Description

This dataset includes Compressed Sequence Alignment files (CRAMs) mapped to GRCh38 and GATK-called gVCFs from the ADSP and ADNI studies. These data were called by the Genome Center for Alzheimer’s Disease (GCAD) using VCPA 1.0, a functionally equivalent CCDG/TOPMed pipeline. GCAD processed a total of 4789 whole genomes, including, 876 ADSP Family Discovery and Discovery Extension samples, 3104 ADSP Case Control Extension samples, and 809 ADNI samples. The next data release will include the ADSP quality control checked GATK joint called VCF containing all 4789 whole genomes.

Sample Summary per Data Type

WGS CRAMsWGS gVCFsGATK Called Genotypes
ADSP Discovery
snd10000
n = 580n = 580n = NA
ADSP Extension
snd10001
n = 3400n = 3400n = NA
ADNI-WGS-1
snd10002
n = 809n = 809n = NA

Available Filesets

NameAccessionVersion/DateDescription/What’s New
ADSP Discovery WGS CRAMs/GATK gVCFsnd10000VCPA1.0/2018.07.30Mapped to GRCh38
ADSP Extension WGS CRAMs/ GATK gVCFssnd10001 VCPA1.0/2018.07.30Mapped to GRCh38
ADNI-WGS-1 CRAMs/ GATK gVCFssnd10002 VCPA1.0/2018.07.30Mapped to GRCh38
ADSP/ADNI QC MetricsNA VCPA1.0/2018.07.30Sequencing Data Quality Control Metrics
ADSP/ADNI Phenotypes/Pedigreesdnd000012018.07.30Phenotypes and Pedigree structures for all whole-genome sequenced subjects
Sample SetAccession NumberNumber of Subjects
ADSP Discoverysnd10000574
ADSP Extensionsnd100013367
ADNI-WGS-1snd10002809
Consent LevelNumber of Subjects
DS-ADRDAGE-IRB-PUB214
DS-ADRD-IRB-PUB98
DS-ADRDMEM-IRB-PUB-NPU20
DS-AGEADLT-IRB-PUB173
DS-AGEADLT-IRB-PUB-NPU77
DS-AGEBRMEM-IRB-PUB-GSO7
DS-DEMND-IRB-PUB 186
DS-DEMND-IRB-PUB-NPU91
DS-ND-IRB-PUB61
DS-ND-IRB-PUB-MDS 4
DS-ND-IRB-PUB-NPU64
DS-NEURO-IRB-PUB168
DS-NEURO-IRB-PUB-NPU1
GRU-IRB-PUB3076
GRU-IRB-PUB-NPU36
HMB-IRB-PUB250
HMB-IRB-PUB-GSO102
HMB-IRB-PUB-NPU 122

Visit the Data Use Limitations page for definitions of the consent levels above.

  • Investigator:
    Adanve, Bertrand
    Institution:
    Genetic Intelligence, Inc
    Project Title:
    AI-based platform to identify causal, genetically-defined therapeutic targets for Alzheimer's disease
    Date of Approval:
    November 8, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Genetic Intelligence (GI) will analyze de-identified whole genome sequences (WGS) from Alzheimer’s disease (AD) and healthy patients using it’s AI-based platform to discover novel causal genes and variants for AD. GI has previously obtained a NSF Phase I SBIR grant (#1819331) to develop and validate it’s computational platform using Amyotrophic Lateral Sclerosis (ALS) WGS (obtained from our partner the New York Genome Center) as a proof-of-principle. GI successfully validated the platform by rediscovering known ALS genes SOD1 and C9orf72, as well as discovering new ALS genes including STMN2, which was independently discovered by two labs this year using experimental approaches. We are currently validating two additional ALS targets experimentally. We have recently submitted a Phase I SBIR grant to NIA to build on our success with ALS, and focus on applying our platform to uncover novel genes and variants in AD using a similar study design as ALS. Objective 1: Obtain de-identified WGS data (e.g., ADSP and ADNI) from NIAGADS and preprocess them for input into GI’s computational pipeline. Objective 2: Run the AD WGS through our in-house genetic background dissector tool, Cato, that uses several machine learning systems to stratify genomes before analysis to avoid spurious results arising from differences in case and control backgrounds. Objective 3: Rediscover known AD genes. Chromosomes containing known AD genes (e.g., APOE, PSEN1 and PSEN2) will be input into our causal gene discovery platform, Bergspitze, to confirm if it can rediscover the known AD genes. The output from Bergspitze will be input into Franklin, GI’s interpretation module that provides a coherent etiology model for the disease with awareness of alternative etiologies advanced in the literature. Objective 4: Discovery of new AD genes. Run the full genomes from all cluster groups and ancestry cohorts in Bergspitze and Franklin to identify and prioritize new AD genes and variants. There are no plans to collaborate with other institutions.
    Non-Technical Research Use Statement:
    Alzheimer’s disease (AD) is the leading cause of dementia affecting over 44 million people worldwide, and for which currently there are no effective prevention or cures. Enormous efforts and funding have been put into the discovery of the root cause of AD so as to find an effective treatment, but even with advancing genetic sequencing and analysis technology, no smoking gun has been found yet. Part of the issue has been scientific focus on analyzing the human exome, the ~1% of the human genome that codes for proteins, due to ease of analysis and lower noise in the data. However, this methodology precludes ~99% of the rest of the human genome, which harbors critical regulatory features that affect many of the processes in the body. Genetic Intelligence (GI) aims to solve this problem using its novel whole genome analysis platform that blends advanced genetic principles with state-of-the-art machine learning to identify causal disease targets. This information can then be used to create new drug candidates that are not only effective and precise, but also affordable.
  • Investigator:
    Boerwinkle, Eric
    Institution:
    University of Texas Health Science Center at Houston
    Project Title:
    ADSP Data Analysis
    Date of Approval:
    March 5, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Dr. Boerwinkle’s group is involved in all aspects of the ADSP including study design, data gathering and data analysis. Permissions are necessary for both the array genotype, DNA sequence and all available phenotype data. This availability and ensuing data access will be used for data processing and data analysis to identify novel AD risk raising and protective loci.
    Non-Technical Research Use Statement:
    Dr. Boerwinkle’s group is involved in all aspects of the ADSP including study design, data gathering and data analysis. Permissions are necessary for both the array genotype, DNA sequence and all available phenotype data. This availability and ensuing data access will be used for data processing and data analysis to identify novel AD risk raising and protective loci.
  • Investigator:
    Bras, Jose
    Institution:
    Van Andel Research Institute
    Project Title:
    Rare Variants in Alzheimer’s Disease and Other Dementias
    Date of Approval:
    May 13, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Objectives To identify genetic variants that are overrepresented in sporadic dementias when compared with controls. To identify genetic variants that are found uniquely in apparently sporadic dementia cases. To determine if rare variants identified in our cohorts of neurodegenerative disease are present in the ADSP controls. Study Design We have performed exome-sequencing in over 3,000 samples from a variety of neurodegenerative dementias. These data were generated using Illumina sequencing and called using GATK’s Best Practices v3. In these samples, we have identified genetic variants that have either much lower frequency in controls and in publicly available databases of genetic data (gnomAD), or that are absent from these cohorts. The proposed study design is largely a case-control study in ADSP data to replicate our findings as well as a simple lookup for rarer variants in cases and controls, where sample size isn’t enough to perform meaningful associations. We will perform single variant and gene-based associations using standard methods (fisher test/logistic regression and SKAT-O) using gender, age and principal components as covariates. These tests are either implemented in PLINK or can be performed in R. To allow us to dissect the associations between genetic variants and phenotype we will require access to gender, age at onset, age at death (where available), Braak staging and CERAD scores for all cases in ADSP. Funding Funding for the study is currently from Van Andel Research Institute’s internal funds.
    Non-Technical Research Use Statement:
    The main objective of the study is to identify genetic variants that cause or predispose to neurodegeneration. To accomplish this, we will analyze data previously generated for a variety of these conditions and use data from ADSP to replicate findings and improve our statistical power to detect these associations with disease. The identification of genetic variants, even if rare, that have a strong impact on dementia phenotypes will be of significant importance in advancing our understanding of disease biology. These variants will also be candidate targets for future diagnostic or therapeutic approaches for these diseases.
  • Investigator:
    Carter, Gregory
    Institution:
    The Jackson Laboratory
    Project Title:
    Prioritization of Genetic Variants Contributing to Late-Onset Alzheimer’s Disease
    Date of Approval:
    December 3, 2018
    Request status:
    Expired
    Research use statements:
    Show statements
    Technical Research Use Statement:
    The goal of this collaborative project is to identify and prioritize genetic variations that contribute to late-onset Alzheimer’s disease (AD), with a particular focus on developing mouse models to understand the biology and of and develop new treatments for AD. We use a variety of approaches to analyze high-throughput genomic and genetic data, including statistical methods for identifying causal variants and genetic interaction networks, as well as epigenetic analyses of cellular regulation. By investigating and quantifying the effects of these mutations individually and interactively, we hope to begin to understand the biology of AD and unravel the complexity of AD genetic risk. Our analysis will use the human genetic data in the Alzheimer’s disease sequencing project (ADSP), from which we will identify high-priority candidate genes and variants for further study in mouse models. Thus the goals of this work are twofold: to understand how brain irregularities progress into full AD, and provide the research community with a valuable mouse model for further studies and therapeutic testing. The described research will pilot novel analytical tools for the study of the biology of AD and provide insights into the genetics of neurodegeneration. We will use independent evidence to identify candidates with putative functional roles. Transcriptome data from Alzheimer’s studies will be used to identify candidate genes and ENCODE and other regulatory data will be used to identify putative regulatory regions. Variants will be ranked based on computationally predicted mutation severity and differential expression in AD. Candidates in regulatory regions will be prioritized based on expression differences in nearby genes. Finally, functional data sources such as Gene Ontology, Allen Brain Atlas, and mouse phenotypes will be used to determine the potential role in neurodegeneration. The result will be a list of candidate variants prioritized for study in mouse models. Since these data will be integrated with a focus on the role of individual genes generated from a population-wide analysis, we do not foresee creating any additional risk to individual participants.
    Non-Technical Research Use Statement:
    While many genetic loci have been identified as contributing to the risk of late-onset Alzheimer’s disease, the biological underpinnings and interdependence of these mutations are generally poorly understood. By investigating and quantifying the effects of these mutations individually and interactively, we hope to begin to unravel the complexity of genetic risk. Data from the Alzheimer’s Disease Sequencing Project will be essential in this approach, allowing us to identify the best candidate genes and perform computational analysis to design advanced mouse models of AD. These models will be an experimental basis for understanding the biology of Alzheimer’s and performing early-stage testing of candidate therapeutics.
  • Investigator:
    Cruchaga, Carlos
    Institution:
    Washington University School of Medicine
    Project Title:
    The Familial Alzheimer Sequencing (FASe) Project
    Date of Approval:
    January 23, 2019
    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 UTAH). 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:
    Curtis, David
    Institution:
    University College London
    Project Title:
    Developing improved methods to analyse next generation sequence data
    Date of Approval:
    September 13, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    The objectives are to identify and characterise genes and genetic variants which increase or decrease the risk of developing Alzheimer's disease. Exome sequenced and whole genome sequenced cases and controls will be analysed. The predicted function of DNA variants will be obtained using software such as VEP, PolyPhen, SIFT. Weighted burden analysis will be performed wherein variants are given higher weights if they are predicted to have a major effect on protein function and/or if they are rare. For each gene, in each subject the weights for the DNA variants possessed by that subject will be summed to produce a score. The scores between cases and controls will be compared using logistic regression and incorporating relevant covariates such as sex, age, principal components. If scores are on average higher in cases this indicates that damage to the gene increases risk of Alzheimer's disease. If scores are higher in controls this indicates that damage to the gene reduces risk. Sets of genes will also be analysed in a similar way. The method has been applied to a smaller ADSP dataset: https://www.biorxiv.org/content/10.1101/596007v1
    Non-Technical Research Use Statement:
    We will analyse whether variants in DNA can interfere with the functioning of particular genes and either increase or decrease the risk of developing Alzheimer's disease. We will examine all the variants in a gene observed in large samples of people with and without Alzheimer's disease to see if variants are more commonly seen in one or other group. We will weight the variants to that more attention is paid to those which are rare and those which are predicted to have a major effect on the functioning of the gene. If we see more variants in the people with Alzheimer's disease then this suggests that damaging that gene could increase risk of illness. If the people without disease have more variants in a gene then that could suggest that damaging that gene would actually protect against Alzheimer's disease. Understanding these effects will ultimately assist in the development of methods to treat or prevent the disease.
  • Investigator:
    DeStefano, Anita
    Institution:
    Boston University
    Project Title:
    Therapeutic target discovery in ADSP data via comprehensive whole-genome analysis incorporating ethnic diversity and systems approaches
    Date of Approval:
    March 4, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Objective: Utilize ADSP data sets to identify genes & specific genetic variants that confer risk for or protection from Alzheimer disease. Aim 1: Using combined WGS/WES across the ADSP Discovery, Disc-Ext, and FUS Phases, including single nucleotide variants, small insertion/deletions, and structural variants. We will: Aim 1a. Perform whole genome single variant and rare variant case/control association analyses of AD using ADSP and other available data; Aim 1b. Target protective variant identification via association analysis using selected controls within the ADSP data and performing meta analysis across association results based on selected controls from non-ADSP data sets. Aim 1c. Perform endophenotype analyses including cognitive function measures, hippocampal volume and circulation beta-amyloid ADSP data in subjects for which these measures are available. Meta analysis will be conducted across ADSP and non-ADSP analysis results. Aim 2: To leverage ethnically-diverse and admixed populations to identify AD variants we will: Aim 2a. Estimate and account for global and local ancestry in all analyses; Aim 2b. Perform admixture mapping in samples of admixed ancestry; and Aim 2c. Perform ethnicity-specific and trans-ethnic meta-analyses. Aim 3: To identify putative therapeutic targets through functional characterization of genes and networks via bioinformatics, integrative ‘omics analyses. We will: Aim 3a. Annotate variants with their functional consequences using bioinformatic tools and publicly available “omics” data. Aim 3b. Prioritize results, group variants with shared function, and identify key genes functionally related to AD via weighted association analyses and network approaches. Analyses will be performed in coordination with the following PIs. Coordination will involve sharing expertise, analysis plans or analysis results. No individual level data will be shared across institutions. Philip De Jager, Columbia University; Eric Boerwinkle & Myriam Fornage, U of Texas Health Science Center, Houston; Sudha Seshadri, U of Texas, San Antonio; Ellen Wijsman, U of Washington. William Salerno, Baylor College of Medicine.
    Non-Technical Research Use Statement:
    This proposal seeks to analyze existing genetic sequencing data generated as part of the Alzheimer’s Disease Sequencing Project (ADSP) including the ADSP Follow-up Study (FUS) with the goal of identifying genes and specific changes within those genes that either confer risk for Alzheimer’s Disease or provide protection from Alzheimer’s Disease. Analytic challenges include analysis of whole genome sequencing data, appropriately accounting for population structure across European ancestry, Hispanic, and African American participants, and interpreting results in the context of other genomic data available.
  • Investigator:
    Ebbert, Mark
    Institution:
    Mayo Clinic
    Project Title:
    Resolving mutations in challenging genomic regions to test association with disease phenotypes
    Date of Approval:
    September 21, 2018
    Request status:
    Expired
    Research use statements:
    Show statements
    Technical Research Use Statement:
    A majority of the human genome has been well characterized through the initial Human Genome Project and numerous large-scale sequencing studies such as the 1000 Genomes Project, Alzheimer's Disease Neuroimaging Initiative (ADNI), Alzheimer’s Disease Sequencing Project, and others. There are, however, many genome regions that are challenging to characterize using standard approaches that are important to human health and disease. We intend to (1) develop and test new methods to characterize mutations in these regions, and (2) test associations between these mutations and disease phenotypes. Data from the ADSP may be combined with other datasets, such as the Alzheimer's Disease Neuroimaging Initiative. All appropriate precautions will be taken to verify proper population stratification and eliminate any sample redundancy. Combining these data will not increase risk to participants, as all individual-level data will remain confidential. We may also use portions of the ADSP data as controls for other diseases such as amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), though only in situations that do not violate genetic or data-use principles. Specifically, data that where participants consented for use only within Alzheimer's disease studies will not be used for any purpose outside Alzheimer's disease research.
    Non-Technical Research Use Statement:
    Many regions of the human genome present challenges that prohibit scientists from discovering potential disease-causing mutations. We are developing methods to characterize mutations in these regions to identify new genes involved in disease.
  • Investigator:
    Farrer, Lindsay
    Institution:
    Boston University
    Project Title:
    ADSP Data Analysis
    Date of Approval:
    October 29, 2018
    Request status:
    Expired
    Research use statements:
    Show statements
    Technical Research Use Statement:
    As part of the Consortium for Alzheimers Sequence Analysis (CASA: NIA grant UF1-AG047133), we plan to analyze whole exome and whole genome sequence data generated from subjects with Alzheimer's disease (AD) and elderly normal controls. These data will be generated by the National Human Genome Institute Large-Scale Sequence Program. The goal of the planned analyses is to identify genes that have alleles that protect against or increase susceptibility to AD. We will evaluate variants detected in the sequence data for association with AD to identify protective and susceptibility alleles using the whole exome case-control data. We will also evaluate sequence data from multiplex AD families to identify variants associated with AD risk and protection, and evaluate variant co-segregation with AD. The family data will be whole genome data. The family-based data will be used to inform the cases control analysis and visa versa. We also will focus on structural variants (insertion-deletions, copy number variants, and chromosomal rearrangements). Evaluation of structural variants will involve both whole genome and whole exome data. Structural variants will be analyzed with single nucelotide variants detected and analyzed in the case-control and family-based data.
    Non-Technical Research Use Statement:
    We are attempting to identify all the inherited elements that contribute to Alzheimer's disease risk. To do this we will analyze DNA sequence data from subjects with Alzheimer's disease and elderly subjects who are cognitively normal. The sequence data from these 2 groups will be compared to identify differences that contribute to the risk of developing Alzheimer's disease of that protect against Alzheimer's disease. These DNA differences can be at a single site in the genetic code, or can span multiple sites, changing the copy number of DNA sequences. Both types of genetic variants will be examined.
  • Investigator:
    Goate, Alison
    Institution:
    Icahn School of Medicine at Mount Sinai
    Project Title:
    Study of Alzheimer's disease and other dementias (e.g. frontotemporal dementia) and related phenotypes
    Date of Approval:
    December 2, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Alzheimer's disease (AD) is the most common form of dementia but has no effective prevention or treatment. Developing a comprehensive picture of the genetic architecture of AD including a network level functional assessment of risk/resilience genes is essential to develop novel therapeutic targets. The overarching goals of this study are to use genetic and genomic approaches to: 1) identify genes and variants that are involved in the development of AD and related disorders; 2) identify functional networks enriched for AD or related disorder risk and protective loci; 3) determine how cellular function and physiology is impacted by these genetic factors in disease-relevant cell types and animal models. This study will use publicly available whole genome/exome sequence data generated by the Alzheimer’s Disease Sequencing Project (ADSP) and genome-wide association study (GWAS) data from the International Genomics of Alzheimer’s Project (IGAP) and others. We will apply a suite of case-control and family approaches to investigate genetic association with dichotomous and continuous disease traits. This study will not only further our understanding of the genetic architecture of AD but also provide key information regarding the molecular mechanisms, setting the stage for novel therapeutic development.
    Non-Technical Research Use Statement:
    Alzheimer’s disease (AD) is the only disease among the top ten killers in the U.S. without a disease modifying therapy. Genetic studies provide a powerful means to identify genes and pathways that are causally linked to disease etiology. We propose to use genomic and functional approaches to identify genes that alter the risk of AD and investigate how these genes disrupt cellular pathways leading to disease.
  • Investigator:
    Greicius, Michael
    Institution:
    Stanford University School of Medicine
    Project Title:
    Examining Genetic Associations in Neurodegenerative Diseases
    Date of Approval:
    January 10, 2019
    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:
    Haines, Jonathan
    Institution:
    Case Western Reserve University
    Project Title:
    Alzheimer Disease Sequence Analysis Collaborative (a.k.a. Collaborative Alzheimer Disease REsearch; CADRE)
    Date of Approval:
    October 22, 2018
    Request status:
    Expired
    Research use statements:
    Show statements
    Technical Research Use Statement:
    We plan to analyze whole exome and whole genome sequence data generated from subjects with Alzheimer disease (AD) and elderly normal controls. The goal of the planned analyses is to identify genes and other functional elements that have variations that protect against or increase susceptibility to AD. We will evaluate variants detected in the sequence data for association with AD to identify protective and susceptibility alleles using the whole exome and whole genome data. We will also evaluate similar sequence data from multiplex AD families to identify variants associated with AD risk and protection, and evaluate variant co-segregation with AD. We also will focus on structural variants (insertion-deletions, copy number variants, and chromosomal rearrangements) detected using both whole genome and whole exome data. All data will be analyzed separately and in an integrated fashion and will incorporate additional genetic and functional data.
    Non-Technical Research Use Statement:
    We are attempting to identify all the inherited elements that contribute to Alzheimer's disease risk. To do this we will analyze DNA sequence data from subjects with Alzheimer's disease and elderly subjects who are cognitively normal. The sequence data from these two groups will be compared to identify differences that contribute to the risk of developing Alzheimer's disease of that protect against Alzheimer's disease. These DNA differences can be at a single site in the genetic code, or can span multiple sites, changing the copy number of DNA sequences. Both types of genetic variants will be examined.
  • Investigator:
    Hohman, Timothy
    Institution:
    Vanderbilt University Medical Center
    Project Title:
    Genetic Drivers of Resilience to Alzheimer's Disease
    Date of Approval:
    November 8, 2019
    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:
    Holstege, Henne
    Institution:
    Amsterdam UMC
    Project Title:
    Searching for Alzheimer-related genetic variants and genes
    Date of Approval:
    May 7, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    The purpose of this study is to find new Alzheimer related variants and genes, by combining exome and whole genome data from healthy controls and Alzheimer patients from different studies. Data will be analyzed using association, burden and variant component statistics.
    Non-Technical Research Use Statement:
    Some individuals develop dementia, while others do not. A large part is likely determined by ones genes, Alzheimer’s disease has a heritability of up to 80%. What are the key genetic factors that determine if one will get Alzheimer disease? In this study, we will thoroughly explore genomic data of a large group of healthy persons and dementia patients to answer this question.
  • Investigator:
    Kulminski, Alexander
    Institution:
    Duke University
    Project Title:
    ApoE2 and protective molecular signatures in Alzheimer’s disease and aging
    Date of Approval:
    July 10, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Objective: to identify personalized polygenic profiles, comprised of the APOE e2 allele, other SNPs in the APOE region, and SNPs spread through the entire genome, with stronger protection in aging and Alzheimer’s Disease (AD) framework, and identify the role of AD risk factors in these profiles using ADSP and 10 more studies. Study design: Aim 1 will identify molecular signatures of aging-related traits (AD, cardiovascular diseases, longevity, etc.), defined as differences in linkage disequilibrium (LD) patterns between affected and unaffected subjects using methods of LD analysis. In Aim 2 we will dissect heterogeneity in the molecular signatures using methods of stratification analyses. We will examine the impact of age at onset, sex, race/ethnicity, Braak stage, AD risk factors (diabetes, lipids, hypertension, body mass index, education), and other factors. Aim 3 will identify personalized polygenic profiles of aging-related traits using traditional and advanced bio-demographic methods. In Aim 4 we will perform bioinformatics analysis and characterize transcription pathways using summary statistics and individual-level data from the expression quantitative trait loci studies. In some cases, we may need to pool several datasets to increase power of the analyses in a mega sample. This will be done by pooling individuals’ records for genotypes and selected phenotypes described above from different studies. This pooling will not create any additional risks to participants because neither genetic nor phenotypic information for the same individual will increase. This research is consistent with data use restrictions for ADSP. We will not conduct non-genetic research, will not investigate individual pedigree structures, population origins, ancestry, individual participant genotypes, perceptions of racial/ethnic identity, variables that could be considered as stigmatizing an individual or group, or issues such as non-maternity. The research is designed to protect data confidentiality and follow local and institutional policies and procedures for data handling. The results of this research will be broadly shared with the scientific community.
    Non-Technical Research Use Statement:
    Increasing population of the elderly individuals worldwide raises serious concerns about burden of geriatric conditions in future, especially Alzheimer’s disease, cardiovascular diseases, and other common aging-related diseases. These diseases can cluster in families suggesting that they can have genetic origin. Understanding their genetic origin could lead to breakthrough in preventing or curing such diseases. Despite continuing efforts, understanding their genetic basis remains very limited. Particular problem is to better understand genetic basis of Alzheimer’s disease, its relationship to other aging-related diseases, and identify genetic variants which could help protect against such diseases. This project focuses on identifying personalized polygenic profiles involving the Alzheimer’s disease protective genetic variant, so-called APOE e2 allele, which could strengthen protective effects against Alzheimer’s disease and investigate which factors can improve this protection. This research will facilitate the development of interventional strategies aiming to promote healthy aging.
  • Investigator:
    Lichtarge, Olivier
    Institution:
    Baylor College of Medicine
    Project Title:
    Integrating the impact of exome variations
    Date of Approval:
    July 10, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    BACKGROUND, OBJECTIVES: Our group developed a method to estimate the impact of missense mutations, that we call the “Action” of missense mutations. This method is better than current state-of-the-art approaches at matching experimental data on mutational loss of function, not just in our own controls but also in blind competitions assessed objectively by independent judges (CAGI 2011, and 2012-13). When we used Action on head and neck cancer patient data (TCGA) we obtained significant separation of patient survival among those with a high Action and those with a low Action in somatic TP53 mutations. However, mutations in other genes may also correlate with patient outcome, such as the mutations of IDH1 in glioblastomas (Nobusawa et al., Clin Cancer Res, 2009). Therefore, we plan to integrate mutation impact information over the human proteome and identify how severely they affect the pathways associated with each cancer type. In addition, we like to test the same principles in data from complex diseases such as Alzheimer’s Disease. To do so, we developed a network diffusion method that uses current information of protein interactions (in a physical or broader sense) in order to project the dysfunction of a protein to its near neighbors (Lisewski et.al., Physica A, 2010). Putting these together, our hypothesis is that the diffusion of Action to the human protein network can identify novel Alzheimer’s disease-associated genes and provide a better stratification of patient outcome. STUDY DESIGN, ANALYSIS PLAN: To test our hypothesis we need to access “Individual germline variant data” of patients. For each individual, we will score the germline missense mutations by Action and treat it as the potential dysfunction on the protein. Then, we will diffuse this action over the network and measure the effect on each gene and on each pathway. When we compare these data to those from healthy individuals (1000 Genomes Project), i) we can identify genes associated to each disease and ii) the pathways that affect mostly the disease, and iii) measure the severity of the mutational damage to these genes or pathways. USE RESTRICTIONS: We will follow all restrictions described.
    Non-Technical Research Use Statement:
    My group is interested in developing computational tools that predict i) disease-associated genes, ii) disease-causing mutations, and iii) the impact of an individual’s mutations to the phenotype. We make these predictions by comparing the mutational patterns of the cases with those expected either by random chance or given the purifying section observed in human polymorphisms. Here, we request access to the database of the NIA Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS) Data Sharing Service (DSS), in order to obtain protein mutation data from patients and healthy individuals.
  • Investigator:
    Lo, Cecilia
    Institution:
    University of Pittsburgh
    Project Title:
    Exploring the shared genetic etiologies of CHD and Alzheimer’s disease
    Date of Approval:
    October 11, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Congenital heart disease (CHD) affects approximately 1% of infants born each year. While CHD was previously fatal, surgical palliation now allows most patients to survive into adulthood. With more adults living with CHD, there is increasing appreciation for continuing health problems among CHD patients, such as high risk for dementia and Alzheimer’s disease. Recent studies show that APOE modifies neurodevelopmental outcomes in the CHD population (Gaynor JW, J Thoracic Cardiovascular Surgery, 2014) and that CHD patients have higher risk for Alzheimer’s disease (Bagge CN, Circulation, 2018). We hypothesize that CHD and Alzheimer’s have shared genetic causes and modifiers. Further insights into the genetic causes for CHD and dementia may reveal novel genetic relationships between the two diseases and provide possibilities for improvements in long term neurological outcomes for CHD patients. We have performed whole exome sequencing at 80x coverage on a discovery cohort of over 600 CHD patients recruited at the University of Pittsburgh Children’s Hospital and obtained access to a cohort of ~4000 healthy older individuals sequenced by the Medical Genome Reference Bank (MGRB) for use as population-matched controls. Here we will perform case-control association analysis with human next-generation sequencing data to identify SNVs, indels, and CNVs associated with CHD. We request access to sequencing data from the Alzheimer’s Disease Sequencing Project to perform a separate case-control analysis, comparing the Alzheimer’s cohort to the MGRB controls. We will then compare genes and variants that are significantly associated with each disease to identify shared pathways involved in disease pathogenesis. Processing and statistical analysis will be performed on the Pittsburgh Supercomputing Center using GATK, bcftools, PLINK, SKAT, and MAGMA well as custom shell, Python, and R scripts. These studies should help us elucidate the shared genetic etiology of CHD and Alzheimer’s disease. We intend to publish or share any findings from this study with the scientific community by presenting at national scientific meetings.
    Non-Technical Research Use Statement:
    Congenital heart disease (CHD) affects approximately 1% of infants born each year. While CHD was previously fatal, surgical palliation now allows most patients to survive into adulthood. With more adults living with CHD, there is increasing appreciation for continuing health problems among CHD patients, such as high risk for dementia and Alzheimer’s disease. We hypothesize that CHD and Alzheimer’s have shared genetic causes and modifiers. Further insights into the genetic causes for CHD and dementia may reveal novel genetic relationships between the two diseases and provide possibilities for improvements in long term neurological outcomes for CHD patients. Here we will compare genes and variants that are significantly associated with each disease based on case-control analysis to identify shared pathways involved in disease pathogenesis. In the future, we will study the functional consequences of such mutations using cells and mouse models. These studies should help us to elucidate the shared genetic etiology of CHD and Alzheimer’s disease.
  • Investigator:
    Mayeux, Richard
    Institution:
    Columbia University
    Project Title:
    Alzheimer's Disease Sequencing Project
    Date of Approval:
    November 16, 2018
    Request status:
    Expired
    Research use statements:
    Show statements
    Technical Research Use Statement:
    For this study, we will analyze data from whole genome sequencing (WGS) of from the Alzheimer's Disease Sequencing Project (ADSP) discovery-replication phase families and independent case control data from ADSP extension study. We will also analyze WGS and whole exome sequencing (WES) data from the Alzheimer's Disease Neuroimaging (ADNI) study and the ADSP follow-up study (ADSP-FUS) as they become available. The overall goal of this project is to identify and annotate causal variants related to LOAD using sequencing data generated from families multiply affected by the disease and validate the results in independent case-control datasets. Using families as discovery and unrelated individuals as replication and having the ability to genotype additional family members can provide direct evidence of causality by establishing which variants co-segregate in families and are associated in the general population with disease.
    Non-Technical Research Use Statement:
    Analyses of whole genome, whole exome and targeted resequencing will continue to provide important new information regarding potential risk conferring genes, biochemical pathways involved in Alzheimer's disease and targets that may be suitable for pharmacological manipulation. While whole exome and targeted sequencing are powerful technologies, analysis of whole genomes will provide more information and allow discovery of rare, high risk variants.
  • Investigator:
    Michaelis, Elias
    Institution:
    University of Kansas
    Project Title:
    Analysis of genome-wide sequencing data from NIAGADS: Searching for gene variants related to gender-Alzheimer's disease (AD) association
    Date of Approval:
    July 5, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Objectives: Analyze the approximately 3,500 DNA sequences from GWAS at NIAGADS for associations between gender, AD, and frequency of SNPs in chromosomal DNA using the P-Link software analytical tool. Rationale: We have performed such analyses of the DNA sequences (VCF files) made available by ADNI and have identified a significantly higher incidence of SNPs (p<10-7) in a few chromosomal genes in males vs. females with the diagnosis of AD. We would like to perform similar analyses to the DNA sequences (in VCF files) of the greater than 3,000 sequenced DNAs at NIAGADS. Plan: The SNP association analyses will be performed on the DNA sequences using information about the gender and diagnosis of each individual whose DNA sequence we would analyze. We will use the P-Link software to generate data tables (Tab delimited P-Link association files) and Manhattan plots of genome-wide associations (gnuplot). There are no multiple research sites participating in the planned analysis of the DNA sequences. All work will be performed at the University of Kansas AD Center.
    Non-Technical Research Use Statement:
    Variations in the sequences of DNA in our chromosomes and their association with the incidence of Alzheimer's disease (AD) have been identified in the last 10 years and have brought about new thinking regarding possible causes of AD. These variations in DNA do not directly cause the disease but increase the likelihood of the onset of AD in some individuals late in their life. For many years, it has been known that among various populations there is differential incidence of AD between males and females. In our initial study of a relatively small number of individuals with or without AD, we identified that there was a significant association between a few of the variants in DNA sequences and the incidence of AD in males as compared with females. The study planned will use the DNA sequences in the NIAGADS repository to conduct a similar analysis for variants in DNA sequences. The NIAGADS sequences represent a substantially larger population than that which we analyzed previously and should allow us to explore the possible association of gender and AD with variants of DNA.
  • Investigator:
    Nicolas, Gael
    Institution:
    University of Rouen
    Project Title:
    Searching for Alzheimer-related genetic variants and genes
    Date of Approval:
    November 21, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    The purpose of this study is to find new Alzheimer related variants and genes, by combining exome and genome data from healthy controls and Alzheimer patients from different studies. Data will be analyzed using association, burden and variant component statistics.
    Non-Technical Research Use Statement:
    Some individuals develop dementia, while others do not. A large part is likely determined by gene, Alzheimer’s disease has a heritability of up to 80%. What are the key genetic factors that determine if one will get Alzheimer disease? In this study, we will thoroughly explore genomic data of a large group of healthy persons and dementia patients to answer this question.
  • Investigator:
    PARIDA, LAXMI
    Institution:
    IBM Thomas J Watson Research Center
    Project Title:
    WAGE ADSP Data Analysis
    Date of Approval:
    December 3, 2018
    Request status:
    Expired
    Research use statements:
    Show statements
    Technical Research Use Statement:
    The “Watson Alzheimer’s Genetics Experiment (WAGE) is a collaboration between IBM TJ Watson Research Center, Curoverse Innovations, the Center for Genomics of Alzheimer’s Disease (CGAD U54 AG052427) and AD geneticists at the University of Pennsylvania, Indiana University, Columbia University, and Indiana University (Alzheimer’s Disease Neuroimaging Initiative [ADNI]). We plan to analyze whole genome sequence data generated from subjects with Alzheimer's disease (AD) and elderly normal controls. These data were generated by the National Human Genome Institute Large-Scale Sequence Program, the Alzheimer’s Disease Neuroimaging Initiative, and National Institute on Aging funded investigators. The goal of the planned analyses is to identify genes that have alleles that protect against or increase susceptibility to AD. We will examine both single nucleotide variants and structural variants (indels, deletions, insertions, etc). We will use whole genome sequence data for AD cases from the Alzheimer’s Disease Sequence Project. We will use control data from the Alzheimer’s Disease Sequencing Project (ADSP), ADNI, and the Multi-ethnic Study of Atherosclerosis (MESA). BAMs and gVCFs will be used by our collaborator Curoverse Innovations to generate a tiling representation of the AD and control genomes. This representation is amenable to common machine learning algorithms for AD-related variant discovery. The tiling representation will then be used by the IBM Watson team using machine-learning methods to find regions of interest for AD. Regions of interest (ROI’s) will be followed up by single variant analysis in a much larger sample using conventional genotypes from GWAS arrays in samples from the Alzheimer’s Disease Genetics Consortium and the International Genomics of Alzheimer’s Project.
    Non-Technical Research Use Statement:
    We are attempting to identify all the inherited elements that contribute to Alzheimer's disease risk. To do this we will analyze DNA sequence data from subjects with Alzheimer's disease and elderly subjects who are cognitively normal. The sequence data from these 2 groups will be compared to identify differences that contribute to the risk of developing Alzheimer's disease of that protect against Alzheimer's disease. These DNA differences can be at a single site in the genetic code, or can span multiple sites, changing the copy number of DNA sequences. Both types of genetic variants will be examined.
  • Investigator:
    Park, Peter
    Institution:
    Harvard Medical School
    Project Title:
    Examining the association between clonal hematoposiesis and Alzheimer's Disease
    Date of Approval:
    December 3, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Recent projects characterizing genomic variation across large numbers of individuals have revealed that somatic mutations driving clonal expansion in hematopoietic cells occur as part of human aging. This phenomenon, is associated with a number of adverse outcomes, including increased mortality, cardiovascular disease risk, and risk of hematological malignancy. The aim of this proposal is to assess what (if any) association clonal hematopoiesis (CH) has with Alzheimer’s disease (AD) or Dementia. We will use the available exome and whole-genome sequencing to look for somatic mutations associated with CH. In general, distinguishing germline mutations from somatic mutations is non-trivial within a single sample. However, somatic and germline variants are expected to differ in their variant allele fraction distributions. Additionally, many somatic mutations associated with CH are thought to cause severe developmental disease when they occur in the germline (e.g., loss of function in DNMT3A is associated with Tatton-Brown-Rahman syndrome). The poor prognosis of affected patients should make germline mutations in these genes rare. After identifying participants with CH, we will use standard statistical methods (e.g. a fisher test) to determine if CH has any association with AD phenotype. We will also look for sex, race, ethnicity, and APOE specific effects.
    Non-Technical Research Use Statement:
    Recent projects characterizing genomic variation across large numbers of individuals have revealed that somatic mutations driving clonal expansion in hematopoietic cells occur as part of human aging. This phenomenon, is associated with a number of adverse outcomes, including increased mortality, cardiovascular disease risk, and risk of hematological malignancy. The aim of this proposal is to assess what (if any) association clonal hematopoiesis (CH) has with Alzheimer’s disease (AD) or Dementia.
  • Investigator:
    Pericak-Vance, Margaret
    Institution:
    University of Miami
    Project Title:
    Collaboration on Alzheimer Disease Research
    Date of Approval:
    November 5, 2018
    Request status:
    Expired
    Research use statements:
    Show statements
    Technical Research Use Statement:
    We plan to analyze whole exome and whole genome sequence data generated from subjects with Alzheimer's disease (AD) and elderly normal controls. The goal of the planned analyses is to identify genes that have alleles that protect against or increase susceptibility to AD. We will evaluate variants detected in the sequence data for association with AD to identify protective and susceptibility alleles using the whole exome and whole genome case-control data. We will also evaluate sequence data from multiplex AD families to identify variants associated with AD risk and protection, and evaluate variant co-segregation with AD. The family data will be whole genome data. The family-based data will be used to inform the cases control analysis and visa veras. We also will focus on structural variants (insertion-deletions, copy number variants, and chromosomal rearrangements). Evaluation of structural variants will involve both whole genome and whole exome data. Structural variants will be analyzed with single nucelotide variants detected and analyzed in the case-control and family-based data
    Non-Technical Research Use Statement:
    We are attempting to identify all the inherited elements that contribute to Alzheimer's disease risk. To do this we will analyze DNA sequence data from subjects with Alzheimer's disease and elderly subjects who are cognitively normal. The sequence data from these 2 groups will be compared to identify differences that contribute to the risk of developing Alzheimer's disease of that protect against Alzheimer's disease. These DNA differences can be at a single site in the genetic code, or can span multiple sites, changing the copy number of DNA sequences. Both types of genetic variants will be examined.
  • Investigator:
    Ridge, Perry
    Institution:
    Brigham Young University
    Project Title:
    Mitochondrial Genetics of Alzheimer's Disease
    Date of Approval:
    January 2, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Objectives In 2010 Swerdlow et al. proposed the Mitochondrial Cascade Hypothesis of AD. This hypothesis receives support from several lines of evidence that suggest an important role of mitochondrial dysfunction in AD. Patterns of inherited risk for AD also suggest a role for the maternally inherited mitochondria. Several mitochondrial haplogroups/SNPs have been reported to correlate with AD. Despite evidence that mitochondrial genetics may influence risk for AD, the exact role remains unknown. Moreover, mitochondrial genetics could plausibly explain maternally inherited AD. The primary objectives of our proposed research are: 1) develop a large dataset of AD mitochondrial genomes (~8000 total samples), 2) identify mitochondrial genetic variants associated with AD, and 3) explore the relationship between mitochondrial inheritance of AD and the mitochondrial genome. Study Design/Analysis Plan 1. Using our published approach, assemble, annotate, and deposit in NIAGADs whole mitochondrial genome sequences from the ADSP, ADNI, the Wellderly Study, the Cache County Study on Memory Health and Aging, the Knight Alzheimer’s Research Center, and the Alzheimer’s Disease Center at the University of Kansas. All samples have already been sequenced. 2. Using an evolutionary based method, TreeScanning, assess the effects of mitochondrial haplogroups associated with mitochondrial haplotypes and AD status, including both risk and protective variation. We have published several papers using this approach to study the relationship between the mitochondrial genome and AD. 3. Also using TreeScanning, conduct association studies between mitochondrial haplotypes and maternal family history of AD. Collaborations The following will provide samples for this study: Dr. Ali Torkamani (The Scripps Translational Science Institute), Dr. Russell Swerdlow (University of Kansas Medical Center), Dr. Carlos Cruchaga (Washington University in St. Louis), and Dr. John “Keoni” Kauwe (Brigham Young University).
    Non-Technical Research Use Statement:
    Alzheimer’s disease is the most common form of dementia, is fatal, and causes a substantial burden to both affected individuals and loved ones. While substantial efforts have been made to develop effective therapeutics, there are still no disease-altering treatments. The majority of drug targets have focused on genetic targets derived from the nuclear genome. Here we propose work that has the potential to provide a new class of potential therapeutic targets. Mitochondria are cellular components and are responsible for the majority of energy production in the cell. There is significant evidence that mitochondria malfunction in Alzheimer’s disease. Each mitochondrion has one or more copies of its own genome. In contrast to the nuclear genome, mitochondrial genomes are very small, circular, and inherited almost exclusively from the mother. In this research we plan to study the relationship between Alzheimer’s disease and the mitochondrial genome.
  • Investigator:
    Saykin, Andrew
    Institution:
    Indiana University School of Medicine
    Project Title:
    Alzheimer's Disease Genomics: Systems Biology and Endophenotypes
    Date of Approval:
    November 15, 2018
    Request status:
    Expired
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Alzheimer’s disease (AD) and related genomic data sets including sequencing, GWAS and phenotypic data will be combined with longitudinal clinical, demographic, cognitive, MRI, PET, CSF and blood endophenotype data, where available, to investigate the genetic architecture of Alzheimer’s disease and related disorders (ADRD) and brain aging. The overall goal to gain a better understanding of fundamental disease mechanisms, genetic susceptibility and protective factors, and the relationship of genetic factors to disease heterogeneity, progression and different trajectories across biomarker profiles. Data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) will be combined with ADSP and other data sets to increase detection power and for replication across samples. Analyses will include conventional statistical association, multivariate profiling of endophenotypes, biological pathway and network approaches, longitudinal models and combinatorial machine learning. Deliverables will include reports of new prioritized lists of candidate genes and variants for further investigation in new samples, functional experiments and in model systems. The ultimate goal is discovery of novel potential diagnostic markers and therapeutic targets that will help provide the foundation for a precision medicine approach to AD/ADRD.
    Non-Technical Research Use Statement:
    Alzheimer’s disease (AD) and related genomic data sets will be combined with longitudinal clinical, demographic, cognitive, MRI, PET, CSF and blood biomarker data to investigate the genetic architecture of Alzheimer’s disease and related disorders (ADRD) and brain aging. The overall goal to gain a better understanding of fundamental disease mechanisms, genetic susceptibility and protective factors, and the relationship of genetic factors to disease heterogeneity, progression and different trajectories across biomarker profiles. Data will be combined across studies to increase detection power and for replication. Analyses will include conventional statistical association and advanced analytic approaches including multivariate profiling, biological pathway and network analysis and machine learning. The ultimate goal is discovery of novel potential diagnostic and therapeutic markers that will help provide the foundation for a precision medicine approach to AD/ADRD.
  • Investigator:
    Schellenberg, Gerard
    Institution:
    University of Pennsylvania
    Project Title:
    ADSP Data Analysis
    Date of Approval:
    August 23, 2018
    Request status:
    Expired
    Research use statements:
    Show statements
    Technical Research Use Statement:
    As part of the Consortium for Alzheimers Sequence Analysis (CASA: NIA grant U19AG047133). We plan to analyze whole exome and whole genome sequence data generated from subjects with Alzheimer's disease (AD) and elderly normal controls. These data will be generated by the National Human Genome Institute Large-Scale Sequence Program. The goal of the planned analyses is to identify genes that have alleles that protect against or increase susceptibility to AD. We will evaluate variants detected in the sequence data for association with AD to identify protective and susceptibility alleles using the whole exome and whole genome case-control data. We will also evaluate sequence data from multiplex AD families to identify variants associated with AD risk and protection, and evaluate variant co-segregation with AD. The family data will be whole genome data. The family-based data will be used to inform the cases control analysis and visa veras. We also will focus on structural variants (insertion-deletions, copy number variants, and chromosomal rearrangements). Evaluation of structural variants will involve both whole genome and whole exome data. Structural variants will be analyzed with single nucelotide variants detected and analyzed in the case-control and family-based data.
    Non-Technical Research Use Statement:
    We are attempting to identify all the inherited elements that contribute to Alzheimer's disease risk. To do this we will analyze DNA sequence data from subjects with Alzheimer's disease and elderly subjects who are cognitively normal. The sequence data from these 2 groups will be compared to identify differences that contribute to the risk of developing Alzheimer's disease of that protect against Alzheimer's disease. These DNA differences can be at a single site in the genetic code, or can span multiple sites, changing the copy number of DNA sequences. Both types of genetic variants will be examined.
  • Investigator:
    Sul, Jae Hoon
    Institution:
    University of California, Los Angeles
    Project Title:
    Impact of common and rare genetic variants in Alzheimer's Disease using whole-genome and whole-exome sequencing data
    Date of Approval:
    January 9, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Alzheimer’s disease (AD) has a strong genetic component, and several studies have identified genetic variants that influence AD. A majority of those variants are common variants that appear frequently in a population, and studies have also found that those variants do not explain all of genetic basis of AD. This finding has led genetic studies to investigate effect of rare variants that may have larger effect than that of common variants. To better understand effect of rare variants on AD, we aim to ADSP whole-genome sequencing (WGS) and whole-exome datasets. We will determine whether rare variants in genes appear more frequently among AD patients than controls. Our lab has developed several statistical approaches for the rare variant association method (both case/control and family), and we will apply these methods to the ADSP dataset. Through this analysis, we will quantify the effect of rare variants on AD. We will also estimate polygenic risk scores (PRS) of individuals with AD and compare them to those without AD. We will check how much phenotypic variance of AD is explained by PRS.
    Non-Technical Research Use Statement:
    Alzheimer’s disease (AD) has a strong genetic component, and although several studies found several genetic variations associated with AD, they do not explain all of genetic basis of AD. Those genetic variations are mostly common in population, and recent studies have shown that rare genetic variations may also influence AD. In this study, we propose to identify rare genetic variations that are associated with AD by applying the statistical approaches that combine effect of multiple rare variants. Our lab has developed several methods to identify effect of rare variants both among unrelated individuals and among family members. We will apply these methods to the ADSP dataset and find rare variants associated with AD. In addition to rare variants, we will also investigate effect of common variants using a method called polygenic risk score. These analyses in this study will elucidate impact of both common and rare variants in AD.
  • Investigator:
    Tanzi, Rudolph
    Institution:
    Massachusetts General Hospital
    Project Title:
    ADSP extension
    Date of Approval:
    December 3, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Late-onset AD (LOAD) is caused by a complex polygenic and environmental background. Whole genome sequencing provides comprehensive coverage of the genome and has several advantages over exome sequencing and genotyping. We plan to use an aggregated collection of whole genome sequenced family-based and case-control datasets to address the following goals. 1) Identify variants (specifically rare) and regions associated with AD (and related or derived phenotypes) or showing an interaction pattern; 2) Functionally finemap associated loci and identify the functional impact of associated variants in non-coding regions; 3) Use identified variants to validate them in a 3D neural-glial culture model. We will utilize several datasets with whole genome sequencing data, including AD datasets from National Institiute of Mental Health (NIMH) AD family sample and Alzheimer’s Disease Sequencing Project. We will use family-based association tests robust to population confounding and other approaches suitable for case-control studies. Novel analysis approaches will be developed and tested. Analysis results and derived data will be made available to the research community.
    Non-Technical Research Use Statement:
    Alzheimer’s disease (AD) is the most common neurodegenerative disorder with a huge burden on the healthcare system and the 6th leading cause of death in the United States. Sequenced DNA from people will help us to better describe the genetic architecture of AD. We will utilize two types of genomic datasets: genomes from related individuals (family-based) and genomes from unrelated individuals (case/control). Identified functional variants will be validated in a 3D neural-glial culture model and enhance the biological understanding of AD.
  • Investigator:
    Valdmanis, Paul
    Institution:
    University of Washington
    Project Title:
    Quantification of Noncoding Variant Burden in DNA De-Identified Samples and Data from Patients with Alzheimer's Disease Versus Controls.
    Date of Approval:
    June 10, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Alternative splicing patterns are notoriously complex and diverse in the brain. Moreover, appropriate regulation of brain-specific splicing is lost during normal aging, which can be exacerbated by trauma or oxidative stress. We have evaluated RNA sequencing data from brain samples from patients with Alzheimer’s disease (AD) and age-matched controls. Our analysis has revealed several alternative splice products and intronic repeat sequences that are enriched in patients with AD in genes implicated in disease (APP, PSEN1 and PSEN2). Our primary objective is to integrate our RNA sequencing results with whole genome sequence data to identify intronic variants that in combination with alternative splicing products may predispose to AD. We will study AD cases and controls to identify a rare variant burden analysis across defined genomic regions. Our analysis plan is as follows: we will extract genomic regions corresponding to intronic regions that can influence alternative splicing from CRAM files. We will quantify the presence of small insertions, deletions and variants in cases and controls and use splicing prediction software to identify the potential contribution to disease. We will determine the burden of these nucleotide changes in cases, controls and large sequencing databases (e.g. Gnomad) and perform t-tests and Chi-square tests to quantify whether an enrichment of intronic variants are present in patients with AD.
    Non-Technical Research Use Statement:
    The genetic information that can be ascertained from large scale sequencing projects can enable novel discoveries for genes that can contribute to disease. The primary objective of many of these projects is to detect nucleotide changes that alter the protein encoded by the host gene. However, the same sequencing information can be used to identify non-coding elements of the genome that can contribute to disease, including variants that can influence splicing – the appropriate assembly of exons that are spliced together to form a gene. As we age, certain elements that preserve tight regulation of exon choice lose effectiveness, particularly when faced with injury or various stressors. We wish to detect variants that influence alternative splicing products in the context of relaxed regulation of exon choice and integrate findings with RNA sequencing databases to identify methods to preserve appropriate splicing.
  • Investigator:
    Wang, Li-San
    Institution:
    University of Pennsylvania
    Project Title:
    ADSP Data Processing
    Date of Approval:
    August 23, 2018
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    NIAGADS is the data coordinating center for ADSP. This request will allow us to access genotype and phenotype data for all ADSP samples and perform data processing and quality assurance, before distributing to the scientific community. Currently a data deposition plan is being developed by ADSP: 1. Plans for aggregating phenotype, GWAS, and exome chip genotype data are in place, and NIAGADS will work with data contributors to organize and review files before data are distributed to study investigators. 2. As suggested by dbGaP, NIAGADS will work with dbGaP/SRA and the three NHGRI large-scale sequencing centers to develop a plan for reviewing incoming sequencing data. This will be done in parallel with basic quality assurance procedures by dbGaP/SRA before data are promoted to archival status and ready for analysis. NIA is in discussion with other similar whole-genome and whole-exome sequencing projects. We plan to harmonize these additional datasets with the ADSP WGS/WES data so the community can combine these datasets for analysis. All associated phenotypes are minimized and there is minimal risk to the participants.
    Non-Technical Research Use Statement:
    NIAGADS is the data coordinating center for ADSP. This request will allow us to access genotype and phenotype data for all ADSP samples and perform data processing and quality assurance, before distributing to the scientific community.
  • Investigator:
    Wijsman, Ellen
    Institution:
    University of Washington
    Project Title:
    Therapeutic target discovery in ADSP data via comprehensive whole-genome analysis incorporating ethnic diversity and systems approaches
    Date of Approval:
    July 22, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Objective: Utilize ADSP data sets to identify genes & specific genetic variants that confer risk for or protection from Alzheimer disease. Aim 1: Using combined WGS/WES across the ADSP Discovery, Disc-Ext, and FUS Phases, including single nucleotide variants, small insertion/deletions, and structural variants. We will: Aim 1a. Perform whole genome single variant and rare variant case/control association analyses of AD using ADSP and other available data; Aim 1b. Target protective variant identification via association analysis using selected controls within the ADSP data and performing meta analysis across association results based on selected controls from non-ADSP data sets. Aim 1c. Perform endophenotype analyses including cognitive function measures, hippocampal volume and circulation beta-amyloid ADSP data in subjects for which these measures are available. Meta analysis will be conducted across ADSP and non-ADSP analysis results. Aim 2: To leverage ethnically-diverse and admixed populations to identify AD variants we will: Aim 2a. Estimate and account for global and local ancestry in all analyses; Aim 2b. Perform admixture mapping in samples of admixed ancestry; and Aim 2c. Perform ethnicity-specific and trans-ethnic meta-analyses. Aim 3: To identify putative therapeutic targets through functional characterization of genes and networks via bioinformatics, integrative ‘omics analyses. We will: Aim 3a. Annotate variants with their functional consequences using bioinformatic tools and publicly available “omics” data. Aim 3b. Prioritize results, group variants with shared function, and identify key genes functionally related to AD via weighted association analyses and network approaches. Analyses will be performed in coordination with the following PIs. Coordination will involve sharing expertise, analysis plans or analysis results. No individual level data will be shared across institutions. Philip De Jager, Columbia University; Eric Boerwinkle & Myriam Fornage, U of Texas Health Science Center, Houston; Sudha Seshadri, U of Texas, San Antonio; Ellen Wijsman, U of Washington. William Salerno, Baylor College of Medicine.
    Non-Technical Research Use Statement:
    This proposal seeks to analyze existing genetic sequencing data generated as part of the Alzheimer’s Disease Sequencing Project (ADSP) including the ADSP Follow-up Study (FUS) with the goal of identifying genes and specific changes within those genes that either confer risk for Alzheimer’s Disease or provide protection from Alzheimer’s Disease. Analytic challenges include analysis of whole genome sequencing data, appropriately accounting for population structure across European ancestry, Hispanic, and African American participants, and interpreting results in the context of other genomic data available.
  • Investigator:
    Wingo, Thomas
    Institution:
    Emory University
    Project Title:
    Identifying Alzheimer's Disease Genetic Risk Factors By Integrated Genomic and Proteomic Analysis
    Date of Approval:
    September 17, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    We aim to uncover new genetic risk variants for Alzheimer’s disease (AD) by analysis of an integrated analysis of proteomics and genetic sequencing performed at Emory University. Results of these analyses will be used to weight analysis of whole-genome sequencing (WGS), whole-genome genotyping (WGG), and whole-exome sequencing (WES) data from dbGaP and ADSP. We plan to publish our findings, so they are shared with the scientific community. Outcomes that will be tested include: (1) clinical disease status, (2) pathologic characterization (e.g., measures of beta-amylodi, tau, etc.), and (3) cognitive decline. For sequencing data, we will perform joint calling from samples previously mapped by ADSP using PECaller using default settings. Variant annotation will be performed using Bystro and quality control will follow Wingo et al., 2017. For rare variants, we will use burden- and variance-based tests to estimate association between genetic variants and each outcome for every gene in the genome. External weights from proteomic analyses will be optionally used, as well as measures of genomic conservation for each site. For common variants, we plan to test for differences in allele frequencies using maximum likelihood tests. For all analyses, we plan to control for population structure deriving principal components from the underlying sequencing or genotyping data.
    Non-Technical Research Use Statement:
    Our aim is to identify genetic variants that are associated with Alzheimer's Disease (AD) either using genomic data (from dbGap or from Emory University) or brain protein sequencing data (from Emory University) as a starting point. Each center’s data will be analyzed separately, and we will determine whether the findings are consistent among the centers. Additionally, we will use protein data from brain or cerebrospinal fluid of individuals with or without AD to guide the analysis of the genomic data to identify genetic variants that influence AD risk. Our overarching aim is to use genetic discoveries to identify mechanisms of AD pathogenesis and creation of more meaningful models of the disease.
  • Investigator:
    Yang, Jingjing
    Institution:
    Emory University
    Project Title:
    Novel Bayesian methods for integrating transcriptomic data in GWAS
    Date of Approval:
    November 8, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    The objective of the proposed project is to derive novel Bayesian methods to integrate multi-omics data in genome-wide association studies (GWAS) for studying complex phenotypes, with the goal of prioritizing genetic variants and identifying causal genes. First, we will model the expression quantitative trait loci (eQTL) and other molecular QTL information in GWAS by an adapted Bayesian variable selection model, such that the model can quantify the enrichment of associated genetic variants with respect to each annotation such as eQTL and prioritize genetic variants that are of the enriched annotation. Second, we will be conducting transcriptome-wide association studies (TWAS) by a Bayesian approach to identify potentially causal genes. Third, we will use our Bayesian GWAS results to evaluate a Bayesian polygenic risk score for the complex phenotype of interest. We will first learn molecular QTL information by using external transcriptomics data set such as GTEx V7 and external molecular QTL from TCGA, and then integrate this information with the whole genome sequence data from ADSP to prioritize genetic variants associated with complex phenotypes of interest and conduct TWAS to identify risk genes. We are interested in studying all complex phenotypes that were profiled for the ADSP samples, especially Alzheimer’s disease (AD) and AD-related complex phenotypes. Especially, our lab has access to the ROS/MAP multi-omics data shared by the Rush Alzheimer’s disease center (http://www.radc.rush.edu/). All samples in the ROS/MAP study are well-characterized with extensive complex phenotypes profiled, including clinical diagnosis of AD, AD-related complex phenotypes, and psychological phenotypes. We will combine the whole genome sequence data from both ADSP and ROS/MAP samples to increase the total sample size in our study, thus improving the mapping power. The purpose of using ADSP data is to increase the sample size for testing our derived methods for functional genetic association studies of complex phenotypes. We are not limited to studying AD only. We are flexible to study any complex phenotypes that are profiled for both ADSP and ROS/MAP samples.
    Non-Technical Research Use Statement:
    This proposed project is to develop novel Bayesian methods to integrate multi-omics data such as transcriptomic in genome-wide association studies (GWAS) of complex phenotypes, with the goal of prioritizing genetic variants and identifying causal genes. i) We will model molecular quantitative trait loci information in GWAS, such that the model can quantify the enrichment for associated genetic variants with respect to each annotation and prioritize genetic variants that are of the enriched annotation. ii) We will derive a novel Bayesian model to use the eQTL effect-sizes as weights to conduct gene-based association tests. iii) We will use the Bayesian results from the proposed two methods to calculate Bayesian polygenic risk scores. We propose to test our proposed methods on the applied genomic analysis data and ROS/MAP multi-omics data to study complex phenotypes that are profiled for both ADSP and ROS/MAP samples, including AD, AD-related pathology traits, and related psychological disorders.
  • Investigator:
    Yokoyama, Jennifer
    Institution:
    University of California, San Francisco
    Project Title:
    Rare variation contributing to Alzheimer's disease risk
    Date of Approval:
    February 19, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Identification and characterization of genetic modifiers of risk for Alzheimer’s disease (AD) is paramount to development of a deeper understanding of AD pathogenesis as well as the identification of biomarkers and drug targets. The proposed research seeks to identify novel rare variants that could modulate an individual’s risk for developing sporadic late-onset or early-onset AD, and validate rare variants identified and characterized by our group that contribute to AD risk. In particular, we will focus on characterizing the contributions of changes in repetitive sequences within the coding regions of genes to AD risk. This study will also combine datasets from this project with whole genome or exome sequencing data generated by the University of California, San Francisco from individuals diagnosed with atypical or early-onset (< 65 years of age at diagnosis) AD to identify genetic risk factors unique to these less common forms of AD.
    Non-Technical Research Use Statement:
    We will use ADSP data in conjunction with existing data from our research center to characterize genetic variation that influence a person’s risk for developing Alzheimer’s disease. After establishing a set of candidate variants, we will functionally characterize their biological effect using cell and biochemical assays. Identification and characterization of Alzheimer’s disease risk modifiers will not only enhance our understanding of disease pathogenesis, but may also facilitate identification of therapeutic targets and biomarkers for preventing Alzheimer’s disease.
  • 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:
    March 19, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    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.

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 ADSP data:

Please cite/reference the use of NIAGADS data by including the accession NG00067. The acknowledgment statement to use from the ADSP can be found here.

For investigators using ADNI data:

Please cite/reference the use of NIAGADS data by including the accession NG00066. The acknowledgement statement to use from ADNI can be found here.

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