• 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:
    Expired
    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:
    Blanck, George
    Institution:
    UNIVERSITY OF SOUTH FLORIDA
    Project Title:
    Alzheimer's disease (AD) and immune receptor recombinations
    Date of Approval:
    July 20, 2020
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    We would like to access the exome files for Alzheimer’s Disease (AD) studies to better ascertain the impact of certain immune receptor (IR) recombinations on the progression of Alzheimer’s patients. First, we will mine the exomes for all IR recombinations present in the AD exome files using our previously published algorithms and software [4-6, 9]. These studies have been successful in identifying IR recombination reads that were strongly associated with distinct survival rates in various cancers. In particular, we have been able to utilize a similar methodology to identify specific T-Cell recombinations associated with cancer features by using the recombinations that are obtained from blood sample exomes. For example, recently published papers from our group have revealed consistent features of T-cell receptor recombinations, obtained from cancer patient blood samples, which were associated with features of cancer progression [4, 8]. Therefore, we hypothesize that we can detect specific IR recombination features from blood-resident T- or B-cells that are relevant to AD clinical features. Thus, we will first identify IR recombinations found within the AD, blood exomes; and then match features of these IRs, such as the chemical aspects of the IR antigen binding sites [1, 4, 5] with clinical characteristics, e.g., age of onset, diagnostic status, and cognitive measures. We thus expect to identify certain IR recombinations associated with distinct prognoses. It should also be noted that there have been previous studies linking HLA-DR alleles to late-onset AD [10]. This is of particular interest, because of our previous work linking T-cell receptor, V or J usage, HLA allele combinations to distinct cancer survival rates [2, 3, 7, 8]. Thus, we will also programmatically obtain the HLA alleles from the exome files and determine whether any T-cell receptor, V or J usage, HLA allele combinations are associated with particular features of AD development. References available by email: gblanck@usf.edu
    Non-Technical Research Use Statement:
    The purpose of this project is to learn whether there are any features of the immune system that are unique to Alzheimer's patients. If so, such unique features might help understand disease progression better and might provide targets for therapies.
  • Investigator:
    Boerwinkle, Eric
    Institution:
    University of Texas Health Science Center at Houston
    Project Title:
    ADSP Data Analysis
    Date of Approval:
    April 13, 2020
    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:
    August 3, 2020
    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 17, 2019
    Request status:
    Approved
    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:
    Chang, Timothy
    Institution:
    University of California, Los Angeles
    Project Title:
    Rare Genetic Risk and Gene Networks in Tauopathy
    Date of Approval:
    September 17, 2020
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    The objectives of this proposal are to identify rare genetic risk factors that are unique to or shared by Alzheimer’s disease (AD) and Progressive Supranuclear Palsy (PSP). We hypothesize we will identify a set of rare genetic risk factors associated with AD and another set of rare genetic risk factors associated with PSP, some of which may be shared between AD and PSP. To determine shared or unique genetic risk, we will compare AD to controls and PSP to controls. Both analyses will use the same controls and have similar number of cases. AD subjects will be included from Alzheimer’s Disease Sequencing Project, Alzheimer’s Disease Neuroimaging Initiative, and Accelerating Medicines Partnership – Alzheimer’s disease. From these studies, we will include adult controls. We will use roughly 1900 whole genomes from PSP subjects. Given the availability of AD sequencing, we will replicate the association of rare genetic risk with an independent holdout AD dataset, which will include AD and controls from the ADSP Follow Up Phase. We will also validate our finding in multi-ethnic cohorts from the ADSP. Traditional rare variant analyses have limited power due to the large number of variants and small variant effect size. Although one solution is to group variants into genes, genes do not act in isolation, but rather interact with one another in networks. Grouping variants in a network can improve power. Additionally, since most genetic risk lies in large noncoding regions of the genome, focusing analyses on noncoding regulatory regions should further increase power. We will incorporate network connectivity in rare variant statistical tests and prioritize functional noncoding variants will identify rare genetic risk factors in AD and PSP by overcoming deficiencies in traditional methods. Analyzing rare variants in protein coding, promoter and distal noncoding regions, we will compare the proposed network and non-coding prioritization methods to traditional gene-based, unprioritized non-coding methods.
    Non-Technical Research Use Statement:
    Neurodegenerative diseases including Alzheimer’s disease and Progressive Supranuclear Palsy are characterized by abnormal tau protein accumulation and do not currently have disease modifying treatments. Analyzing whole genome sequencing with novel genomic and genome informatic methods may identify rare genetic risk factors that lead to these diseases. The shared or unique rare genetic risk factors of Alzheimer’s disease and Progressive Supranuclear Palsy may become future therapeutic targets.
  • Investigator:
    Cruchaga, Carlos
    Institution:
    Washington University School of Medicine
    Project Title:
    The Familial Alzheimer Sequencing (FASe) Project
    Date of Approval:
    March 10, 2020
    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:
    August 25, 2020
    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:
    July 1, 2020
    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:
    January 22, 2020
    Request status:
    Approved
    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:
    Falcone, Guido
    Institution:
    Yale School of Medicine
    Project Title:
    Genomic analyses to evaluate the contribution of hypertension and hypercholesterolemia to risk of Alzheimer's Disease and cognitive decline in non-demented persons.
    Date of Approval:
    October 15, 2020
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Novel treatments for Alzheimer’s Disease (AD) are urgently needed. Observational data indicate that hypertension and hypercholesterolemia are associated with increased risk of both AD and cognitive status (CS) in non-demented persons. Because hypertension and hypercholesterolemia can be treated effectively, confirmation of causal links between them and AD/CS would provide an appealing therapeutic opportunity. Because mutations are randomly distributed during meiosis, mutation-disease associations are immune to confounding by postnatal exposures. In this setting, mutations strongly associated with an exposure of interest constitute ideal instrumental variables to evaluate the causal effect of that exposure on an outcome of interest. This is an appealing strategy for hypertension/hypercholesterolemia (exposures of interest) and AD/CS (outcomes of interest) because genetic variation explains a substantial proportion of the variance of these two vascular risk factors. We will combine novel methods in statistical genetics and well-established instrumental variable techniques to test the overarching hypothesis that genetically-determined hypertension and hypercholesterolemia influence risk of both late-onset AD and CS in non-demented persons. Our proposal leverages our team’s expertise and successful track record of impactful contributions in the fields of Aging; the robust research infrastructure available through Yale’s OAIC; and access, through the NIAGAD Data Storage Site and UK Biobank, to clinical and genomic data from 550,990 persons to pursue the following aims: determine whether genetically-determined hypertension and hypercholesterolemia are associated, individually or jointly, with increased risk of late-onset AD; and determine whether genetically-determined hypertension and hypercholesterolemia are associated with CS in community-dwelling individuals not yet diagnosed with dementia. This administrative supplement to Yale’s OAIC will deploy an innovative strategy for causal inference based on genetic information to clarify whether observed associations between hypertension/ hypercholesterolemia and AD/CS reflect true causal relationships.
    Non-Technical Research Use Statement:
    Novel treatments for Alzheimer’s Disease are urgently needed. Observational data indicate that hypertension and hypercholesterolemia are associated with increased risk of both late-onset Alzheimer’s Disease and cognitive decline in non-demented persons; however, it is not clear whether these relationships are causative or associative. We will combine novel methods in statistical genetics and well-established instrumental variable techniques to test the overarching hypothesis that genetically-determined hypertension and hypercholesterolemia influence risk of both late-onset Alzheimer’s Disease and cognitive decline in nondemented persons.
  • Investigator:
    Fardo, David
    Institution:
    University of Kentucky
    Project Title:
    Localizing risk variants and estimating effects in the Alzheimer's Disease Sequencing Project (ADSP) Data (Update to GRCh38)
    Date of Approval:
    April 6, 2020
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    This is an update to a currently-active project through dbGaP in order to garner GRCh38 mapped reads and additional WES subjects (a total of 19,922) for downstream analysis of regions identified through the scan statistic, endophenotype and causal approaches. The original edited RUS is copied below: We aim to better isolate causal variants within putative ADRD disease genes via two primary approaches. First, we will use an empirical Bayes scan statistic to detect regions of disease variant enrichment. In addition, we will employ novel causal inference methodology to estimate variant-specific causal risk for ADRD. These complementary approaches will allow for discovery of novel ADRD genes as well as enumeration/localization of important variants within putative AD risk genes. We will also employ more conventional approaches (e.g., SKAT, endophenotype development) as appropriate. We have read and approved the Data Use Agreement as signed and submitted on dbGaP and plan to upload results of our findings in a timely manner.
    Non-Technical Research Use Statement:
    The main goals of the Alzheimer’s Disease Sequence Project (ADSP) include the identification of novel genomic variants contributing to risk of Late-Onset Alzheimer’s Disease or to protection against Alzheimer’s Disease (AD), as well as providing information as to why at-risk individuals may not develop AD or related dementias, especially in multi-ethnic populations. The aim of our data analysis aligns with these goals to identify novel genomic variants associated with AD. We will aim to do so via a scan-based statistic at each variant, where the statistic is specially designed for the analysis of genomic data. We will also explore alternative methodologies for these discoveries including the calculation of a causal estimate of variants within putative AD genes.
  • Investigator:
    Farrer, Lindsay
    Institution:
    Boston University
    Project Title:
    ADSP Data Analysis
    Date of Approval:
    March 10, 2020
    Request status:
    Approved
    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:
    Funk, Cory
    Institution:
    Institute for Systems Biology
    Project Title:
    Immunity in AD
    Date of Approval:
    June 30, 2020
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Objective: The HLA region was previously identified in an Alzheimer’s GWAS study. The role of the adaptive immune system in Alzheimer’s is not well understood, despite emerging evidence suggesting infectious agents may be contributing to the disease. Study design: Using the data requested from NIAGADS, we will attempt to remap the HLA region to produce a more better defined haplotypes for each sample. We will also look at other variants of interest associated with Alzheimer’s and/or the innate and adaptive immune system, to pursue hypotheses around how these two arms of the immune system may interact. We will also use these data to perform association testing, identifying variants associated with AD risk or infection and evaluating their sensitivity to covariates such as APOE genotype, sex, and ancestry. Analysis plan: Data from NIAGADS with be downloaded to an AWS instance. Regions of interest, such as the HLA region on chromosome 6, will be extracted using samtools. Additional genotype data will be imputed using the Michigan Imputation Server and reference data selected to best match the ancestry(s) represented in the data. Association testing will adjust for population structure and genetic relatedness. Variants of interest will be annotated using resources such as the Variant Effect Predictor and the Genotype-Tissue Expression project to facilitate the interpretation of association results. Pathway analyses may be used to better understand potential relationships between implicated genes and genes previously implicated in AD and related disorders. We will perform genome scans in large data sets representing diverse ancestries. We will use imputed genotype data within association signals to fine-map the location of variants associated with Alzheimer’s disease. Association testing across independent data sets will be used to replicate these signals. We will use variant annotation to describe the potential relationships between implicated variants and gene function, regulation, and pathways. This work will attempt to identify genes involved in the innate and adaptive immune responses in connection with AD.
    Non-Technical Research Use Statement:
    We will be investigating the possible connections between genes in the immune system and Alzheimer's disease. We will be looking at both the innate and adaptive arms of the immune system. WE will also include approaches that consider the potential role of pathogens in contributing to Alzheimer's etiology.
  • Investigator:
    Gibbs, Richard
    Institution:
    Baylor College of Medicine
    Project Title:
    Therapeutic Target Discovery in ADSP data via Comprehensive Whole-Genome Analysis Incorporating Ethnic Diversity and Systems Approaches
    Date of Approval:
    September 17, 2020
    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. Anita DeStephano, Boston University, 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.
    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:
    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:
    November 18, 2020
    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:
    November 23, 2020
    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:
    April 15, 2020
    Request status:
    Approved
    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 and related disorders (ADRD) 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 ADRD. We will evaluate variants detected in the sequence data for association with ADRD to identify protective and susceptibility alleles using the whole exome and whole genome data. We will also evaluate similar sequence data from multiplex ADRD families to identify variants associated with ADRD risk and protection, and evaluate variant co-segregation with ADRD. We also will focus on structural variants (e.g. insertion-deletions, copy number variants, and chromosomal rearrangements, etc.) 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 the risk of Alzheimer's disease and related disorders (ADRD). To do this we will analyze DNA sequence data from subjects with ADRD 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 ADRD. 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:
    Expired
    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:
    November 4, 2020
    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:
    Jinwal, Umesh
    Institution:
    University of South Florida, College of Pharmacy
    Project Title:
    Characterize the Role of Shroom-3 in Alzheimer's Disease
    Date of Approval:
    February 5, 2020
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Objectives of the proposed research: Alzheimer's disease (AD), the most common type of dementia, is a neurodegenerative disease that generally affects people greater than 45 years old. AD patients show a persistent cognitive decline that leads to total disability at the end stage. Tau protein is one of major proteins linked to AD progression; it accumulates in neurons and forms paired helical filaments. As a result, Tau protein loses its capability to bind with microtubules and leading to neurodegeneration. We have performed Cdc37 chaperone based mass spectrometry to identify novel proteins linked to AD. We found Shroom-3 interaction with Cdc37 completely abolished in AD brain tissues compared to normal human brain tissues. These data provide strong evidence for potential role of Shroom-3 in AD. Currently, there is no genomic data available on Shroom-3 in AD cases. Hence, with this data access we aimed to perform genomic analysis for Shroom-3 and identify any potential mutations (SNPs) in Shroom-3 in AD. After analyzing Shroom-3, we will look at Cdc37 chaperones and other related proteins to fully characterize Shroom-3 and associated proteins. Study design: As a pilot study, we will aim for sample size n=100. Depending on available data and information for analysis, we will group samples as follows: male & female, different ethnicity, and age groups. Depending on data analysis results sample size will be adjusted to higher numbers after completion of pilot study with n=100. We will use bioinformatic software to compare gene sequences from AD patients with normal healthy individual (wild-type gene sequences) to identify any potential mutations/ Single nucleotide polymorphisms (SNPS). Based on results, we will plan cellular and animal model studies for further characterization. Analysis plan, including the phenotypic characteristics that will be evaluated in association with genetic variants: We will carefully look at AD and normal aligned sequences for any changes in a particular nucleotide or set of nucleotides to identify mutations/SNPs in different groups (gender, ethnicity, & age).
    Non-Technical Research Use Statement:
    Alzheimer's disease (AD), the most common type of dementia, is a neurodegenerative disease that generally affects people greater than 45 years old. AD patients show a persistent cognitive decline that leads to total disability at the end stage. Tau protein is one of major proteins linked to AD progression; it accumulates in neurons and forms paired helical filaments. As a result, Tau protein loses its capability to bind with microtubules and leading to neurodegeneration. We have performed Cdc37 chaperone based mass spectrometry to identify novel proteins linked to AD. We found Shroom-3 interaction with Cdc37 completely abolished in AD brain tissues compared to normal human brain tissues. These data provide strong evidence for potential role of Shroom-3 in AD. Currently, there is no genomic data available on Shroom-3 in AD cases. Hence, with this data access we aimed to perform genomic analysis for Shroom-3 and identify any potential mutations (SNPs) in Shroom-3 in AD. We will also look at Cdc37 chaperones and related proteins to fully characterize Shroom-3 and associated proteins.
  • Investigator:
    Knowles, David
    Institution:
    New York Genome Center
    Project Title:
    Learning the Regulatory Code of Alzheimer's Disease Genomes
    Date of Approval:
    September 17, 2020
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Our overarching objective is to apply machine learning techniques to predict and interpret the functional effects of genetic variants including Single Nucleotide Variants (SNVs), indels and Structural Variants (SVs) from AD WGS data at the levels of DNA regulation and RNA processing, and link these effects directly to pathways and network context. We will leverage WGS generated by the ADSP and others together with harmonized endophenotypes and clinical data, multi-omics data from the AMP-AD, functional genomics data from Roadmap Epigenomics, PsychENCODE and GTEx Projects, and microglia and monocytes specific transcriptomic and single-cell RNA-seq data sets. Our central hypothesis is that many AD-associated genetic risk or protective variants influence pre- and post-transcriptional gene regulation, resulting in changes to gene expression and cellular pathways/networks, and ultimately contribute to protein aggregation in AD. The objective of this aim is to leverage deep-learning-based models capable of predicting functional effects of genomic variants on pre- and post-transcriptional gene regulation. We will train existing and novel sequence-based deep learning models of epigenomic state and RNA regulation and processing specific to AD-relevant cell types and states. in silico mutagenesis under these trained models will be used to calculate functional impact “delta scores” for every SNV, indel and structural variants (SV) detected from AD WGS. We will use these delta scores to empower non-coding rare variant tests of association with AD at the regulatory region, gene and pathway levels. We will conduct functional fine-mapping through the integration of (i) the CNN delta scores (ii-iii) expression and splicing quantitative trait loci (eQTL and sQTL), (iv) AD endophenotypes and (v) multi-ethnic AD WGS data. We will use probabilistic ML methods, combined with cell-type-specific and single-cell RNA-seq datasets, to build gene regulatory networks. This NIH funded project is a close collaboration with Dr. Towfique Raj at Mount Sinai Medical School.
    Non-Technical Research Use Statement:
    Despite decades of research and enormous investment, no disease-modifying treatment is available for Alzheimer’s disease (AD). Combining population-scale data collection, human genetics and machine learning provide a way forward to uncover and characterize new causal cellular processes involved in AD. Effectively integrating diverse genomic data to better understand AD represents a substantial computational challenge, both in terms of data scale and analysis complexity. We will train machine learning models to predict epigenomic signals from the genomic sequences to estimate the functional impact of any genetic variant. These analyses will highlight variants and genes involved in AD. However, genes do not operate in a vacuum so robust machine learning will be used to learn cell-type and disease- specific networks. Such pathways will be prime candidates for future functional and therapeutic studies of AD.
  • Investigator:
    Kulminski, Alexander
    Institution:
    Duke University
    Project Title:
    ApoE2 and protective molecular signatures in Alzheimer’s disease and aging
    Date of Approval:
    August 10, 2020
    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:
    Kulminski, Alexander
    Institution:
    Duke University
    Project Title:
    Personalized genetic profiles of risk and resilience in Alzheimer’s and vascular diseases
    Date of Approval:
    August 10, 2020
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Objective: to identify personalized genetic profiles of risks and resilience to Alzheimer’s disease (AD) and vascular diseases in the disease-specific and pleiotropic contexts in prioritized loci leveraging information from the AD-centered pleiotropic meta-analysis planned in this project and previous analyses by our and other research groups, and identify the role of AD risk and other factors in these profiles using ADSP and 13 more studies. Study design: Aim 1 will identify specific and pleiotropic loci for AD and vascular traits from new analyses and the existing publications by: (i) performing pleiotropic genome-wide analysis focused on AD, cardiovascular diseases (CVD), and AD risk factors and (ii) identifying promising loci from this analysis and the results of previous analyses by our and other research groups. Aim 2 will dissect heterogeneity leveraging the analysis of molecular signatures defined as differences in linkage disequilibrium patterns in affected and unaffected subjects. Aim 3 will identify personalized genetic profiles of AD-specific and pleiotropic risks and resilience. Aim 4 will leverage biological, bioinformatics, and omics analyses to make sense of statistical inferences. 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 genetic profiles of risk and resilience to AD and vascular diseases. 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 22, 2020
    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:
    Expired
    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:
    February 24, 2020
    Request status:
    Approved
    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:
    August 25, 2020
    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:
    Myers, Richard
    Institution:
    HudsonAlpha Institute for Biotechnology
    Project Title:
    Replication of risk factors for early-onset dementias
    Date of Approval:
    August 18, 2020
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    We are a part of collaborations with the Yokoyama lab at UCSF and the Kosik lab at UCSB to analyze genomes for early onset Alzheimer’s and frontotemporal dementia cohorts compared to unaffected controls. A critical part of these efforts is replication of any findings in independent cohorts. Access to Alzheimer's Disease Sequencing Project (ADSP) data is ideal for this purpose. We will analyze ADSP data for association signals identified in our independent cohorts using either single variant or burden analysis approaches. Phenotypic characteristics that will be evaluated in association with genetic variants will be either case/control status or age of symptom onset as available. Although we conduct these projects as collaborations, this application is for analysis of ADSP data at HudsonAlpha.
    Non-Technical Research Use Statement:
    We work together with the Yokoyama lab at UCSF and the Kosik lab at UCSB to analyze the DNA from patients with early onset Alzheimer’s and frontotemporal dementia in comparison to people without these diseases. A critical part of this type of work is checking to see if findings from one set of patients are reproducible in different sets of patients. Access to Alzheimer's Disease Sequencing Project (ADSP) data would allow for us to answer this question. We will analyze ADSP data for association signals identified in our independent sample sets. The types of data that will be evaluated in association with genetics will be either if the individuals assessed have disease or not, or if their genetics affects when they develop disease.
  • Investigator:
    Nicolas, Gael
    Institution:
    University of Rouen
    Project Title:
    Searching for Alzheimer-related genetic variants and genes
    Date of Approval:
    November 9, 2020
    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:
    Oukraintseva, Svetlana
    Institution:
    Duke University
    Project Title:
    Genetics of Aging, Health, and Longevity: Focus on Regulatory Mechanisms and Functional Variants Connecting Aging and Alzheimer's Disease
    Date of Approval:
    August 10, 2020
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    The objective of this project is to find common regulatory and rare functional genetic variants involved in both aging and Alzheimer’s disease (AD), and suggest new genetic targets for AD prevention. We plan to: (i) evaluate collective effects of genetic interactions using newly developed in our group Interaction Polygenic Risk Score (IPRS), allowing to integrate the additive and interaction effects of genes on AD and aging traits, which presents significant methodological advantage; (ii) leverage the whole exome sequencing data (WXS), to find rare functional variants associated with aging and AD; (iii) focus on genetic regulators of translation that influence levels of proteins and provide connection between genes and phenotypes; and (iv) explore biological pathways involved in aging and AD. For this, we will conduct only secondary analyses of existing genetic and phenotypic data collected in the Alzheimer's Disease Sequencing Project (ADSP), as well as in other studies, including Framingham Cohort (a.k.a., Framingham Heart Study (FHS)), Cardiovascular Health Study (CHS), Alzheimer’s Disease Neuroimaging Initiative (ADNI), and UK Biobank. Current request refers to the ADSP. The analyses will be performed using relevant statistical methods and software. The project does not involve any contact with or participation of the real subjects.
    Non-Technical Research Use Statement:
    The objective of this project is to significantly improve our understanding of the heterogeneity of Alzheimer’ s disease (AD) and common genetic mechanisms in aging and AD, and find new genetic targets for AD prevention, with emphasis on regulatory and rare functional variants involved in both aging and AD. This objective will be addressed by conducting secondary analyses of existing human data collected in existing human studies, containing genetic and phenotypic information on thousands of individuals.
  • 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:
    December 18, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    We have been developing more powerful statistical methods to detect common variant (CV)- or rare variant (RV)-complex trait associations and/or putative causal relationships for GWAS and DNA sequencing data. Here we propose applying our new methods, along with other suitable existing methods, to the existing ADSP sequencing data and other AD GWAS data provided by NIA, hence requesting approval for accessing the ADSP sequencing and other related GWAS/genetic data. We have the following two specific Aims: Aim1. Association testing under genetic heterogeneity: For complex traits, genetic heterogeneity, especially of RVs, is ubiquitous as well acknowledged in the literature, however there is barely any existing methodology to explicitly account for genetic heterogeneity in association analysis of RVs based on a single sample/cohort. We propose using secondary and other omic data, such as transcriptomic or metabolomic data, to stratify the given sample, then apply a weighted test to the resulting strata, explicitly accounting for genetic heterogeneity that causal RVs may be different (with varying effect sizes) across unknown and hidden subpopulations. Some preliminary analyses have confirmed power gains of the proposed approach over the standard analysis. Aim 2. Meta analysis of RV tests: Although it has been well appreciated that it is necessary to account for varying association effect sizes and directions in meta analysis of RVs for multi-ethnic cohorts, existing tests are not highly adaptive to varying association patterns across the cohorts and across the RVs, leading to power loss. We propose a highly adaptive test based on a family of SPU tests, which cover many existing meta-analysis tests as special cases. Our preliminary results demonstrated possibly substantial power gains.
    Non-Technical Research Use Statement:
    We propose applying our newly developed statistical analysis methods, along with other suitable existing methods, to the existing ADSP sequencing data and other AD GWAS data to detect common or rare genetic variants associated with Alzheimer’s disease (AD). The novelty and power of our new methods are in two aspects: first, we consider and account for possible genetic heterogeneity with several subcategories of AD; second, we apply powerful meta-analysis methods to combine the association analyses across multiple subcategories of AD. The proposed research is feasible, promising and potentially significant to AD research. In addition, our proposed analyses of the existing large amount of ADSP sequencing data and other AD GWAS data with our developed new methods are novel, powerful and cost-effective.
  • Investigator:
    PARIDA, LAXMI
    Institution:
    IBM Thomas J Watson Research Center
    Project Title:
    WAGE ADSP Data Analysis
    Date of Approval:
    January 17, 2020
    Request status:
    Approved
    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:
    Parrado, Antonio
    Institution:
    Janssen R&D
    Project Title:
    Extensive search for variants that protect or elevate the risk of Alzheimer's Disease
    Date of Approval:
    August 25, 2020
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Objectives: The aim of our analyses of the Alzheimer’s Disease Sequence Project (ADSP) cohort data is to improve the identification of reliable gene-drug targets to develop effective therapeutic medicines to prevent or slow-down the progression of Alzheimer’s disease in patients. Study design: Our goal is to identify novel and reliable gene-targets to develop effective therapeutic medicines to prevent or slow-down the human burden caused by Alzheimer’s disease. To identify variants that confer risk or provide protection to Alzheimer’s disease it is essential to obtain deep sequencing (whole-genome and whole-exome) in families with high penetrant variants and in case-control populations. We will capitalize on the Discovery Phase and Extension Phase cohorts that includes WGS data in families by performing family-based association analyses. Additionally, we will perform case-control association analyses on the whole-exome sequence data from the Discovery Phase Case/Control, Extension Case/Control, FUS1, and FUS2 cohorts. Analysis Plan: We plan to analyze the WGS and WES data with several phenotype-variant analysis approaches. We will perform common variant, rare variant gene-based (i.e. stop-gain, frameshift, putatively deleterious non-synonymous, and splice-site variants), pathway-based analysis, and sex-stratified analysis. We plan to perform association analysis with a dichotomous outcome (i.e. affected/unaffected) and with neuropathology quantitative measures (where available). We have expertise in several analyses software to perform the proposed analysis; they include PLINK, PLINKSeq, MENDELSCAN, and varianttools. We plan to analyze the ADSP cohort and other Alzheimer's disease cohorts independently (i.e. UKBB) and to combine the summary statistics (Odds ratio and p-values) by meta-analysis. The planned research is consistent with the data use limitations/restrictions for the requested dataset(s), and we promise to follow all regulations within. Our proposed research will support all conditions specified in the Data Use Agreements associated with the study and will not violate relevant privacy or consent policies.
    Non-Technical Research Use Statement:
    The aim of our analyses of the Alzheimer’s Disease Sequence Project (ADSP) cohort data is to unravel the genetic architecture of AD with an objective to identify reliable gene-drug targets through various family-based and population-based statistical analyses, followed by prioritizing molecular targets and to develop effective therapeutic medicines to prevent or slow-down the progression of Alzheimer’s disease in patients.
  • 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:
    Raj, Towfique
    Institution:
    Icahn School of Medicine at Mount Sinai
    Project Title:
    Learning the Regulatory Code of Alzheimer's Disease Genomes
    Date of Approval:
    September 29, 2020
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Our overarching objective is to apply machine learning techniques to predict and interpret the functional effects of genetic variants including Single Nucleotide Variants (SNVs), indels and Structural Variants (SVs) from AD WGS data at the levels of DNA regulation and RNA processing, and link these effects directly to pathways and network context. We will leverage WGS generated by the ADSP and others together with harmonized endophenotypes and clinical data, multi-omics data from the AMP-AD, functional genomics data from Roadmap Epigenomics, PsychENCODE and GTEx Projects, and microglia and monocytes specific transcriptomic and single-cell RNA-seq data sets. Our central hypothesis is that many AD-associated genetic risk or protective variants influence pre- and post-transcriptional gene regulation, resulting in changes to gene expression and cellular pathways/networks, and ultimately contribute to protein aggregation in AD. The objective of this aim is to leverage deep-learning-based models capable of predicting functional effects of genomic variants on pre- and post-transcriptional gene regulation. We will train existing and novel sequence-based deep learning models of epigenomic state and RNA regulation and processing specific to AD-relevant cell types and states. in silico mutagenesis under these trained models will be used to calculate functional impact “delta scores” for every SNV, indel and structural variants (SV) detected from AD WGS. We will use these delta scores to empower non-coding rare variant tests of association with AD at the regulatory region, gene and pathway levels. We will conduct functional fine-mapping through the integration of (i) the CNN delta scores (ii-iii) expression and splicing quantitative trait loci (eQTL and sQTL), (iv) AD endophenotypes and (v) multi-ethnic AD WGS data. We will use probabilistic ML methods, combined with cell-type-specific and single-cell RNA-seq datasets, to build gene regulatory networks. This NIH funded project is a close collaboration with Dr. David Knowles at the New York Genome Center/ Columbia University.
    Non-Technical Research Use Statement:
    Despite decades of research and enormous investment, no disease-modifying treatment is available for Alzheimer’s disease (AD). Combining population-scale data collection, human genetics and machine learning provide a way forward to uncover and characterize new causal cellular processes involved in AD. Effectively integrating diverse genomic data to better understand AD represents a substantial computational challenge, both in terms of data scale and analysis complexity. We will train machine learning models to predict epigenomic signals from the genomic sequences to estimate the functional impact of any genetic variant. These analyses will highlight variants and genes involved in AD. However, genes do not operate in a vacuum so robust machine learning will be used to learn cell-type and disease-specific networks. Such pathways will be prime candidates for future functional and therapeutic studies of AD.
  • Investigator:
    Ridge, Perry
    Institution:
    Brigham Young University
    Project Title:
    Mitochondrial Genetics of Alzheimer's Disease
    Date of Approval:
    June 22, 2020
    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, 2) identify mitochondrial genetic variants associated with AD, 3) explore the relationship between mitochondrial inheritance of AD and the mitochondrial genome, and 4) determine if mitochondrial genetic variants explain maternal inheritance of AD. We are requesting access to all whole genomes and exomes. 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:
    February 13, 2020
    Request status:
    Approved
    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:
    Seshadri, Sudha
    Institution:
    Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX
    Project Title:
    Therapeutic target discovery in ADSP data via comprehensive whole-genome analysis incorporating ethnic diversity and systems approaches
    Date of Approval:
    October 14, 2020
    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:
    Shah, Naisha
    Institution:
    J. Craig Venter Institute
    Project Title:
    Multimodal Analysis of AD
    Date of Approval:
    October 5, 2020
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    The principal goal of this study is to identify novel genetic signatures associations with subtypes of Alzheimer’s disease (AD). Specifically, we aim to (a) identify subtypes of AD using phenotypic variables including age-of-onset, sex, years of education, clinical measurements, and neurocognitive measurements, and (b) identify novel genetic signatures that include rare variants, APOE status and/or polygenic risk scores (PRS), which are associated with these subtypes. Current knowledge from literature and databases will be utilized to perform feature engineering such as calculation of PRS and genomic-region-based-bins for rare variant burden. We will employ unsupervised learning such as community detection and clustering algorithms to identify subtypes of AD, and supervised learning such as decision tree and regularization algorithms to find genetic signatures that are associated with the subtypes. The identified genetic signatures will be evaluated using appropriate performance metrics for the predictive models used. As more phenotypic variables are made available, the models will be updated to refine the subtypes with better characterization. It is anticipated that the novel genetic signatures will yield insight into the etiologies of the heterogeneous Alzheimer’s disease, and therefore provide opportunities to develop personalized treatments. Initially, we will use the NIAGADS data to generate preliminary data for grant submissions. After receiving award, the project would utilize the very valuable dataset fully.
    Non-Technical Research Use Statement:
    Alzheimer’s disease (AD) is a heterogeneous condition and is high heritable (58-79% heritability for late-onset and >90% heritability for early-onset). We have yet to identify large proportion of genetic variants that either increase or decrease risk for different subtypes of AD such as early-onset or late-onset. In the proposed study, we plan to identify genetic signatures for the different subtypes of AD. Unraveling the heterogeneity of AD and its associated genetic signatures is critical for implementation of precision medicine to combat such a devastating disease.
  • Investigator:
    Sharp, Andrew
    Institution:
    Icahn School of Medicine at Mount Sinai
    Project Title:
    Investigating the role of tandem repeat variation in Alzheimer’s disease
    Date of Approval:
    June 16, 2020
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Variation in tandem repeats (TRs), particularly large expansions of triplet repeats (eg. polyCAG), are known to cause a number of late-onset neurological diseases. Due to their repetitive and degenerate nature, variations in TRs are typically ignored by standard genome analysis pipelines. Furthermore, pathogenic repeat expansions typically span hundreds to thousands of bases, making variations in them difficult to detect in short read data. However, recently a number of specialized algorithms have been developed which enable expansions of short TRs (motif sizes 1-12bp) to be detected in short-read sequencing data. Our lab has also developed approaches that allow the copy number of repeats with larger motifs (motif size ranging from 12bp-200kb) to be estimated based on read depth. We hypothesize that variation in TR regions contributes to risk of AD, and will test two hypotheses: 1. That rare pathogenic expansions of TRs (either in the “full” or “pre-mutation” range) occur at increased frequency in AD patients compared to controls. 2. That length variation in TRs of all sizes represents a class of common genetic variation that may alter an individual’s susceptibility to AD. In Aim 1, we will search for expansions of microsatellite repeats using tools such as ExpansionHunter, exSTRa and STRetch, that analyze WGS BAM files for signatures of expansion. We will look for loci with an excess of rare outlier genotypes in cases vs controls. If loci showing rare expansions in cases are identified, if possible, we would request aliquots of DNA from the specific individuals to perform long-read sequencing to validate the presence of potentially pathogenic repeat expansions. In Aim 2, we will use read depth approaches to estimate copy number of large TRs. We will compare estimated copy numbers of these repetitive regions in cases vs. controls to identify TR loci that show significant associations of copy number with AD compared to controls. Analysis will incorporate technical and biological covariates, such as principal components of WGS read depth data, ethnicity from SNV data, gender, etc, and will utilize a multiple testing correction for genome-wide analysis.
    Non-Technical Research Use Statement:
    Some types of neurodegenerative diseases are known to be caused by a specific type of genetic mutation where a short piece of DNA becomes repeated hundreds of time. Termed “repeat expansions”, these types of mutation can be difficult to find using standard methods scientists use to sequence DNA. We believe that some cases of Alzheimer’s disease may also be caused by repeat expansions. We will apply new analysis approaches that are specifically designed to find these repeat expansions in genome sequencing data, with the aim of finding novel types of genetic mutation that contribute to some cases of Alzheimer’s disease.
  • Investigator:
    Sirota, Marina
    Institution:
    UCSF
    Project Title:
    Elucidating Sex Differences in Alzheimer's Disease Using Genetics
    Date of Approval:
    June 30, 2020
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Alzheimer’s disease (AD) is a devastating multifactorial neurodegenerative disorder caused by interactions among multiple genetic and environmental factors. A major challenge of studying AD pathogenesis and developing and testing new drugs is the disease heterogeneity in both clinical phenotype and the underlying pathophysiology. Sex differences both play a significant role in disease risk and are a major source of disease heterogeneity in AD. Although the sex differences in the risk of AD, vulnerability to genetic load and severity of AD pathology burden have been well established, the molecular underpinnings and pathways that are differentially mediated in male and female AD patients are still poorly understood. The goal of this project is to analyze publicly available, large-scale genomic datasets of AD patients and age-matched controls to identify genomic regions that are associated with AD differentially in male and female patients and examine their interactive effects with apoE genotypes.
    Non-Technical Research Use Statement:
    We would like to leverage GWAS and other sequencing efforts in AD to identify sex specific markers associated with the disease.
  • 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, 2020
    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:
    Tzeng, Jung-Ying
    Institution:
    Department of Statistics and Bioinformatics Research Center, North Carolina State University
    Project Title:
    Genetic Association Study of Alzheimer’s Disease with Whole-Genome and Whole-Exome Sequence Data
    Date of Approval:
    October 19, 2020
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    Objective of the proposed research: Recent studies have found that single nucleotide polymorphism (SNPs) and copy number variations (CNVs) can both play significant roles in missing heritability of Alzheimer’s Disease (AD). In this project, we propose to conduct a comprehensive investigation on both variant types and understand their contributions in AD risk. Study design: We will use the whole-genome (WGS) and whole-exome (WES) sequence data in the Alzheimer's Disease Sequencing Project (ADSP) and conduct case-control association analyses of SNPs and CNVs. Analysis plan: Using the ADSD sequence data, we will start with CNV detection and characterization of CNV sequence features (e.g., microhomology, non-template insertions, and segmental duplications) to understand potential mechanisms of CNV formation. Next, we will study the association of AD status with SNPs and CNVs (common and rare) using standard association methods and adjusting for population structure (PS) and ages of onset. We will also develop and apply new methods using kernel and regularized regressions to assure efficient modeling of joint SNP-CNV effects from common and rare variants. We will perform ethnic-specific and ethnic-combined association analyses. We will use principle-component-based methods to adjust for PS, but also explore the efficacy of other PS adjustment methods. Finally, we will conduct biological annotation on identified risk variants. Collaborators: The team includes researchers from University of Pennsylvania (UPenn) and North Carolina State University (NCSU). UPenn researchers include Gerard Schellenberg (PI: Professor of Pathology and Laboratory Medicine), Li-San Wang (PI: Professor of Pathology and Laboratory Medicine), Wan-Ping Lee (PI: Research Assistant Professor of Pathology and Laboratory Medicine), Adam Naj (Assistant Professor of Biostatistics and Epidemiology) and Yuk Yee Leung (Research Assistant Professor of Pathology and Laboratory Medicine).
    Non-Technical Research Use Statement:
    Copy number variants (CNVs) are DNA regions that have gains (duplications) or losses (deletions). CNVs affect a considerable number of base pairs in the human genome. Unlike single-nucleotide polymorphisms (SNPs) that has been broadly studied in diseases, CNVs were not intensively discovered. The large-scale Alzheimer’s Disease Sequencing Project (ADSP) provides a systematic way to capture nearly all genomic variations and to study the genetic basis of Alzheimer’s Disease (AD). In this project, using the data of affected and unaffected samples from ADSP, we propose to conduct a comprehensive investigation on both variant types (SNPs and CNVs) and study their contributions in AD risk and etiology. We will start with CNV genotyping, followed by conducting standard association analysis of AD with SNPs and CNVs. We will also develop and apply new analytical methods for efficient modeling of joint SNP-CNV effects from common and rare variants. Finally, we will conduct functional annotation on identified risk variants to uncover possible biological mechanisms.
  • 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:
    July 1, 2020
    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:
    Vassar, Robert
    Institution:
    Northwestern University
    Project Title:
    Whole-exome burden analysis and functional assessment of rare variants in Alzheimer's disease
    Date of Approval:
    July 22, 2020
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    The first objective is to perform an exome-wide burden analysis of variants in 10,088 ADSP cases and controls. We will count all alleles per individual across frequencies (AF< 5%, 1%, 0.1%, singleton, ultra-rare variants), functional annotations (Protein altering variants, nonsynonymous, loss-of-function, synonymous and noncoding) and damaging predictions (CADD scores >12.37=damaging). We will also stratify cases according to age of onset (early onset, late onset). Then we will run a logistic regression modeling the number of alleles per individual against disease status, including correction for relevant covariates, such as age, sex, population structure (PCA) and sequencing coverage if applicable. We will correct associations for multiple testing using the Bonferroni method to detect significant results. The second objective is to perform a gene-set burden analysis of the most enriched variants from the previous goal, using gene-list from highly constrained genes according to gnomAD (pLI>0.9), the Molecular Signatures database Hallmark and C2 curated gene-sets, and highly expressed genes from 54 specific GTEx tissues, in order to identify molecular pathways, biological processes and tissue-specific expression patterns enriched. Here we will use the SKAT-O software to perform the variant enrichment on each geneset with the same covariates used on the first objective. We will use 10,000 permutations and a family-wise error rate (FWER< 0.05) as correction for multiple testing to select the most enriched gene-sets and tissues. The third objective will be to run a gene-wise burden test and perform a protein-protein interaction network along with enrichment in brain single-cell expression data, in order to prioritize significant candidate genes. Here we will map variants to single genes and use SKAT-O in a similar way to the previous objective. Then we will take all genes with uncorrected P< 0.05 and run a protein-protein interaction network with WebgestaltR, using the network-topology analysis and random walk algorithm, and Gene-Ontology enrichment of the resulting network using BIOGRID. We will use STRING in order to get network interaction significance.
    Non-Technical Research Use Statement:
    We will assess the load of rare variants in the 10,088 ADSP Discovery Case Control WES samples across the whole-exome, biological pathways and genes in a “hypothesis-free” approach, leveraging state-of-the art variant annotation tools and databases. We aim to detect the most enriched type of variants in cases that increase risk, including different ages of onset. This approach will help us to increase our power to reveal novel biological pathways and genes associated with AD, expanding the understanding of rare variation and their implication on disease risk.
  • Investigator:
    Vogel, Briana
    Institution:
    University of Pennsylvania
    Project Title:
    Test 2
    Date of Approval:
    September 9, 2020
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    test
    Non-Technical Research Use Statement:
    test
  • Investigator:
    Wang, Li-San
    Institution:
    University of Pennsylvania
    Project Title:
    ADSP Data Processing
    Date of Approval:
    February 26, 2020
    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. NIAGADS will work with ADSP investigators to develop a plan for reviewing incoming sequencing data. This will be done in parallel with basic quality assurance procedures by sequencing center partners 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:
    Expired
    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, 2020
    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:
    Expired
    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:
    Yesavage, Jerome
    Institution:
    Stanford University
    Project Title:
    Identifying Variable Number Tandem Repeats Associated with Alzheimer Disease in Diverse Populations
    Date of Approval:
    December 18, 2019
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    This goal of this study is to examine the presence of Variable Number Tandem Repeats (VNTRs) in Alzheimer’s disease (AD) population. Within a genome exists short sequences of repeating DNA. While the repeated sequence (usually >6 bases in length) is usually conserved within a population, the number of times the sequence is repeated in any given individual varies. These genetic variants are known as VNTRs and the number of these repeats can be considered a polymorphism, with individuals or families having a different number of repeats than those seen in the general population. In the past VNTRs have been an overlooked polymorphic component of the genome even though a number of VNTRs have been shown to be associated with neurological disorders and brain functions. We aim to understand not only the presence of specific VNTRs associated with Alzheimer’s disease but also how different phenotypes influences the relationship of these VNTRs to AD, this may inform precise genetic profiles which can be applied to a diverse population. As the associative genetics of AD is known not to be conserved across races it is important to not only assess the dataset as a whole but also the association of the ethno-racial phenotypes to inform these precise genetic profiles. If we can use VNTRs to predict this debilitating disorder it opens up avenues to apply treatments earlier and impact one of the most prevalent social and economic burdens on our society. Whole genome sequence data will be analyzed using VNTRseek against a set of reference tandem repeats generated from the tandem repeats database. This software determines the presence of a particular repeat. Each output will contain the number of repeats for each VNTR for each genome and this will be filtered for repeats that have a variation from the reference to determine the presence of a VNTR. This will be used to determine allelic frequency of specific polymorphic repeats comparing controls to AD. We will also further separate the analysis into gender and the different ancestral phenotypes in the ADSP to assess for specific alleles that may be more associated in one ethno-racial group than another.
    Non-Technical Research Use Statement:
    Within a genome exists short sequences of repeating DNA. While the repeated sequence (usually >6 bases in length) is usually conserved within a population, the number of times the sequence is repeated in any given individual varies. These variants are known as Variable Number Tandem Repeats (VNTRs) and the number of these repeats can be considered a polymorphism. In the past VNTRs have been an overlooked polymorphic component of the genome even though a number of VNTRs have been shown to be associated with neurological disorders and brain functions. This study will examine the association of VNTRs in Alzheimer’s disease (AD) population using a specialized program, VNTRseek, to explore the presence of these VNTRs in the whole genome sequences from AD cases and controls. We believe analyzing the distribution of VNTRs in a large and diverse AD population may yield new associative genetic alleles which may not only assist in the prediction of AD development but identify new cellular pathways of interest in understanding the pathophysiology of the disorder.
  • 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:
    Expired
    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:
    April 6, 2020
    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.