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NIA Alzheimer's Disease Genetics Portfolio

D N A double helix

Identifying and Understanding Alzheimer's Disease Genes

Researchers have identified dozens of genes involved in Alzheimer’s disease and what role they may play. Read about NIA's continued research into the genetics of Alzheimer's disease.

Background

The Genetics of Alzheimer’s Disease Portfolio supports research to discover long-term treatments for the disease by the identification of risk factor and protective genes and the underlying molecular pathways. Researchers have identified more than 50 regions of the genome (called loci) that contain variants that may increase risk for the disease. Variations in more than 23 individual genes within the 50 loci have been demonstrated to be associated with increased risk for late-onset Alzheimer’s disease (AD).

Current and future research is targeted to:

  1. Discovery of additional genes and loci that contain gene regulators
  2. Determining the association of risk factor genes on disease progression
  3. Determining the function of identified genes
  4. Determining the influence of genes on specific disease biomarkers
  5. Identifying the underlying molecular pathways
  6. Determining why some individuals who have risk factor genes can escape the disease

The portfolio supports a wide range of NIH funding mechanisms. Researchers funded under the Genetics of Alzheimer’s Disease Portfolio identify and explore genes to determine their influence on the age of disease onset and the rate of progression through various disease phases, from the first symptoms through mild cognitive impairment to full-blown disease. They are also examining genes associated with specific disease biomarkers, such as the number of amyloid plaques or neurofibrillary tangles, concentrations of beta-amyloid and tau in cerebral spinal fluid, and responses to environmental factors (e.g., drugs and nonpharmaceutical factors). Recent accomplishments in the AD genetics portfolio include robust expansion of multi-ethnic cohorts, the development of new analytical approaches, and broad data sharing with the research community.

Evolution of the Alzheimer's Disease Genetics Portfolio

The Alzheimer’s Disease Genetics Portfolio was established at NIA in 2002. Since its formation, enormous advances in understanding the genetics of Alzheimer’s disease have been achieved. The advent of genome wide association studies (GWAS) and high-throughput technologies greatly facilitated the identification of risk factor genes for AD. The advanced technologies include whole genome, whole exome, and targeted sequencing analysis. Identification and elucidation of risk and protective genes for AD — and determining the function of the AD genes — enables the understanding of disease development to target potential pathways for treatment or disease prevention. AD geneticists have identified genetic hubs that have clusters of genes in the same functional pathways. An important goal is to define the subtypes (subgroups of symptoms called endophenotypes) of AD that are indicated by the gene clusters. Finding groups of people with the same genetic endophenotypes may help in the selection of participants for clinical trials that are targeted to these pathways.

Broadening the Definition of Alzheimer's Disease

Consistent with the National Alzheimer’s Project Act (NAPA), NIA updated the definition of AD to establish data-sharing criteria to include Alzheimer’s disease-related dementias (ADRD). ADRD include frontotemporal dementia, Lewy body dementia, vascular contributions to cognitive impairment and dementia, and mixed dementias. The redefinition is enabling the advancement of research on dementia-causing disease processes commonly embedded in and/or difficult to distinguish from AD. The update recognizes the scientific advances in recent years in understanding the variety of pathologies and genetic influences associated with the full spectrum of dementia in individuals (NOT-AG-17-007).

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Alzheimer’s Disease Sequencing Project

Funded under a number of cooperative agreements and research grant awards, studies on the discovery of genes involved in AD are robustly supported by the Alzheimer’s Disease Sequencing Project (ADSP). The ADSP comprises more than 150 investigators from dozens of institutions across the United States and in 20 countries across the globe including 33 institutions, and more than 60 cohorts, as of 2020. The ADSP aims to identify both genes that increase risk for AD and those that confer protection, as well as to provide insight into why some people with known risk factor genes don’t develop AD. The effort also aims to identify potential avenues to prevent and/or treat AD.

Learn more about the ADSP Study Design
 

Genetic samples and phenotypic data that are analyzed by the ADSP are provided by several consortia, initiatives, centers, and studies. See Infrastructure Support below for more information.

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New Initiatives Supporting the ADSP

ADSP Functional Genomics

Functional interpretation of genetic variations has been challenging and remains a persistent bottleneck in genetic studies of complex diseases, hindering the discovery of targets for biomarkers and treatments. In 2020, NIA launched the ADSP Functional Genomics Consortium (FGC) (RFA-AG-21-006) initiative to strengthen the translational pathways leading from genetic variations to potential targets. This initiative supports a concerted effort that takes a multipronged team-science strategy and applies large-scale, high-throughput genome-wide approaches to systematically characterize and validate biological functions and cellular mechanisms of genetic variants and genes underpinning the heterogeneity of AD/ADRD. The goals of this initiative are to:

  • Identify and validate causal variants that affect disease susceptibility and/or progression
  • Characterize the molecular and physiological effects of these variants and identify target genes on which these variants act and the cell types and states in which these variants operate
  • Determine links to heterogeneous cellular and pathologic mechanisms and elucidate mechanistic understanding
  • Identify genetic drivers and their mechanisms of action underlying AD endophenotypes that are clinically relevant but difficult to ascertain

Projects funded by this RFA will form the ADSP-FGC and will focus on revealing and validating biological functions of a broad range of coding and noncoding functional genomic elements including ADSP-identified variants.

The ADSP-FGC represents a large integrated collaborative effort in AD/ADRD to characterize genomic elements and provide novel insights into the landscape of functional genomic elements at the molecular and cellular level. The greater understanding of genomics and mechanisms is critical in translating genetic discoveries into mechanistic insights to inform translational and clinical research. The ADSP-FGC will work with the Accelerating Medicines Partnership-Alzheimer’s Disease (AMP-AD) through complementary approaches to accelerate the identification of therapeutic targets for AD/ADRD endophenotypes and disease subtypes to inform precision AD medicine.

The ADSP Phenotypic Data Harmonization Consortium

ADSP genetic data are quality control checked and harmonized across all cohorts and all phases of the study by the Genome Center for Alzheimer’s Disease (GCAD). In order to perform artificial intelligence/machine learning/deep learning analysis on these data, the related phenotypic data must also be harmonized. NIA issued an announcement to support the effort (PAR 20-099). The intent of this initiative is to generate a legacy dataset that involves all types of ADSP data, curate the dataset regularly, maintain the dataset in perpetuity, and share the data through the NIA Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS). Phenotypic data will be harmonized for at least 60 ADSP cohorts, and at least seven types of data will be harmonized for thousands of participants.

The ADSP Phenotypic Data Harmonization Consortium will facilitate and support the harmonization and analysis of large-scale phenotypic data for the next phase of the ADSP activities. It will act as an interface between study leads contributing cohorts and studies, the ADSP investigators, NIAGADS, and the AD research community, including researchers involved in the artificial intelligence/machine learning/deep learning efforts. The effort will serve as a means to improve our understanding of endophenotypes of AD/ADRD in order to better select participants for clinical trials and accelerate identification of therapeutic targets for AD/ADRD.

Artificial Intelligence/Machine Learning/Deep Learning Approaches to Analyzing ADSP Data

The amount of data that the ADSP will generate is enormous. In 2023, the ADSP will have 11.4 petabytes of data for analysis, where one petabyte is one quadrillion bytes of data. Some examples of this big data are:

  • 94,437 participants with GWAS for AD/ADRD data
  • 27,742 whole genomes expected to be ready for analysis in 2021
  • Approximately 68,000 whole genomes by 2023 when sample sets from cohorts in India, China, Japan, Australia, Iceland, Iberian Peninsula, Central and South America, Australia, and South Korea are included. When data from other NIH-funded large-scale sequencing projects are harmonized with ADSP data, the number of participants will increase to about 114,000.
  • Most of the NIA-funded epidemiologic cohorts have genetic data; some have “omic” data.

Artificial intelligence, machine learning, and deep learning — collectively called cognitive systems — provide solutions that can empower geneticists to rapidly augment gene discovery in big, complex datasets. Technological advancements in conjunction with the formation of large, highly collaborative consortia have augmented the capabilities to successfully identify AD genes. NIA issued PAR-19-269 to apply cognitive systems approaches to the analysis of AD genetic and related data.

Under this initiative, a large amount of existing GWAS, whole genome, whole exome, genomic, endophenotypic, clinical, and epidemiological data from ethnically diverse affected and unaffected individuals will be harmonized and analyzed using cognitive systems analytical approaches. Analysis of these data will identify new genes and genetic pathways that will reveal risk and protective factors for AD and guide the field toward novel therapeutic approaches to the disease. Harmonized, final outcome, and other data will be shared with the research community through NIAGADS. Open source technology, including all noncommercial software and hardware designs and technical data will be provided to the research community in a transparent fashion. Intellectual property rights asserted by applicants will be aligned with open source management. Investigators will submit applications that may include ADSP investigators, geneticists, and other experts with expertise in complex neurological diseases; private industry partners; and academicians with expertise in cognitive systems approaches.

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Infrastructure Support for the Genetics of AD Portfolio

Several NIA-funded consortiums, centers, and repositories help support the work of the ADSP and AD genetics research.

Alzheimer’s Disease Genetics Consortium (ADGC)

ADGC is a large and highly collaborative U.S. consortium dedicated to AD genetics research. It has been a major contributor to the identification of many of the recently identified genetic variants known to late-onset AD. Working with the Alzheimer’s Disease Research Centers (ADRCs), the National Alzheimer’s Coordinating Center (NACC), and the National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD), the ADGC identifies and studies the genetics of well-characterized AD patients and cognitively unaffected controls. The ADGC also studies participants from families multiply affected by the disease. Single nucleotide polymorphisms, whole exome, and whole genome sequences from study participants are generated using cutting-edge DNA technologies. Several ethnically diverse cohorts have been recruited through the ADGC. These genetic data are analyzed together with genetic data from other participating studies to identify genes contributing to the risk of AD.

Learn more about ADGC
 

The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE)

The CHARGE Consortium was formed to facilitate GWAS meta-analyses and replication opportunities among multiple large population-based cohort studies, which collect data in a standardized fashion. The consortium includes five prospective cohort studies from the United States and Europe:

  • Age, Gene/Environment, Susceptibility-Reykjavik Study (AGES-Reykjavik)
  • Atherosclerosis Risk in Communities (ARIC) Study
  • Cardiovascular Health Study
  • Framingham Heart Study
  • Rotterdam Study

With genome-wide data on several thousand individuals, these cohort studies have a large number of health-related phenotypes, including AD, measured in similar ways.

Learn more about CHARGE
 

The Collaborative for Alzheimer's Disease Research (CADRE)

The diverse ADSP datasets require a comprehensive analytical effort to uncover the wealth of information hidden in the sequence data. CADRE is studying whether protective and risk genomic variants will provide potential therapeutic targets for Alzheimer's disease. The collaboration is performing comprehensive genomic analysis of ADSP data with clinical and other biological data to identify the highest priority variants and loci as candidates for downstream functional analysis. The team is characterizing genomic variation in late-onset AD, enhancing discovery and fine-mapping in ethnically diverse datasets, prioritizing variants and genes, and integrating statistical and biological information. CADRE is then analyzing structural and functional genomics data and integrating data into a comprehensive biological network that will be used to prioritize loci for functional testing as therapeutic targets.

Learn more about CADRE
 

Genome Center for Alzheimer’s Disease (GCAD)

GCAD serves as a national resource for identifying genetic and genomic factors to identify potential avenues for therapeutic approaches and prevention. All sequence data generated by the ADSP are provided to GCAD. GCAD captures whole genome sequence (WGS)/whole exome sequence (WES) data, genotype data, and analysis data from the sequencing centers; performs quality control checks and variant calling using the established ADSP methodology; and ensures rapid data sharing, receiving, and managing for the ADSP according to existing ADSP and NIA policies. GCAD supports the coordinated efforts to analyze ADSP data using artificial intelligence, machine learning, and deep learning approaches. GCAD also works with the FGC to coordinate harmonization of relevant functional data for the research community. GCAD has broad interaction with the research community and coordinates data collection and sharing with ADGC, NCRAD, ADRCs, NACC, CHARGE, the International Genomics of Alzheimer's Project (IGAP), the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Trans-Omics for Precision Medicine (TOPMed), and the Centers for Common Disease Genomics (CCDG).

To date, GCAD has processed and released 19,922 WES and 4,789 WGS samples. In 2020, another 12,890 WGS samples have been processed, including samples from the ADSP Follow-Up Study, AMP-AD, and other projects. In 2020/2021, all of these 17,679 WGS samples will be harmonized and released to qualified researchers.

Learn more about GCAD
 

The NIA Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS)

NIAGADS organizes, stores, and shares genetic, genomic, and phenotypic data, including clinical and neuropathology data, from NIH-funded genetic studies in a secure environment. NIAGADS currently hosts 70 high-quality human genetics datasets in addition to ADSP data, corresponding to 86,819 participants, and has a genomics database for cross-referencing and visualizing known genomic variants and annotations with AD genetic analysis findings.

The ADSP provides genetic data from large numbers of genetically informative, multi-ethnic, phenotypically well-characterized families having multiple individuals affected with AD, and includes AD/ADRD cases and controls. NIAGADS is the ADSP Data Coordinating Center that supports ADSP data production, management, and sharing. A partial list of NIAGADS’s many responsibilities includes scheduling, preparing, and maintaining public data releases. NIAGADS also maintains the ADSP website and facilitates community access to ADSP data.

NIAGADS was charged with developing a HIPAA/FISMA-compliant, fully featured data-sharing service for ADSP using cloud technology. The NIAGADS Data Sharing Service (DSS) was launched in July 2018 and facilitates the deposition and sharing of genomic data from the ADSP and other NIA-funded AD/ADRD genomic studies with approved users in the research community. NIAGADS provides qualified investigators with several different types of data from genetic/genomic studies, including high-density genotyping and sequencing data, extensive phenotype data, and summary statistics from published genetic studies.

The DSS currently hosts 4,789 whole genomes and 19,922 whole exomes. Later in 2020 there will be a total of ~17,000 whole genomes and 19,922 whole exomes available through NIAGADS DSS.

Learn more about NIAGADS
 

National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD)

NCRAD is a state-of-the-art repository for DNA samples, cell lines, plasma, serum, RNA, brain tissue, cerebrospinal fluid, and peripheral blood mononuclear cells or fibroblasts. NCRAD does the following:

  • Provides management of samples, and receives and organizes previously collected biosamples and new biospecimen collections
  • Provides scientific and laboratory expertise to apply cutting-edge science to the development and use of biospecimen protocols and quality analysis
  • Coordinates the receipt, processing, storage, and distribution of biospecimens for the ADSP
  • Coordinates with a range of stakeholders including government, academic scientists, industry, and data-management experts such as NACC and NIAGADS
  • Implements data and sample sharing and protocol standardization for sample acquisition
  • Allocates the samples to the sequencing centers
  • Coordinates requests to the ADRCs to provide samples
  • Coordinates with ADRCs to provide any sample replacements at the sequencing centers

To facilitate sample sharing and increase the rate of research discovery, NCRAD is also banking induced pluripotent stem cells (iPSCs) and fibroblasts.

Learn more about NCRAD
 

The NIA Late Onset Alzheimer’s Disease (LOAD) Family Based Study (FBS)

NIA LOAD FBS began in 2003 with a goal of recruiting large multiply affected families with late-onset Alzheimer’s disease for genetic research. The study has created a resource of well-characterized families with late-onset AD. The initial phases of the ADSP included genotyping of hundreds of participants from NIA LOAD FBS. The ADSP Follow-Up Study heavily engages resources provided by the NIA-LOAD FBS and depends upon the longitudinal follow-up of families, and the collection of additional families, in particular those from diverse populations.

NIA-LOAD FBS is extending recruitment within each family to other affected family members and to the next generation, the offspring (adult children) of the probands and their siblings. The NIA-LOAD FBS is also extending the collection of biological materials beyond DNA. Samples will include biological materials for GWAS and WGS; peripheral blood mononuclear cells (PBMC) for stem cell modeling; plasma for studies of metabolomics, proteomics, and biomarker research; and brain autopsy materials for bulk RNA sequencing. These biological materials will be made available to the larger scientific community through NCRAD. Genetic, genomic, and related phenotypic data will be made available through NIAGADS.

Taken together in 2020 NIA-LOAD FBS had collected genetic and phenotypic data on 9,840 individuals from 2,383 multi-ethnic families. As an essential research resource, the NIA-LOAD FBS acts as the key provider of the biological materials and clinical data on large multiplex families not only for the ADSP, but also for ADGC, CADRE, and the ADRCs. Other ongoing collaborations include the Uniformed Services University of the Health Sciences (USUHS) large-scale sequencing center, GCAD, NACC, and AMP-AD.

An important aspect of the function of the NIA-LOAD FBS is to continue to coordinate with other components of essential infrastructure related to the ADSP and to provide infrastructure support in the form of biological materials and clinical data for NIA-funded genetic research on AD.

Learn more about LOAD FBS
 

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    Essential Documents for Data Sharing

    To ensure researchers have access to data that could lead to products and knowledge that benefit public health, NIH advocates for broad sharing of research resources when possible. Specifically, NIH promotes sharing of large-scale and nonhuman genomic data generated from NIH-funded research. There are three documents associated with Alzheimer’s disease genetics sharing that investigators must complete:

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    Funding Opportunity Announcements Related to AD Genetics

    Active funding opportunity announcements

    • Notice of Information: Alignment Among the Disease Definitions Utilized to Govern Genetic and Genomic Data Sharing for Studies Involving Alzheimer's Disease NOT-AG-17-007
    • Notice to Specify High-Priority Research Topic for PAR-19-070 and PAR-19-071 NOT-AG-18-053 (PAR-19-070 and PAR-19-071 expiration date: Nov. 13, 2021)
    • Limited Competition: Renewal of, and Revisions to, the Alzheimer's Disease Genetics Consortium (U01 Clinical Trial Not Allowed) PAR-18-889 — expiration date: Sept. 8, 2021
    • Limited Competition: Additional Sequencing for the Alzheimer's Disease Sequencing Project: Opportunity for Revision Requests for Active Cooperative Agreements (U01 Clinical Trial Not Allowed) PAR-18-890 — expiration date: Sept. 8, 2021
    • Limited Competition: Additional Sequencing for the Alzheimer's Disease Sequencing Project (U01 Clinical Trial Not Allowed) PAR-19-234 — expiration date: May 8, 2022
    • Cognitive Systems Analysis of Alzheimer's Disease Genetic and Phenotypic Data (U01 Clinical Trial Not Allowed) PAR-19-269 — expiration date: Sept. 28, 2022
    • Limited Competition: NIA Genome Center for Alzheimer's Disease (GCAD) (U54 Clinical Trial Not Allowed) PAR-19-288 — expiration date: Sept. 26, 2022
    • Harmonization of Alzheimer’s Disease and Related Dementias (AD/ADRD) Genetic, Epidemiologic, and Clinical Data to Enhance Therapeutic Target Discovery (U24 Clinical Trial Not Allowed) PAR 20-099 — expiration date: Jan. 26, 2023
    • National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (U24 Clinical Trial Not Allowed) PAR-20-110 — expiration date: Jan. 26, 2023
    • Notice to Specify High-Priority Research Topic for PAR-19-070 and PAR-19-071 NOT-AG-18-046 (PAR-19-070 and PAR-19-071 expiration date: Nov. 13, 2021)
      • Genetic Underpinnings of Endosomal Trafficking as a Pathological Hub in Alzheimer's Disease and Alzheimer's Disease-Related Dementias (AD/ADRD)

    Past funding opportunity announcements

    • National Institute on Aging Analysis of Alzheimer's Disease Genome Sequencing Project Data [U19] PAR-12-183
    • Centers for Common Disease Genomics (UM1) RFA-HG-15-001
    • NIA Coordinating Center for Genetics and Genomics of Alzheimer's Disease (U54) RFA-AG-16-001
    • Alzheimer's Disease Sequencing Project (ADSP) Replication Phase Analysis Studies (U01) RFA-AG-16-002
    • Notice of Information: The Alzheimer's Disease Sequencing Project Policy (ADSP) on the Publication of Study-Related Data NOT-AG-16-033
    • The National Institute on Aging (NIA) Late Onset of Alzheimer’s Disease (LOAD) Family-Based Study (FBS) (U24) PAR-16-205
    • Limited Competition: Additional Sequencing for the Alzheimer's Disease Sequencing Project (U01) PAR-16-406
    • Notice to Specify High-Priority Research Topics for PAR-18-596 NOT-AG-18-001
    • Alzheimer’s Disease Sequencing Project Functional Genomics Consortium (U01 Clinical Trial Not Allowed) RFA-AG-21-006
    • Limited Competition: Analysis of Data from NIA's Alzheimer's Disease Sequencing Project Follow-Up Study (U01) PAR-17-214

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