NG00127 - A longitudinal study of Alzheimer's Disease and other dementing illnesses

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Description

The search for novel risk factors for Alzheimer’s disease relies on access to accurate and deeply phenotyped datasets. The Memory and Aging Project at the Knight-ADRC (Knight ADRC-MAP) collects plasma, CSF, fibroblast, neuroimaging clinical and cognition data longitudinally and autopsied brain samples. We are using multi-tissue (brain, CSF and plasma) multi-omic data (genetics, epigenomics, transcriptomics, proteomics and metabolomics) to identify novel risk and protective variants, create new prediction models and identify drug targets. Knight-ADRC participants have to be at least 45 years old and have no memory problems or mild dementia at the time of enrollment. There is no age at onset criteria for this cohort. Cases had to have a CDR >=0.5 whereas controls had to have a CDR=0 at last assessment. AD definition is based on a combination of both clinical and pathological information if available. Pathologic diagnosis will overrule clinical diagnosis. Autopsy information was provided if available, but it is not a requirement for enrollment. Deep molecular profiling has been generated in this study, through the NeuroGenomics and Informatics Center at Washington University (https://neurogenomics.wustl.edu/).

The data being submitted includes GWAS for 4,496 participants from the Knight-ADRC MAP study. Participants include 1,958 AD cases (58% Females, 56% APOE4+, average age 73), 755 ADRD cases (FTD, DLB, PD, PSP, ALS, other: 51% Females, 38% APOE4+, average age 73), and 1,783 cognitively healthy controls (60% Females, 31% APOE4+, average age 74). Approximately 86% of the samples are self-defined as “White” and 12.5% are self-defined as “Black or African-American”. Genotyping was generated through 10 different genotyping arrays (Infinium CoreExome-24, Infinium Neuro Consortium Array, Infinium Global Screening Array-24, Infinium OmniExpress-24, Illumina Human660W-Quad, Human610-Quad, Illumina Omni1-Quad, Affy UK Biobank Axiom, Infinium OmniExpressExome-8, and Illumina Human1M-Duo). All genotype samples have polygenic risk scores indicating the risk for Alzheimer’s disease (AD), Parkinson’s disease (PD), Frontotemporal dementia (FTD) risk along with CSF Aβ42, phosphorylated tau, and total tau.

Additional information about Knight ADRC datasets available through NIAGADS/DSS can be found on the Knight ADRC Collection page: https://www.niagads.org/knight-adrc-collection

Sample Summary per Data Type

Sample Set	Accession	Data Type	Number of Samples
KnightADRC GWAS	snd10036	Genotyping SNP Array	4,496

Available Filesets

Fileset	Accession	Latest Release	Description
KnightADRC GWAS	fsa000033	NG00127.v1	GWAS and phenotype data
KnightADRC GWAS: Polygenic risk scores	fsa000138	NG00127.v2	Polygenic risk scores

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

Sample Set	Accession Number	Number of Subjects	Number of Samples
KnightADRC GWAS	snd10036	4,496	4,496

Consent Level	Number of Subjects
DS-ADRD-IRB-PUB	4,375
DS-ADRD-IRB-PUB-NPU	2
HMB-IRB-PUB	119

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 NG00127.

For investigators using Charles F. and Joanne Knight Alzheimer’s Disease Research Center (sa000008) data:

This work was supported by grants from the National Institutes of Health (R01AG044546, P01AG003991, RF1AG053303, R01AG058501, U01AG058922, RF1AG058501 and R01AG057777). The recruitment and clinical characterization of research participants at Washington University were supported by NIH P50 AG05681, P01 AG03991, and P01 AG026276. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, and the Departments of Neurology and Psychiatry at Washington University School of Medicine.

We thank the contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, and the Departments of Neurology and Psychiatry at Washington University School of Medicine.

See below for additional dataset specific acknowledgments:

For use of the ADSP-PHC harmonized phenotypes deposited within dataset, ng00067, use the following statement:

The Memory and Aging Project at the Knight-ADRC (Knight-ADRC), supported by NIH grants R01AG064614, R01AG044546, RF1AG053303, RF1AG058501, U01AG058922 and R01AG064877 to Carlos Cruchaga. The recruitment and clinical characterization of research participants at Washington University was supported by NIH grants P30AG066444, P01AG03991, and P01AG026276. Data collection and sharing for this project was supported by NIH grants RF1AG054080, P30AG066462, R01AG064614 and U01AG052410. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, the Neurogenomics and Informatics Center (NGI: https://neurogenomics.wustl.edu/) and the Departments of Neurology and Psychiatry at Washington University School of Medicine.

For use of ng00050 and ng00052, use the following statement:
This work was supported by Pfizer and grants from the National Institutes of Health (R01-AG044546, P01-AG003991), and the Alzheimer's Association (NIRG-11–200110). This research was conducted while Carlos Cruchaga was a recipient of a New Investigator Award in Alzheimer's disease from the American Federation for Aging Research. Carlos Cruchaga is a recipient of a BrightFocus Foundation Alzheimer's Disease Research Grant (A2013359S). The recruitment and clinical characterization of research participants at Washington University were supported by NIHP50 AG05681, P01 AG03991, and P01 AG026276. Some of the samples used in this study were genotyped by the ADGC and GERAD. ADGC is supported by grants from the NIH (#U01AG032984) and GERAD from the Wellcome Trust (GR082604MA) and the Medical Research Council (G0300429). Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen Idec; Bristol-Myers Squibb Company; Eisai; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Medpace; Merck; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Rev December 5, 2013 Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Total number of approved DARs: 20

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:
Approved
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:
March 18, 2025
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:
Ertekin-Taner, Nilufer
Institution:
Mayo Clinic
Project Title:
CLEAR-AD
Date of Approval:
January 6, 2025
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
This U19 aims to bridge these knowledge gaps for discovery and validation of Centrally-linked Longitudinal pEripheral biomARkers of AD (CLEAR-AD) in multi-ethnic populations. CLEAR-AD U19 is based on the premise that AD is a complex disorder in which many biological pathways are disrupted due to multi-omic perturbations, which can be detected in brain and reflected in blood. The specific aims of CLEAR-AD are: 1) To discover CLPMS of the complex and heterogeneous AD pathophysiology and its co-pathologies. 2) To identify longitudinal CLPMS that detect and predict dynamic neuroimaging, fluid biomarker, and clinical changes across AD spectrum. 3) To characterize differences and similarities in CLPMS profiles across NHW, African American (AA) and Latino American (LA) participants to uncover biomarker patterns in multi-ethnic groups. 4) To make these vast resources available to the scientific community to amplify and accelerate its impact. In this U19, we will leverage NIH-funded ADNI, MCSA and ADRC cohorts of >3,700 multi-ethnic participants to generate >20,000 multi-omics measures (Omics Core) that will be processed and integrated with >48,000 harmonized AD cognitive, neuroimaging and fluid endophenotypes (Analytic Core). Using these data, we will identify brain region and cell-type specific CLPMS, which reflect biological subtypes of AD and disease stage (Project 1). We will discover longitudinal changes in CLPMS that predict cognitive and A/T/N/V progression (Project 2). We will define longitudinal cognitive and A/T/N/V changes and CLPMS in URP that are either conserved with NHW or population-specific (Project 3). This U19 will a) Identify the next generation of AD biomarkers with mechanistic insights; b) Establish a precision medicine approach for rigorous multi-omics biomarker discovery and validation in AD; c) Discover molecules that can serve as biomarkers and therapeutic targets; d) Enhance biomarker research in trial-ready multi-ethnic populations; and e) Generate and share a vast and harmonized resource of endophenotype and multi-omics data in NIH-funded cohorts.
Non-Technical Research Use Statement:
There is a clear and immediate need for the discovery of peripheral molecular signatures linked to central disease processes, core and co-pathologies in Alzheimer’s Disease (AD), that will serve as precision medicine blood-based biomarkers for diagnostic, prognostic, theragnostic and therapeutic purposes. AD is a complex disorder in which many biological pathways are disrupted due to multi-omic perturbations, which can be detected in brain and reflected in blood, i.e. centrally-linked peripheral molecular signatures (CLPMS). This U19 will leverage deeply phenotyped, longitudinal NIH-funded multi-ethnic cohorts and cross-disciplinary expertise for multi-omics data generation and its integration with harmonized AD endophenotypes, will share these data and utilize them in integrated U19 projects to discover CLPMS that will serve as the next generation of AD biomarkers.
Investigator:
Ezzati, Ali
Institution:
University of California, Irvine
Project Title:
Advanced Modeling in Alzheimer's Cohorts and Trials
Date of Approval:
January 28, 2025
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
We aim to develop robust, calibrated, and reliable predictive models by utilizing machine learning techniques on a rich dataset of imaging, omics, and phenotypic data. Our approach is twofold:1. We will employ supervised machine learning methods to construct predictive models for defined outcomes such as clinical disease stages, meaningful cognitive decline, and imaging and neuropathologic markers (e.g., amyloid or tau positivity). We acknowledge that a major challenge in implementing predictive models is their integration into real-world settings. Therefore, once validated, we will convert these models into web applications or simplify them into risk scores to improve their usability and practical application. 2. Given the significant heterogeneity in Alzheimer's disease and existing gaps in our mechanistic understanding, we will utilize semi-supervised machine learning to subtype the disease. This method will incorporate genetics, biofluid biomarkers, imaging, and neuropsychological tests. Our methodology offers a significant advantage over the unsupervised machine and deep learning techniques commonly used in the field. By incorporating "partial labeling" and incorporating knowledge of disease states, our semi-supervised approach enables more clinically relevant disease subtyping and results.For this project, we are collaborating with experts from other institutions, including Dr. Christos Davatzikos from the University of Pennsylvania, who is an expert in neuroimaging and machine learning, and Dr. Richard Lipton from Albert Einstein College of Medicine, known for his expertise in epidemiology and the design of trials and cohorts.
Non-Technical Research Use Statement:
Despite the extensive research on Alzheimer's and other dementias, the success rate of randomized clinical trials has been disappointingly low. This is primarily due to the significant clinical variations among patients, which limits our ability to predict their outcomes accurately. To address this challenge, we aim to develop a predictive framework specifically focusing on Alzheimer's and related dementias. We will use several cohorts and will employ machine learning and advanced statistical modeling on various types of data, including demographics, neuropsychological measures, biofluid and imaging biomarkers from existing international cohorts and concluded clinical trials. The framework will enable us to predict disease progression which will facilitate early intervention and personalized care for individuals at risk of developing dementia. Our project seeks to harness the power of machine learning and advanced analytics to improve our understanding of Alzheimer's Disease, enhance prediction accuracy, and ultimately contribute to more effective treatments and interventions.
Investigator:
Greicius, Michael
Institution:
Stanford University School of Medicine
Project Title:
Examining Genetic Associations in Neurodegenerative Diseases
Date of Approval:
December 19, 2024
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
We are studying the effects of rare (minor allele frequency < 5%) genetic variants on the risk of developing late-onset Alzheimer’s Disease (AD). We are interested in variants that have a protective effect in subjects who are at an increased genetic risk, or variants that lead to multiple dementias. Our aim is to identify any genetic variants that are present in the “case” group but not the “AD control” groups for both types of variants. The raw data we receive will be annotated to identify SNP locations and frequencies using existing databases such as 1,000 Genomes. We will filter the data based on genetic models such as compounded heterozygosity, recessive and dominant models to identify different types of variants.
Non-Technical Research Use Statement:
Current genetic understanding of Alzheimer’s Disease (AD) does not fully explain its heritability. The APOE4 allele is a well-established risk factor for the development of Alzheimer’s Disease (AD). However, some individuals who carry APOE4 remain cognitively healthy until advanced ages. Additionally, the cause of mixed dementia pathology development in individuals remains largely unexplained. We aim to identify genetic factors associated with these “protected” and mixed pathology phenotypes.
Investigator:
Hatchwell, Eli
Institution:
Population Bio
Project Title:
Mutational Spectrum of Causal Genes for Neurological/Neurodegenerative Diseases and Endometriosis Identified via High Resolution Genome Wide Copy Number Analysis
Date of Approval:
August 21, 2024
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
While single gene rare variants have been shown to play a significant role in Early-Onset Alzheimer’s Disease (EOAD), their role in Late-Onset (LOAD) has not been emphasised. The gene discovery methodology we have developed at Population Bio allows for unbiased exploration of highly informative genomic variants in any cohort of interest. Our approach is based on ultra-high resolution copy number variant (CNV) analysis. We have invested heavily in such analysis on normal populations. These are used as comparators for cohorts of interest, such as LOAD. In our LOAD work, this analysis generated a list of CNVs which were either absent in the normal populations we studied or else present at significantly higher frequency in the LOAD cohort. Such CNVs are routinely annotated to determine if they overlie known genes and/or regulatory regions. As an example, we have discovered a deletion in 3% of our LOAD cases, which is present in <= 1% of normals. This deletion disrupts a transcription factor binding site in the intron of a gene, which, via GeneHancer, is known to control exon 1 of the gene. The gene in question is novel to LOAD, and is an important metabolic gene, with known biology. It is vital that we validate this finding by analysis of independent LOAD datasets. In addition, we wish to validate other genes discovered in the same manner We have very deep experience of analyzing WGS/WES datasets. Our focus will be to pull out of the available WGS/WES datasets all the variants for the candidate genes of interest. Such variants, including SNVs, indels and CNVs (called using a variety of tools we have experience with) will be analyzed by reference to databases of normal individuals: i.CNVs, by reference to our own internal database but also gnomad (https://gnomad.broadinstitute.org) CNV data and DGV (http://dgv.tcag.ca) ii.SNVs/indels, by reference to gnomad These analyses will allow us to determine whether there exists a mutational burden for our candidate genes of interest in independent LOAD cohorts, and will serve as validation/refutation. The main phenotype of interest will be definitive diagnoses of LOAD, based on neuropathological and clinical cognitive analyses
Non-Technical Research Use Statement:
Most of the common conditions that affect large numbers of the general population have a genetic basis. While progress has been rapid in the field of cancer, the same cannot be said for common, non-cancer, conditions, such as Late-Onset Alzheimer's Disease (LOAD). It is pretty clear now that not all cases of LOAD represent the same disease, in terms of what is the cause. Our approach has been to consider common diseases as collections of rare subgroups, each of which has a specific cause and which, in due course, will have a specific treatment. We have pioneered and implemented a method to rapidly uncover potentially causal genes in common disorders and will use the data generated from this study to strengthen our discoveries, by validating a set of novel candidate genes we have identified in LOAD Our project will allow us to: 1.Define subsets of disease 2.Work with pharmaceutical companies to develop drugs that will specifically target each subset of disease. In some cases, disease progression may be halted by the therapies developed. In some cases, reversal and/or cure may be possible
Investigator:
Hohman, Timothy
Institution:
Vanderbilt University Medical Center
Project Title:
Genetic Drivers of Resilience to Alzheimer's Disease
Date of Approval:
February 4, 2025
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
“Asymptomatic” Alzheimer’s disease (AD) is a phenomenon in which 30% of individuals over age 65 meet criteria for autopsy-confirmed pathological AD (beta-amyloid plaques and tau aggregation) but do not clinically manifest cognitive impairment.1-3 The resilience that underlies asymptomatic AD is marked by both protection from neurodegeneration (brain resilience)4 and preserved cognition (cognitive resilience).Our central hypothesis is that genetic effects allow a subset of individuals to endure extensive AD neuropathology without marked brain atrophy or cognitive impairment. We are uniquely positioned to identify resilience genes by leveraging the Resilience from Alzheimer’s Disease (RAD) database, a local resource in which we have harmonized a validated quantitative phenotype of resilience across 8 large AD cohort studies.Our strong interdisciplinary team represents international leaders in genetics, neuroscience, neuropsychology, neuropathology, and psychometrics who will leverage the infrastructure and rich resources of the AD Genetics Consortium, IGAP, ADSP, and our recently established and harmonzed continuous metric of resilience to fulfill the following aims:Aim 1. Identify and replicate common genetic variants that predict cognitive resilience (preserved cognition) and brain resilience (protection from brain atrophy) in the presence of AD pathology. We hypothesize that common genetic variation will explain variance in resilience above and beyond known predictors like education. Replication analyses will leverage age of onset data from IGAP to demonstrate that resilience loci predict a later age of AD onset.Aim 2. Identify and replicate rare and low-frequency genetic variants that predict cognitive and brain resilience. Rare and low-frequency variants with large effects have been identified in AD case/control studies, providing new insight into the genetic architecture of AD.Aim 3: Identify sex-specific genetic drivers of cognitive and brain resilience to AD pathology. Our preliminary results highlight sex differences in the downstream consequences of AD neuropathology, including sex-specific genetic markers of resilience.
Non-Technical Research Use Statement:
As the population ages, late-onset Alzheimer’s disease (AD) is becoming an increasingly important public health issue. Clinical trials targeted a reducing AD progression have demonstrated that patients continue to decline despite therapeutic intervention. Thus, there is a pressing need for new treatments aimed at novel therapeutic targets. A shift in focus from risk to resilience has tremendous potential to have a major public health impact by highlighting mechanisms that naturally counteract the damaging effects of AD neuropathology. The goal of the present project is to characterize genetic factors that protect the brain from the downstream consequences of AD neuropathology. We will identify both rare and common genetic variants using a robust metric of resilience developed and validated by our research team. The identification of such genetic effects will provide novel targets for therapeutic intervention in AD.
Investigator:
Kamboh, M. Ilyas
Institution:
University of Pittsburgh
Project Title:
Genetics of Alzheimer's Disease and Endophenotypes
Date of Approval:
January 7, 2025
Request status:
Approved
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:
Lee, Jonghun
Institution:
TAKEDA PHARMACEUTICAL COMPANY LTD
Project Title:
Identification of genetic risks and potential target for stratified Alzheimer's disease patient groups
Date of Approval:
June 12, 2024
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
The goal of analyzing ADSP umbrella cohort data is identifying variants, genes and pathways associated to Alzheimer’s disease (AD), and stratifying patients by genetic risks. Following describes procedure.1) Identification and validation of genetic risks The whole genome and whole exome sequencing data will be analyzed to identify genetic variants or genes associated to phenotypes in case-control cohort, such as AD status and Braak stages. Several latest methods will be applied, such as VEP [William McLaren et al, 2016], LOFTEE [Karczewski, 2015] and PEXT scoring [Beryl B.C et al., 2020] for variant annotation and SAIGE-GENE [Wei Zhou et al., 2020] for the association test. The association will be tested for other endophenotypes such as cognitive scores and brain volumes that available in subset of the cohort. Replication and meta-analysis will be conducted on UK biobank and Tohoku medical megabank organization (ToMMo) cohort data. The ToMMo data consists of Japanese cohort so that we can analyze the effect of the variants among multi ethnic groups.2) Patient stratification in ADSP cohort Leveraging the increased sample size, we will stratify the cohort by genetic risks such as ApoE types, or phenotypes such as Braak stages, and compare the effect size of variants or genes among the patient groups. In addition, the genetic risk score (GRS) will be calculated using LDpred2 [Florian Prive, 2020], RapidoPGS [Guillermo Reales, 2020], and PRSice2 [Choi, S.W., 2020], and validated in independent cohorts and compared to available clinical endophenotypes. Last, we will search the effect of the GRS to extensive phenotypes in UK biobank and ToMMo.
Non-Technical Research Use Statement:
The aim of our study is identifying variants or genes potentially causal of the Alzheimer’s disease in whole or subset of patients. To be specific, WES and WGS data will be analyzed to investigate common and rare variants associated with disease status and intermediate phenotypes. In addition, the patients will be stratified and sub-grouped by their genetic or phenotypic characteristics. Last, we will incorporate other large biobanks such as UK biobank or ToMMo to investigate the genetic effects to extensive phenotypes potentially linked to symptoms appearing in sub patient groups.
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:
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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:
Pendergrass, Rion
Institution:
Genentech
Project Title:
Genetic Analyses Using Data from the Alzheimer’s Disease Sequencing Project (ADSP) and related studies
Date of Approval:
October 3, 2024
Request status:
Approved
Research use statements:
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Technical Research Use Statement:
The purpose of our study is to identify novel genetic factors associated with Alzheimer’s Disease, corticobasal degeneration (CBD) and progressive supranuclear palsy (PSP). This includes identifying genetic factors associated with the risk of these conditions, as well as genetic risk factors associated with age-at-onset (AAO) for these conditions. We will also evaluate genetic associations with sub-phenotypes individuals have within these broad disease categories, such as their Braak staging results which provide insights into the level of severity of Alzheimer’s. Thus we are requesting access to the set of genomic Whole Exome and Whole Genome Sequences (WES and WGS) have just been released through the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (DSS NIAGADS). The findings from our genetic association testing have the potential for identification of new therapeutic targets for Alzheimer's Disease, CBD, and PSP. The findings from our studies also have the potential for identification of genetic and phenotypic biomarkers that will be beneficial for subsetting patients in new ways standard genetic epidemiological methods to handle the WGS and WES data. All data will remain anonymized and securely stored, and only those listed on our application and their staff will have access to these data. We will not share any of the individual level data outside of Genentech nor beyond the researchers on our application. We will adhere to all data use agreement stipulations through the DSS NIAGADS. We have a secure computational environment called Rosalind within Genentech where we will use these data. We have IT security staff that constantly monitor all our research computing, assuring safety and privacy of all of our stored data. We will not collaborate with researchers at other institutions.
Non-Technical Research Use Statement:
Genetic variation allows us to understand more of the genetic contribution to risk and protection from diseases such as Alzheimer’s and dementia. This information also allows us to identify important biological contributors to disease for developing effective treatment strategies, and identifying groups of individuals that would benefit most from new treatments. Our exploration of this relationship between genotype and disease traits and outcomes through these datasets will allow us to pursue important new findings for disease treatment.
Investigator:
Safo, Sandra
Institution:
University of Minnesota
Project Title:
Innovative Machine and Deep Learning Analyses of Alzheimer's Disease Omics and Phenotypic Data
Date of Approval:
October 27, 2023
Request status:
Expired
Research use statements:
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Technical Research Use Statement:
AD is the most common cause of dementia and presents a substantial and increasing economic and social burden. Our ability to diagnose and classify AD from cognitive normals (CN), or discriminate among individuals with AD, early mild cognitive impairment [EMCI], or late mild cognitive impairment (LMCI), is essential for the prevention, diagnosis, and treatment of AD. Since individuals with MCI have a high chance of converting to AD, effectively discriminating between those who convert to AD (MCI-C) from those who do not convert (MCINC) is important for early diagnosis of AD. The heterogeneity of AD has motivated attempts to classify distinct subgroups of AD to better inform the underlying physiology. There is evidence to suggest that using data across multiple modalities (e.g. genetics, imaging, metabolomics) has potential to classify AD subgroups better than using single modality. We will apply machine and deep learning methods to gain deeper insight into AD and ADRD pathobiology. We will use datasets that include genomics, genetics, metabolomics, and phenotypic data for this purpose. Data will be divided into discovery and validation sets. On the discovery set, state-of-the-art ML and DL methods for integrative analysis that we and others have developed will be coupled with resampling techniques to determine candidate molecular signatures and pathways discriminating the AD groups considered. Molecular scores will be developed from these candidate biomarkers. The clinical utility of the scores beyond well-known clinical risk factors for AD will be ascertained. We will validate our findings using the validation data. We will visually and quantitatively compare the risk scores across several clinical variables and outcomes. We will use (un)supervised clustering methods to identify molecular clusters, and we will investigate molecular clusters differentiating MCI to AD converters from non-converters. We may explore differences across ethnic subgroups. We will also innovatively apply our multimodal molecular subtyping methods to discover, reproduce, and characterize novel molecular subgroups of AD– this will allow for better risk stratification.
Non-Technical Research Use Statement:
We have been developing novel machine learning (ML) and deep learning (DL) methods that leverage genomics, other omics (including proteomics and metabolomics), clinical and epidemiology data to better understand the pathogenesis of complex diseases. By integrating data from different sources, we have identified molecular signatures contributing to the risk of the development of complex diseases beyond established risk factors. We are proposing to innovatively apply these, and other existing, methods, to data pertaining to Alzheimer’s disease (AD) and Alzheimer’s disease related dementias (ADRD). A deeper understanding of the genes, genetic pathways, and other molecular signatures of AD is essential and could facilitate the identification of potential therapeutic targets for the disease.
Investigator:
Saykin, Andrew
Institution:
Indiana University School of Medicine
Project Title:
Centrally-linked Longitudinal pEripheral biomARkers of AD (CLEAR-AD)
Date of Approval:
November 25, 2024
Request status:
Approved
Research use statements:
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Technical Research Use Statement:
This U19 aims to bridge these knowledge gaps for discovery and validation of Centrally-linked Longitudinal pEripheral biomARkers of AD (CLEAR-AD) in multi-ethnic populations. CLEAR-AD U19 is based on the premise that AD is a complex disorder in which many biological pathways are disrupted due to multi-omic perturbations, which can be detected in brain and reflected in blood. The specific aims of CLEAR-AD are: 1) To discover CLPMS of the complex and heterogeneous AD pathophysiology and its co-pathologies. 2) To identify longitudinal CLPMS that detect and predict dynamic neuroimaging, fluid biomarker, and clinical changes across AD spectrum. 3) To characterize differences and similarities in CLPMS profiles across NHW, African American (AA) and Latino American (LA) participants to uncover biomarker patterns in multi-ethnic groups. 4) To make these vast resources available to the scientific community to amplify and accelerate its impact. In this U19, we will leverage NIH-funded ADNI, MCSA and ADRC cohorts of >3,700 multi-ethnic participants to generate >20,000 multi-omics measures (Omics Core) that will be processed and integrated with >48,000 harmonized AD cognitive, neuroimaging and fluid endophenotypes (Analytic Core). Using these data, we will identify brain region and cell-type specific CLPMS, which reflect biological subtypes of AD and disease stage (Project 1). We will discover longitudinal changes in CLPMS that predict cognitive and A/T/N/V progression (Project 2). We will define longitudinal cognitive and A/T/N/V changes and CLPMS in URP that are either conserved with NHW or population-specific (Project 3). This U19 will a) Identify the next generation of AD biomarkers with mechanistic insights; b) Establish a precision medicine approach for rigorous multi-omics biomarker discovery and validation in AD; c) Discover molecules that can serve as biomarkers and therapeutic targets; d) Enhance biomarker research in trial-ready multi-ethnic populations; and e) Generate and share a vast and harmonized resource of endophenotype and multi-omics data in NIH-funded cohorts.
Non-Technical Research Use Statement:
There is a clear and immediate need for the discovery of peripheral molecular signatures linked to central disease processes, core and co-pathologies in Alzheimer’s Disease (AD), that will serve as precision medicine blood-based biomarkers for diagnostic, prognostic, theragnostic and therapeutic purposes. AD is a complex disorder in which many biological pathways are disrupted due to multi-omic perturbations, which can be detected in brain and reflected in blood, i.e. centrally-linked peripheral molecular signatures (CLPMS). This U19 will leverage deeply phenotyped, longitudinal NIH-funded multi-ethnic cohorts and cross-disciplinary expertise for multi-omics data generation and its integration with harmonized AD endophenotypes, will share these data and utilize them in integrated U19 projects to discover CLPMS that will serve as the next generation of AD biomarkers.
Investigator:
Wainberg, Michael
Institution:
Sinai Health System
Project Title:
Uncovering the causal genetic variants, genes and cell types underlying brain disorders
Date of Approval:
September 5, 2024
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:
Xiao, Peng
Institution:
University of Nebrask Medical Center
Project Title:
Uncovering the genetic basis of Alzheimer's Diseases by integrating GWAS with multiomics approaches across different ethnicities
Date of Approval:
August 9, 2024
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
ObjectivesIn this study we aim to understand the system level understanding of Alzheimer's disease (AD) by integrating GWAS with robust multiomics datasets across diverse ethnic groups and harmonization of the results to include associated genes and pathways to understand underlying disease mechanisms and to inform our understanding of biological continuum of the diseases. Investigating AD associated variants as quantitative trait loci for epigenetic, transcriptomic, and proteomic layers to explore how variants in genes perturb pathways leading to AD.Study designWe will comprehensively examine the genetic architecture of Alzheimer's diseases based on different races (Caucasians, Latinos, Asians and Africans) GWAS data from publicly available datasets. We request access to as many datasets available in NIAGADS and other repositories like EADB-consortium, IGAP and we also have requested access to datasets through our literature search from corresponding authors for Asian cohorts. We will perform meta-analysis at two levels for GWAS datasets and find genome wide significant loci (GWS). Mendelian randomization analyses will be adapted to multi-omics setting through analyzing QTLs and GWS. We will perform correlation and enrichment analysis for significant findings from different omics layers.Analysis PlanGenome wide meta-analysis across different races to identify new loci and functional pathways influencing AD. Find genes most likely to be responsible for association signal with AD at each loci by applying mendelian randomization (MR) method. We plan to use MR method to combine multiomics (GWAS, eqtl, mqtl, aqtl and pqtl).
Non-Technical Research Use Statement:
To our knowledge our findings reveal crosstalk between epigenetic, genomic, and transcriptomic determinants of AD pathogenesis and define catalogues of candidate genes. In addition, rare or population specific common variants can be identified thus genes with underlying genetic support for an association with AD are likely to encode successful drug targets in clinical development. This should further lead to patient stratification.
Investigator:
Zhang, Haiyang
Institution:
Vivid Genomics
Project Title:
Validation and optimization of Alzheimer's Disease phenotypes prediction using machine learning enabled polygenic risk models
Date of Approval:
March 28, 2023
Request status:
Expired
Research use statements:
Show statements
Technical Research Use Statement:
Analyzing postmortem phenotype and genomics data from ~1100 human brain samples with machine learning, Vivid Genomics, Inc., has developed prototype genetic biomarker assays that predict the presence of amyloid plaques, Lewy body pathology, cerebral amyloid angiopathy, and rate of cognitive decline. The objective is to increase subject numbers with similar data available through NIAGADS and NACC, along with datasets from several individual academic centers, to further optimize and validate our assays for neurodegenerative/cerebrovascular lesion types including tau, TDP-43, hippocampal sclerosis and microinfarcts, and for predicting rate of cognitive decline. NIAGADS datasets requested are NG00067 (including the newly released data which is new version 9 for dataset NG00067), NG00119, NG00117 and NG00127; data use limitations from these do not exclude our proposed usage. We are targeting >3000 subjects in total to be used for the validation of our models. We will focus on SNP selection and test the effects of different analysis strategies: 1) changing SNP p-value cutoffs 2) using LD-filtered representative SNPs with full genome coverage 3) testing the value of stratifying by APOE genotype 4) determining if it is better to add other covariates including age and sex. A fraction of the genetic data (~30%) will be withheld for validation. Optimization is defined as an area under the curve (AUC) of 80% and positive predictive value (PPV) of 80%, as well as R2 >0.75 for all assays. Values within 10% of this will be considered a successful validation. Through these assays, this project will benefit those suffering from Alzheimer’s disease and other neurodegenerative disorders by increasing clinical trial efficiency through more precise subject selection and/or stratification.
Non-Technical Research Use Statement:
Vivid Genomics is dedicated to developing genetic tests, typically done from DNA obtained from blood, that will predict, for any given older person, the likelihood that they have, or might develop when they become old, the characteristic brain changes of Alzheimer’s disease as well as other brain changes that affect thinking in older people. These changes include amyloid or senile plaques, tangles or tau, amyloid angiopathy, Lewy bodies, TDP-43 pathology, hippocampal sclerosis and brain infarcts (strokes). The objective of this study is to improve upon initial tests developed by Vivid, and to also develop genetic tests to predict the rate at which older people’s thinking ability decreases over time. To do this, Vivid Genomics requests human subject DNA analysis data stored at NIAGADS. Through these new genetic tests, Vivid hopes to benefit those suffering from Alzheimer’s disease and other brain diseases of aging by allowing better selection of subjects for clinical trials of these diseases, which would increase the chances of clinical trials finding useful new treatments.
Investigator:
Zhao, Jinying
Institution:
University of Florida
Project Title:
Identifying novel biomarkers for human complex diseases using an integrated multi-omics approach
Date of Approval:
November 21, 2023
Request status:
Expired
Research use statements:
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Technical Research Use Statement:
GWAS, WES and WGS have identified many genes associated with Alzheimer’s Dementia (AD) and its related traits. However, the identified genes thus far collectively explain only a small proportion of disease heritability, suggesting that more genes remained to be identified. Moreover, there is a clear gender and ethnic disparity for AD susceptibility, but little research has been done to identify gender- and ethnic-specific variants associated with AD. Of the many challenges for deciphering AD pathology, lacking of efficient and power statistical methods for genetic association mapping and causal inference represents a major bottleneck. To tackle this challenge, we have developed a set of novel statistical and bioinformatics approaches for genetic association mapping and multi-omics causation inference in large-scale ethnicity-specific epidemiological studies. The goal of this project is to leverage the multi-omics and clinical data archived by the ADSP, ADNI, ADGC as well as other AD-related data repositories to identify novel genes and molecular markers for AD. Specifically, we will (1) validate our novel methods for identifying novel risk and protective genomic variants and multi-omics causal pathways of AD; (2) identify novel ethnicity- and gender-specific genes and molecular causal pathways of AD. We will share our results, statistical methods and computational software with the scientific community.
Non-Technical Research Use Statement:
Although many genes have been associated with Alzheimer’s Dementia (AD), these genes altogether explain only a small fraction of disease etiology, suggesting more genes remained to be identified. Of the many challenges for deciphering AD pathology, lacking of power statistical methods represents a major bottleneck. To tackle this challenge, we have developed a set of novel statistical and bioinformatics approaches for genetic association mapping and multi-omics causation inference in large-scale ethnicity-specific epidemiological studies. The goal of this project is to leverage the rich genetic and other omic data along with clinical data archived by the ADSP, ADNI, ADGC as well as other AD-related data repositories to identify novel genes and molecular markers for AD. Such results will enhance our understanding of AD pathogenesis and may also serve as biomarkers for early diagnosis and therapeutic targets.
Investigator:
Zhao, Zhongming
Institution:
University of Texas Health Science Center at Houston
Project Title:
AIM-AI: an Actionable, Integrated and Multiscale genetic map of Alzheimer's disease via deep learning
Date of Approval:
March 27, 2025
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
Objectives: The objective of our study is to advance our understanding of the genetic basis of Alzheimer’s Disease (AD) through the analysis of comprehensive genomic datasets such as Whole Exome Sequencing (WES), Whole Genome Sequencing (WGS), single-nuclei RNA sequencing, and Genome-Wide Association Studies (GWAS), as well as the related phenotype. We aim to identify genetic variants that are integral to the development and progression of AD.Study Design: Our approach involves a detailed multi-omics analysis focusing on both coding and non-coding regions within these datasets. We will develop new analytical variables from existing data, ensuring that our research adheres to the established data use limitations and contributes meaningfully to the field of genetic research in AD.Analysis Plan: The plan centers on investigating the correlation between genetic variants and AD, exploring how these variants influence the disease at a genetic level. We will employ cutting-edge computational methods to analyze interactions between these genetic markers and their potential role in AD pathogenesis. The integration of data from multiple sources will be carefully executed to maintain compliance with data use agreements, emphasizing the scientific exploration of AD.
Non-Technical Research Use Statement:
Our research is dedicated to unraveling the genetic components of Alzheimer’s Disease. By analyzing genetic sequences and variations through various genomic datasets, we seek to deepen the scientific understanding of how these genetic elements contribute to AD. The outcomes of this study will be shared with the public, enhancing general knowledge of Alzheimer’s Disease and supporting the global research community in its ongoing efforts to decode this complex condition.
Investigator:
Zhou, Weichen
Institution:
University of Michigan
Project Title:
Explore the functional impact of transposable elements in Alzheimer’s disease and related dementias
Date of Approval:
May 9, 2024
Request status:
Expired
Research use statements:
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Technical Research Use Statement:
Explore somatic transposable elements and their Alzheimer's disease-related patterns using genomic and phenotypic data from large cohorts:In order to explore the impact of the transposable element in Alzheimer's disease, we propose to conduct a systematic survey in the available large cohorts. The ADSP dataset in NIAGAlzheimer's diseaseS (Accession No. NG00067) includes 16,906 whole-genome sequences and 20,504 whole-exome sequences for case-control and family-based studies of Alzheimer's disease from diverse populations, which is a perfect resource to leverage in this project. Under the support of the Michigan Alzheimer's Disease Center, we will request to access NIAGADS. To detect somatic transposable elements in the ADSP dataset, we will employ established computational pipelines to resolve the transposable elements in the sequencing data, MELT and xTEA for WGS and SCRAMble for WES, respectively. Parameters in these tools, for instance, the calling threshold of supporting reads, will be adjusted accordingly to cooperate with the detection of somatic transposable elements in cells at low frequency. To exclude potential germline transposable elements, we will leverage a master set of polymorphic transposable elements from diverse populations, which are based on our previous projects at the Human Genome Structural Variation Consortium, and the case-control information provided by ADSP. We aim to summarize a spectrum of somatic transposable elements that would be Alzheimer's disease-relevant along with various clinical and phenotypic information. To build Alzheimer's disease-related genetic patterns we will implement Mutect2 (GATK) and Strelka2 to discover SNVs from WGS and WES data and link them with transposable elements in the same haplotype. After obtaining this set of patterns, we will collect phenotypic information from the ADSP dataset to conduct family-based associated analysis and gene-burden analysis. RegulomeDB will be used to annotate the effects of non-coding functional impact and regulatory changes for these Alzheimer's disease-related patterns.
Non-Technical Research Use Statement:
It seeks to explore the connection between the somatic transposable elements in the human genome and Alzheimer’s disease and related dementias. It will leverage large-scale datasets to extensively explore the genome-wide transposable elements and then stratify Alzheimer’s disease-relevant ones by using the rich clinical information from the cohorts. Further analysis pipelines will be built based on the results of the proposed project to investigate the functional impact of these transposable elements on Alzheimer’s disease and would improve the understanding of genetic causes of Alzheimer’s disease and related dementias.
Investigator:
Zhou, Wenyu
Institution:
Teal Omics. Inc
Project Title:
Understanding CNS diseases through organ specific aging biomarkers
Date of Approval:
February 27, 2025
Request status:
Approved
Research use statements:
Show statements
Technical Research Use Statement:
Can blood-based protein biomarkers of aging predict functional decline in organ health and in systemic health? We have developed organ-specific aging models derived from plasma proteins, and previously found that the blood proteome can be used to monitor organ health and aging in smaller cohorts (Oh, et., al., Nature 624, 164–172 (2023)). We aim to test this hypothesis at scale with diversified datasets. Aim 1. Validate our approach to modeling aging and brain health with NIAGADS cohorts. Approach: we will use protein biomarker data from cohorts to train machine learning models of organ aging. We will employ machine learning best practice for data curation, data augmentation, data normalization, and training/testing, to evaluate whether we can reproducibly estimate brain aging in diversified healthy cohorts and disease cohorts. Aim 2. Test the effects of brain aging on future disease risk and functional decline. We will focus particularly on the aging brain to investigate the relationship between organ aging and cognitive decline. Approach: we will evaluate the relationships between our model aging predictions and aging, diseases, and clinical phenotypes, such as mortality and lab and physical assessments. This is primarily done by first calculating the difference between a sample’s chronological age and its predicted organ age. This organ age residual is then tested for association with phenotypes of interest, such as Alzheimer’s Disease status or future risk of cognitive decline.
Non-Technical Research Use Statement:
As we age, our risk of getting sick increases, but did you know that everyone ages differently? Some people's bodies deteriorate faster than others, and we don't fully understand why. By studying how our bodies change with age, we aim to identify which organs are aging the most and find ways to improve quality of life tailored to each individual's needs. We want to understand how people age differently, both as individuals and as a population on organ specific levels. By analyzing molecules in blood, we aim to: - Identify how aging affects people uniquely - Link aging patterns to diseases and lifespan - Use advanced computer models to connect blood changes to organ function Our research can lead to: - Personalized patient care - New discoveries for medicines - Improved understanding of aging and age-related diseases We'll use a large, diverse dataset to validate our approach and gain valuable insights into the aging process. By unlocking these secrets, we can work towards a healthier future for everyone.

Total number of samples: 4,496

Female 2,597 57.8 %

Male 1,886 41.9 %

Other
Case	1	0.0%
Other	527	11.7%

NG00127 – A longitudinal study of Alzheimer’s Disease and other dementing illnesses – KnightADRC GWAS

Overview

Description

Sample Summary per Data Type

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Sample information

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Acknowledgement

Acknowledgment statement for any data distributed by NIAGADS:

For investigators using any data from this dataset:

For investigators using Charles F. and Joanne Knight Alzheimer’s Disease Research Center (sa000008) data:

Related Publications

Approved Users

Total number of samples: 4,496

AD
Control	1,783	39.7%
Case	1,958	43.5%