NIA Alzheimer's Disease Genetics Portfolio

The Diverse Population Initiative (PAR-21-212) was launched in 2021 to expand the sample set in ADSP to represent more diverse populations. The long-term goals are to: 

• Move the field closer to enabling the prediction of who will develop AD;
• Fully characterize AD subtypes by studying endophenotypes in diverse populations; 
• Better understand the differences in the genetic underpinnings of AD pathogenesis among diverse populations; and 
• Identify specific genetically driven therapeutic targets based on a diverse population.

The primary focus of this study is on Hispanic/Latino, Black/African American, and Asian populations. The numbers of Hispanic/Latino, Black/African American, and Asian participants included in the ADSP Follow UP Study are presently insufficient to provide statistical significance for the identification of rare or very rare variants. Variants in the Alzheimer’s genome are largely rare or very rare in the population. It is estimated that for 80% certainty for single variant testing for rare variants, ~16,100 cases and ~16,100 controls are needed for a variant with a minor allele frequency of 0.5% in the population; single variant testing for rare variants indicate that for 90% certainty, ~18,500 cases and ~18,500 controls are needed for each population for a variant with a minor allele frequency of 1% in the population.  This study will ensure that there are sufficient numbers of study participants to achieve statistical power for analysis of rare or very rare variants in the three largest diversity cohorts’ AD/ADRD genome given the available funding. Consortia are leveraging cohorts already recruited or in planning for recruitment to obtain sufficient numbers; sharing diversity data across consortia is essential to the success of this effort. Genetic samples and phenotypic data analyzed by the ADSP are provided by several consortia, initiatives, centers, and studies. See Infrastructure Support below for more information.

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 Initiative (RFA-AG-21-006) to strengthen the translational pathways leading from genetic variations to potential targets. This initiative takes a multipronged team-science strategy and applies large-scale, high-throughput genome-wide approaches to systematically discover and characterize functional genomic elements and elucidate and validate their functional roles and mechanisms of action underpinning the heterogeneity, pathogenesis, and progression of Alzheimer’s disease and related dementias.

NIA awarded the following six projects in 2021 that comprise the core projects of the new ADSP Functional Genomics Consortium:

• Alzheimer’s variants: Propagation of shared functional changes across cellular networks (U01AG072572)
  • Principal Investigator: Philip L. De Jager, Columbia University
• Multi-omic functional assessment of novel AD variants using high-throughput and single-cell technologies (U01AG072573)
  • Principal Investigator: Thomas J. Montine, Stanford University
• Functional genomic dissection of Alzheimer’s disease in humans and drosophila models (U01AG072439)
  • Principal Investigator: Joshua M. Shulman, Baylor University
• Investigating the functional impact of AD risk genes on neurovascular interactions (U01AG072464)
  • Principal Investigator: Sally Temple, Regenerative Research Foundation
• Functional genomic studies in diverse populations to characterize risk loci for Alzheimer Disease (U01AG072579)
  • Principal Investigator: Jeffery M. Vance, University of Miami
• Circular RNAs and their interactions with RNA-binding proteins to modulate AD-related neuropathology (U01AG072577)
  • Principal Investigator: Xiaoling Zhang, Boston University

The Consortium aims to elucidate the causal path linking Alzheimer's disease and Alzheimer's disease-related dementias (AD/ADRD) associated alleles to disease susceptibility by characterizing the impact of DNA sequence variants on the regulation and function of genes (e.g., altered expression and/or protein function), defining how these variants interact with one another, and understanding their molecular, cellular and tissue impact on nervous system health. The Functional Genomics Consortium will coordinate efforts to identify the causal elements, genes and pathways, and the specific cell types and contexts in which they act to modulate disease risk, onset, or progression, for a better understanding of disease biology and the generation of novel therapeutic modalities. The Consortium will work with the Accelerating Medicines Partnership® Alzheimer’s Disease (AMP® AD) through complementary approaches to accelerate the identification of therapeutic targets to inform precision medicine for AD/ADRD.

Functional genomic studies span a continuum of high-throughput genome-scale approaches to more granular, in-depth investigations of specific genes and variants and can involve a broad range of computational and experimental strategies. The omic datasets, computational tools, and cellular and animal models developed by the Consortium will be made available to the larger scientific community.

The xQTL project, a collaborative effort across the Functional Genomics Consortium, is designed to generate a reference map of Alzheimer’s-related quantitative trait loci (QTLs) to determine the effect of genetic variation on molecular traits. The multi-omic approach will enable mapping the propagation of functional consequences of each variant, which will help identify the causal gene(s) in each locus as well as novel biomarkers and therapeutic targets. The project builds on existing datasets from multiple omics layers including bulk and single cell transcriptomics, epigenomics, proteomics, lipidomics, and metabolomics, and multiple tissues including brain, CSF, and plasma. This xQTL reference map will be made available to the general scientific community in 2022.

More information on the Functional Genomics Consortium can be found here.

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). To perform classical genetic data analysis or artificial intelligence/machine learning/deep learning analysis on these data, the related phenotypic/clinical 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 phenotypic/clinical data, curate the dataset regularly, continuously maintain the dataset, and share the data through the NIA Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS). Phenotypic data will be harmonized for at least 70 ADSP cohorts, and at least seven types of data will be harmonized for thousands of participants.

This 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 the identification of therapeutic targets for AD/ADRD.

The Alzheimer’s Disease Sequencing Project Phenotype Harmonization Consortium (PAR-20-099) is led by the following principal investigators: Timothy Hohman, Michael Cuccaro, and Arthur Toga.

The amount of data that the ADSP will generate is enormous. For example, by 2023 the ADSP will include approximately 68,000 whole genomes when sample sets from cohorts in India, Africa, Australia, Iberian Peninsula, Central and South America, 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. Artificial intelligence, machine learning, and deep learning — collectively called cognitive systems — provide solutions that can empower geneticists to amplify gene discovery in big, complex datasets. Technological advancements in conjunction with the formation of large, highly collaborative consortia have augmented our ability 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.

NIA currently funds the following ADSP artificial intelligence, machine learning, and deep learning projects:

• Learning the Regulatory Code of Alzheimer’s Disease Genomes, (U01 AG068880)
  • Principal Investigator: Towfique Raj
• Ultrascale Machine Learning to Empower Discovery in Alzheimer’s Disease Biobanks, (U01 AG068057)
  • Principal Investigator: Paul Thompson
• Alzheimer’s MultiOme Data Repurposing: Artificial Intelligence, Network Medicine, and Therapeutics Discovery, (U01 AG073323)
  • Principal Investigator: Feixiong Chen
• Assessing Alzheimer Disease Risk and Heterogeneity Using Multimodal Machine Learning Approaches, (U01 AG068221-01A1)
  • Principal Investigator: Honghuang Lin
• Cognitive Computing of Alzheimer’s Disease Genes and Risk, (U01 AG068214)
  • Principal Investigator: Olivier Lichtarge
• Genetics of Deep-Learning-Derived Neuroimaging Endophenotypes for Alzheimer’s Disease, (U01 AG070112)
  • Principal Investigator: Degui Zhi
• Artificial Intelligence Strategies for Alzheimer’s Disease Research, U01 AG066833)
  • Principal Investigator: Jason Moore
• Causal and integrative deep learning for Alzheimer’s disease genetics; (U01 AG073079)
  • Principal Investigator: Wei Pan

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 recently identified genetic variants known to be associated with 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/ADRD patients and cognitively unaffected controls. The ADGC also studies well-characterized AD/ADRD 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 this announcement (PAR-18-889) and the awarded project, the Alzheimer’s Disease Genetics Consortium, (U01 AG032984). The principal investigator for the consortium is Gerard Schellenberg.

Learn more about ADGC.
 

The CHARGE Consortium is a voluntary collaboration that 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 began with five prospective cohort studies from the United States and Europe but has expanded cohorts.

All cohorts are characterized by deep phenotyping over many years and repeated visits, especially for vascular and lifestyle measures. Cohorts also have ADRD endophenotypes such as MRI imaging, cognitive testing, blood biomarker and brain autopsy data that are being harmonized along with other data from other ADSP cohorts through the Phenotype Harmonization Consortium. With genome-wide array, sequence, and multi-omics data on thousands of individuals, these cohort studies contribute community representative ADRD cases and controls, and endophenotype data, drawn from diverse backgrounds. The ADSP will analyze a subset of CHARGE datasets with genetic and genomic data from the five ethnically diverse sample sets with cognitive testing.

This CHARGE award was funded under funding announcement (PAR-17-214).

Therapeutic target discovery in ADSP data via comprehensive whole-genome analysis incorporating ethnic diversity and systems approaches, (U01 AG058589). The principal investigator is Anita DeStefano.

Learn more about CHARGE
 

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 (AD) and related disorders (ADRD). 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 AD/ADRD, enhancing discovery and fine-mapping with a particular focus on datasets with diverse genetic ancestry. The team is also prioritizing variants and genes, and integrating statistical and biological information. CADRE combines these findings with additional structural and functional genomics data to integrate all these data into a comprehensive biological network to prioritize loci for functional testing as therapeutic targets.

Learn more about the CADRE funding opportunity (PAR-17-214) and awarded project:

The Alzheimer Disease Sequence Analysis Collaborative. The principal investigator is Jonathan Haines.

Learn more about CADRE.
 

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 works with the Functional Genomics Consortium (FGC) to coordinate harmonization of relevant functional data for the research community and coordinates data collection and sharing with the 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). Since 2017, GCAD has processed and released thousands of WES and WSG samples each year. In 2022, a total of 37,095 WGS samples will be harmonized and released to qualified researchers.

Learn more about GCAD.
 

NIAGADS is the NIA Genetics of Alzheimer's Disease Data Storage Site, a national genetics data repository that facilitates access to AD/ADRD genetic, genomic, and related phenotypic data by qualified investigators. NIAGADS archives, processes and distributes these data.  NIAGADS provides an integrated toolset for examining and comparing the genomes of affected and unaffected individuals. With it, you can locate single nucleotide polymorphisms (SNPs) and sequences on the reference genome.

There is a variety of data available from NIAGADS and sites that partner with NIAGADS.

• Genomic data from NIA-funded genetic studies
• Deep phenotype data and biomarkers
• Primary and secondary analyses including CHIP-Seq, RNA-Seq, and expression data
• Genome wide association studies
• Genetic data from case-control, family-based, and epidemiologic studies
• A variety of basic science and clinical research approaches
• Next generation and targeted genome sequencing

The NIAGADS Data Sharing Service (DSS) is a repository that facilitates the deposition and sharing of genomic data from the ADSP and other NIA funded Alzheimer’s Disease and Related Dementias genomic studies with approved users in the research community. Information on how to apply for the AD/ADRD genetic data are available here.

Learn more about NIAGADS.

The American Genome Center (TAGC) at the Uniformed Services University of the Health Science (USUHS) is a federal government academic large-scale sequencing center, supporting population genomics and precision health research studies.  The validated and operational infrastructure includes laboratory information management system-enabled robotic liquid handling platforms for automated sample processing and sequencing library preparation workflows, high performance compute cluster for quality control and genomic data analysis, petabyte-scale nearline data storage for staging data processes and redundant petabyte-scale tape archive platform for long-term data retention.  TAGC laboratories has validated workflows for transcriptomic, epigenomic, single-cell multiomic and NGS-based proteomic profiling.  TAGC has conducted large-scale human whole genome sequencing processing from well characterized DNA samples via the National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD) supporting the Alzheimer’s Disease Sequencing Project (ADSP), Alzheimer’s Disease Genetics Consortium (ADGC), the Alzheimer’s Disease Research Centers (ADRCs), the National Alzheimer’s Coordinating Center (NACC), the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the NIMH NeuroBioBank (NBB).  Across these consortium studies as of 2022, TAGC has generated whole genome sequencing data for over 35,000 study participants.

The goal of the National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD) is to support research focused on the etiology, early detection and therapeutic development for Alzheimer disease and related dementias. To accomplish this goal, NCRAD was first funded in 1990 by the National Institutes on Aging (NIA). NCRAD is a national resource where clinical information and biological materials, such as DNA, plasma, serum, RNA, CSF, cell lines, stool and brain tissue can be stored and requested. NCRAD currently maintains samples from individuals with Alzheimer’s disease and/or related dementias as well as healthy controls.

NCRAD has developed a series of services that are utilized by new studies to ensure uniform specimen collection so samples distributed for ADRD research are of the highest quality.  This includes development of a study specific manual of procedures which includes descriptive and pictorial information about sample collection, processing and shipping.  Site training is provided prior to the initiation of the study and sample kits with materials for samples to be collected, processed and shipped as well as an online kit request module is available to request the kit materials. Data reconciliation between NCRAD and the study’s data repository is completed on a regular basis to ensure all biospecimen data is accurate.  NCRAD’s ultimate objective of uniformly collecting and banking biospecimens is to distribute the samples for ADRD research.  Therefore, to make it easy for researchers to identify the samples they need for their research, NCRAD offers online biospecimen catalogs that can be queried by approved researchers to explore the range of biospecimens available at NCRAD for research use.  Studies with specimens available to request can be found on the NCRAD website.  Finally, with the establishment of the Biomarker Assay Laboratory, NCRAD has expanded our services to include processing of well-established fluid-based biomarkers and provide more support to research studies.  The goal is to ensure standardized processing and reliable research biomarker results.  This approach allows for longitudinal quality monitoring and consistent deliver of results over time, as well as the opportunity for cross-laboratory comparability studies.

Learn more about NCRAD.
 

The NIA Alzheimer’s Disease Family Based Study (NIA-AD FBS) began in 2003 to recruit and characterize large families with late-onset AD for genetic research. The initial phases of the ADSP included genotyping of hundreds of participants from NIA AD FBS. The ADSP Follow-Up Study heavily engages resources provided by the NIA Family Based Study (FBS). The Family Based Study has subjects from across the age spectrum (earlier to later in the disease state) and depends upon the longitudinal follow-up of families, and the collection of additional families from diverse populations. As an essential research resource, the NIA-AD 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), GCAD, NACC, and AMP AD.

The NIA-AD FBS plans to:

• Extend recruitment within each family to other affected family members, the next generation, the offspring (adult children) of the probands, and their siblings, which will inform the predictive utility of genetic variants and blood-based biomarkers.
• Enhance the representativeness of the families by recruiting additional multiplex families regardless of the age-at-onset and race/ethnicity. 
• Include blood-based biomarkers to provide a more precise diagnosis. 
• Provide samples of biological materials as well as genetic, genomic, and related phenotypic data to the larger scientific community through NCRAD and NIAGADS respectively.

The NIA-AD FBS will continue to coordinate with other components of essential infrastructure related to the ADSP and provide infrastructure support in the form of biological materials and clinical data for NIA-funded genetic research on AD. The NIA-AD FBS will remain a cornerstone of the research community’s success in the study of the genetic etiology of AD, the functional characterization of previously identified variants, and the delineation of the early progression of AD in multigenerational families with high a risk of AD.

Learn more about the NIA-AD FBS funding opportunity (PAR-16-205) and awarded project:

The National Institute on Aging (NIA) Late Onset of Alzheimer's Disease (LOAD) Family-Based Study. The principal investigator is Richard Mayeux.

Learn More About NIA-AD FBS