory problems); and predicting whether they will develop memory problems. The long-term goal of this work is to help identify people who might be at risk for developing memory problems and help them access preventative care or interventions to minimize future cognitive impairment."},{"dar_ID":10026,"expiration_date":"2027-01-21 15:13:51","approval_date":"2026-01-21 15:13:51","initial_approval_date":"2019-01-23 09:28:16","project_name":"The Familial Alzheimer Sequencing (FASe) Project","status_name":"Approved","first_name":"Carlos","last_name":"Cruchaga","institution":"Washington University School of Medicine","dataset_accessions":["NG00169"],"publications":[],"dar_publications":["39927718","38172904","37333337","32894242","33591372","33627629","29670507","29183403","29091718","28943286"],"researchSTMT":"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","ntResearchSTMT":"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."},{"dar_ID":12038,"expiration_date":"2027-02-10 12:59:57","approval_date":"2026-02-10 12:59:57","initial_approval_date":"2026-02-10 12:59:57","project_name":"GADIR","status_name":"Approved","first_name":"Victoria","last_name":"Fernandez","institution":"ACE Alzheimer Center","dataset_accessions":["NG00169"],"publications":[],"dar_publications":[],"researchSTMT":"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.\n\nOur 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.\n\nData 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.","ntResearchSTMT":"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."},{"dar_ID":10008,"expiration_date":"2027-07-08 15:00:01","approval_date":"2026-07-08 15:00:01","initial_approval_date":"2018-09-17 16:19:09","project_name":"Study of Alzheimer's disease and other dementias (e.g. frontotemporal dementia) and related phenotypes","status_name":"Approved","first_name":"Alison","last_name":"Goate","institution":"Icahn School of Medicine at Mount Sinai","dataset_accessions":["NG00169"],"publications":["40111762"],"dar_publications":["40111762","41040731","40676597","40533518","40470240","40084664","40033057","39937857","38448474","39251599","36907103","36824752","35005195","35379992","34978149","34874463","34757660","34719765","35750033","33712570","34470617","34417470","34386572","34301914","34041846","33589841"],"researchSTMT":"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.\n","ntResearchSTMT":"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.\n"},{"dar_ID":11841,"expiration_date":"2026-08-08 20:45:40","approval_date":"2025-08-08 20:45:40","initial_approval_date":"2025-08-08 20:45:40","project_name":"Modeling Alzheimer’s disease risk and associated molecular phenotypes","status_name":"Expired","first_name":"Silvana","last_name":"Konermann","institution":"Arc institute","dataset_accessions":["NG00169"],"publications":[],"dar_publications":[],"researchSTMT":"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.","ntResearchSTMT":"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."},{"dar_ID":10141,"expiration_date":"2027-07-29 17:05:33","approval_date":"2026-07-29 17:05:33","initial_approval_date":"2019-12-18 15:47:57","project_name":"Powerful and novel statistical methods to detect genetic variants associated with or putative causal to Alzheimer’s disease","status_name":"Approved","first_name":"Wei","last_name":"Pan","institution":"University of Minnesota","dataset_accessions":["NG00169"],"publications":["40209152","41537459"],"dar_publications":["40209152","41537459","36179104"],"researchSTMT":"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:\nAim1. 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. \nAim 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.\nAim 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. \n","ntResearchSTMT":"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."},{"dar_ID":10085,"expiration_date":"2026-08-12 00:23:32","approval_date":"2025-08-12 00:23:32","initial_approval_date":"2020-10-14 14:30:45","project_name":"Therapeutic target discovery in ADSP data via comprehensive whole-genome analysis incorporating ethnic diversity and systems approaches","status_name":"Expired","first_name":"Sudha","last_name":"Seshadri","institution":"Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX","dataset_accessions":["NG00169"],"publications":["38511601","39428839","40084664"],"dar_publications":["38511601","39428839","40084664","37923804","37322115","37333771"],"researchSTMT":"Objective: Utilize ADSP data sets to identify genes & specific genetic variants that confer risk for or protection from Alzheimer disease.\nAim 1: Using combined WGS/WES across the ADSP Discovery, Disc-Ext, and FUS Phases, including single nucleotide variants, small\ninsertion/deletions, and structural variants. We will:\nAim 1a. Perform whole genome single variant and rare variant case/control association analyses of AD using ADSP and other available data;\nAim 1b. Target protective variant identification via association analysis using selected controls within the ADSP data and performing meta analysis\nacross association results based on selected controls from non-ADSP data sets.\nAim 1c. Perform endophenotype analyses including cognitive function measures, hippocampal volume and circulation beta-amyloid ADSP data in\nsubjects for which these measures are available. Meta analysis will be conducted across ADSP and non-ADSP analysis results.\nAim 2: To leverage ethnically-diverse and admixed populations to identify AD variants we will:\nAim 2a. Estimate and account for global and local ancestry in all analyses;\nAim 2b. Perform admixture mapping in samples of admixed ancestry; and\nAim 2c. Perform ethnicity-specific and trans-ethnic meta-analyses.\nAim 3: To identify putative therapeutic targets through functional characterization of genes and networks via bioinformatics, integrative ‘omics\nanalyses. We will:\nAim 3a. Annotate variants with their functional consequences using bioinformatic tools and publicly available “omics” data.\nAim 3b. Prioritize results, group variants with shared function, and identify key genes functionally related to AD via weighted association analyses and\nnetwork approaches.\nAnalyses will be performed in coordination with the following PIs. Coordination will involve sharing expertise, analysis plans or analysis results. No\nindividual level data will be shared across institutions. Philip De Jager, Columbia University; Eric Boerwinkle & Myriam Fornage, U of Texas Health\nScience Center, Houston; Sudha Seshadri, U of Texas, San Antonio; Ellen Wijsman, U of Washington. William Salerno, Baylor College of Medicine","ntResearchSTMT":"This proposal seeks to analyze existing genetic sequencing data generated as part of the Alzheimer’s Disease Sequencing Project (ADSP) including\nthe ADSP Follow-up Study (FUS) with the goal of identifying genes and specific changes within those genes that either confer risk for Alzheimer’s\nDisease or provide protection from Alzheimer’s Disease. Analytic challenges include analysis of whole genome sequencing data, appropriately\naccounting for population structure across European ancestry, Hispanic, and African American participants, and interpreting results in the context of\nother genomic data available."},{"dar_ID":11020,"expiration_date":"2027-02-03 12:33:46","approval_date":"2026-02-03 12:33:46","initial_approval_date":"2024-04-03 19:21:21","project_name":"Uncovering the causal genetic variants, genes and cell types underlying brain disorders","status_name":"Approved","first_name":"Michael","last_name":"Wainberg","institution":"Sinai Health System","dataset_accessions":["NG00169"],"publications":[],"dar_publications":[],"researchSTMT":"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. \n\nSecond, 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. ","ntResearchSTMT":"We have a comprehensive plan to understand and explain the genetic factors that contribute to Alzheimer's disease. Our approach involves two main steps.\n\nFirst, 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.\n\nSecond, 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."},{"dar_ID":11464,"expiration_date":"2027-06-05 19:45:50","approval_date":"2026-06-05 19:45:50","initial_approval_date":"2025-02-04 15:59:31","project_name":"Developing a novel tau-based polygenic score (TPGS)","status_name":"Approved","first_name":"Jennifer","last_name":"Yokoyama","institution":"University of California, San Francisco","dataset_accessions":["NG00169"],"publications":[],"dar_publications":[],"researchSTMT":"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.\n\nStudy 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.\n\nAnalysis 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.\n\nCollaboration: Data obtained through this application will not be shared with researchers at other institutions.","ntResearchSTMT":"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. "}]}