Alzheimer’s Disease Sequencing Project

Background

The Alzheimer's Disease Sequencing Project (ADSP) was launched in 2012 in response to the National Alzheimer's Project Act (NAPA), which aimed to improve the health outcomes of people with Alzheimer's disease (AD) and AD-related dementias (ADRD). The overall goal of the ADSP is to expand and coordinate genetics research of AD/ ADRD across diverse populations, globally. The ADSP consortia utilize well-characterized genetically diverse cohort samples to identify common, rare, and structural variations believed to play an important role in AD disease progression or protection.

Program Objectives

  1. Identify new genes involved in AD/ADRD in diverse populations.
  2. Identify gene alleles contributing to increased risk for or protection against the AD/ADRD in diverse populations.
  3. Provide insight as to why individuals with known risk factor genes escape from developing AD/ADRD or have a delayed onset of developing AD/ADRD.
  4. Investigate the impact of genetic admixture on AD risk, resistance, or resilience.
  5. Maximize the scientific and translational impact of the genetic discoveries by ensuring that the large-scale AD genetics data, methods and analyses are shared rapidly and broadly with the external research community.
  6. Facilitate the uptake of ADSP resources by the external research community through effective outreach and communication.
  7. Develop metrics to quantify the impact of ADSP resources on basic and translational AD/ADRD research in support of NIA’s overall strategy to enable a precision medicine approach to AD/ADRD treatment and prevention.

The ADSP consists of large components, seeking to advance AD genetic research.

  • The objective of the Coordination Center is to manage and lead the administrative functions within the ADSP consortium, ensuring effective communication, fostering collaborations between members, and promoting outreach, resource dissemination, and training within the global AD research community.
  • ADSP Sequencing, Analysis and Infrastructure: The ADSP Consortia has facilitated the discovery of loci associated with AD/ADRD through a wide range of initiatives related to the recruitment of diverse participants and population samples, large scale sequencing data collection, and harmonization resulting in the release of 5 whole genome/ whole exome sequencing dataset.
  • The Functional Genomics Consortium (FunGen) generates large-scale, high-throughput functional genome-wide Omics data to discover translational pathways leading from genetic variations to potential AD/ADRD therapeutic targets.
  • The Artificial Intelligence/Machine Learning Consortium (AI/ML) employs cutting-edge computational methods on genetic and Omics data derived from ethnically diverse individuals. These researchers use cognitive systems analytical approaches to integrate and analyze ADSP data and optimize subject selection for clinical trials. The consortium also generates open-source technology that is made available to the research community.

Data Access and Sharing

The ADSP consortium along with cooperative agreements through the National Institute on Aging (NIA) ensures researchers all over the world have access to high quality genomics and associated omics data, along with standardized clinical and neuropathological research data.

Access to NIA designated AD/ ADRD data can be found:

  • Alzheimer’s Disease Knowledge Portal

    The Alzheimer’s Disease Knowledge Portal (ADKP) is a public data repository that features data and resources from over 100 grant projects across more than 10 programs or centers supported by the NIA. It includes "omics" data derived from human samples and experimental model systems, as well as bioinformatic analyses conducted by research teams and cross-consortium working groups.

List of Active Awards

The National Institutes of Health (NIH) provides resources with information about the grants, institutions and investigators it funds. To see a list of active ADSP grants, please visit NIH RePorter .

ADSP Coordination Center

  • Collaboration, Communication, and Coordination Center for ADSP (ADSP-4C) (Kenneth Rice)

ADSP Sequencing, Analysis and Infrastructure

  • The Alzheimer Disease Sequence Analysis Collaborative (Jonathan, Haines)
  • Alzheimer’s Disease Sequencing Project Phenotype Harmonization Consortium (Timothy, J Hohman)
  • Alzheimer’s Disease Genetics Consortium (Gerard, Schellenberg)
  • Genome Center for Alzheimer’s Disease (GCAD) (Gerard, Schellenberg)
  • Additional Sequencing for the Alzheimer Disease Sequencing Project (ADSP) the Follow-Up Study (FUS), The Diverse Population Initiative (Margaret, A. Pericak-Vance)
  • The NIA Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS) (Li-San, Wang)
  • Asian Cohort for Alzheimer’s Disease (ACAD) (Li-San, Wang)

ADSP Functional Genomics (FunGen)

  • Alzheimer Variants: Propagation of Shared Functional Changes Across Cellular Networks (Phillp L De Jager)
  • Multi-Omic Functional Assessment of Novel AD Variants Using High-Throughput and Single-Cell Technologies (Thomas J Montine)
  • Functional Genomic Dissection of Alzheimer’s Disease in Humans and Drosophila Models (Joshua M Shulman)
  • Investigating the Functional Impact of AD Risk Genes on Neuro-Vascular Interactions (Sally Temple)
  • Functional Genomic Studies in Diverse Populations to Characterize Risk Loci for Alzheimer Disease (Jeffery Vance)
  • Circular RNAs and Their Interactions With RNA-Binding Proteins to Modulate AD-Related Neuropathology (Xiaoling Zhang)

ADSP Artificial Intelligence & Machine Learning (AIML)

  • Alzheimer’s MultiOme Data Repurposing: Artificial Intelligence, Network Medicine, and Therapeutics Discovery (Feixiong Cheng)
  • Assessing Alzheimer Disease Risk and Heterogeneity Using Multimodal Machine Learning Approaches (Honghuang Lin)
  • Cognitive Computing of Alzheimer’s Disease Genes and Risk (Olivier Lichtarge)
  • Artificial Intelligence Strategies for Alzheimer’s Disease Research (Jason Moore)
  • Causal and integrative deep learning for Alzheimer’s disease genetics (Wei Pan)
  • Ultrascale Machine Learning to Empower Discovery in Alzheimer's Disease Biobanks (Paul M Thompson)
  • Learning the Regulatory Code of Alzheimer’s Disease Genomes (Raj Towfique)
  • AIM-AI: an Actionable, Integrated and Multiscale genetic map of Alzheimer’s disease via deep learning (Zhongming Zhao)
  • Genetics of Deep-Learning-Derived Neuroimaging Endophenotypes for Alzheimer’s Disease (Degui Zhi)

External Program Consultant

The External Program Consultant (EPC) provides feedback to NIA and ADSP investigators to enhance the effectiveness and alignment of study aims. EPC members are not directly involved in the research of the ADSP, but are charged with the following:

  • Providing feedback to the NIA and ADSP investigators on the overall progress of the consortium toward meeting its scientific goals with guidance on future directions of the consortium.
  • Attending the annual program review meeting and providing NIA with a written report summarizing the evaluations and recommendations for the consortium.
  • Reviewing the progress of individual projects and contributing scientific insights on issues and questions for consideration by the Principal Investigators.
  • Sharing knowledge and guidance on best practices in research or advanced methodologies applied in other fields that may be relevant to the ADSP.

EPC Members:

  • Kristen Brennand, PhD (Yale University, School of Medicine)
  • Nilanjan Chatterjee, PhD, MS (Johns Hopkins University, Bloomberg School of Public Health)
  • Maria Carrillo, PhD (Alzheimer’s Association)
  • Ron Do, PhD (Mount Sinai, Icahn School of Medicine)
  • Albert La Spada, MD, PhD, FACMGG (University of California Irvine, School of Medicine)
  • Sriram Sankararaman, PhD (University of California Los Angeles).
  • Sarah Tishkoff, PhD (University of Pennsylvania, Perelman School of Medicine)
  • Donna Wilcock, PhD (Indiana University, School of Medicine)
  • Wei Zheng, MD, PhD, MPH (Vanderbilt University, Medical Center)

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