Alzheimer’s Disease Sequencing Project Phenotype Harmonization Consortium (ADSP-PHC)

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Description
PIs
Cohorts
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Description

Learn more:
vmacdata.org/adsp-phc

The Alzheimer’s Disease (AD) Sequencing Project (ADSP) was initiated in 2012 with a focus on identifying novel genes driving risk and resilience in AD and related dementias (ADRD). To date, the ADSP has curated sequencing data from 20,000+ individuals from 39 cohorts, which will increase to 100,000 participants across 70+ cohorts by 2023. The ADSP has focused primarily on AD case/control phenotypes derived from clinical data; however, advancements in our understanding of AD and movement toward a biological definition that integrates pathological and neurodegenerative aspects of the disease, many of which precede clinical symptoms by decades, has presented an opportunity and pressing need to integrate rich endophenotypic data and characterize the genetic architecture of these complex biological cascades in ADRD.

The ADSP Phenotype Harmonization Consortium (ADSP-PHC, U24-AG074855) was established in response to PAR-20-099 to harmonize the rich endophenotype data across cohort studies to enable modern genomic analyses of ADRD. The mission of the ADSP-PHC is to work in coordination with other ADSP workgroups and initiatives to streamline access to endophenotype data, provide high quality phenotype harmonization across domains, and provide comprehensive documentation of both data availability and harmonization procedures, with the goal of generating harmonized data that will become a “legacy” dataset perpetually curated and shared through NIAGADS.

The aims of the ADSP-PHC are below:

  1. Procure, collate and curate endophenotype data from ADSP cohort studies.
  2. Harmonize data from ADSP cohort studies leveraging advanced statistical approaches.
  3. Disseminate harmonized phenotypes and harmonization protocols to the research community.
  4. Educate the research community on available harmonized resources and best practices.

Current Documentation:

  • ADSP-PHC Read Me
  • ADSP-PHC Cognitive Harmonization Read Me and Cohort White Papers
  • ADSP-PHC Demographic Diagnosis Harmonization Read Me and White Paper
  • ADSP-PHC Fluid Biomarker-CSF Harmonization Read Me
  • ADSP-PHC Fluid Biomarker-Plasma Harmonization Read Me and White Paper
  • ADSP-PHC Neuropathology Harmonization Read Me
  • ADSP-PHC Cardiovascular Risk Factors Harmonization Read Me
  • ADSP-PHC DTI Harmonization Read Me
  • ADSP-PHC FLAIR Harmonization Read Me and White Paper
  • ADSP-PHC MRI Harmonization Read Me
  • ADSP-PHC PET Harmonization Read Me and White Paper

PIs

MPI:

  • Cuccaro, Michael
  • Hohman, Timothy J.
  • Toga, Arthur

Domain Leads:

  • Beecham, Gary
  • Brickman, Adam
  • Crane, Paul
  • Cruchaga, Carlos
  • Davatzikos, Christos
  • Habes, Mohamad
  • Landman, Bennett
  • Mayeux, Richard
  • Mez, Jesse
  • Montine, Thomas
  • Mormino, Elizabeth
  • Risacher, Shannon
  • Saykin, Andrew
  • Thompson, Paul
  • Tosun-Turgut, Duygu

Cohorts

Cohort Autopsy Cardiovascular Risk Cognition Diagnosis Fluid Bio-CSF Fluid Bio-Plasma Diffusion FLAIR T1-FS T1-MUSE PET-Amyloid PET-Tau
TOTAL 7,699 24,399 32,874 37,107 1,665 4,114 1,969 7,493 6,153 6,522 6,395 2,035
A4 0 0 3,345 3,345 0 3,114 0 1,418 1,400 804 3,340 338
ACT 502 0 1,337 1,337 0 0 0 0 0 0 0 0
Case Western Autopsy 106 0 0 106 0 0 0 0 0 0 0 0
EFIGA 0 4,296 4,563 4,730 0 0 0 0 0 0 0 0
HABS-HD 0 1,325 1,325 1,325 0 650 1,231 1,312 1,275 1,314 923 630
KBASE 0 0 603 603 0 0 0 602 508 601 602 180
Knight ADRC 0 0 870 903 558 0 190 297 433 428 458 159
Miami HIHG 85 0 0 85 0 0 0 0 0 0 0 0
MARS-Rush 13 48 48 48 0 0 17 20 19 20 0 0
Miami Brain Bank 306 0 0 306 0 0 0 0 0 0 0 0
NACC 5,211 12,216 11,955 14,319 836 350 354 1,809 1,190 1,836 587 263
NIA-AD FBS 381 0 1,650 2,819 0 0 0 0 0 0 0 0
TARCC 0 1,161 1,161 1,161 0 0 0 0 0 0 0 0
WHICAP 0 3,142 3,795 3,798 0 0 0 1,306 659 767 0 0
WRAP 0 989 1,000 1,000 271 0 121 585 579 608 485 465
ROSMAP-Rush 1,095 1,222 1,222 1,222 0 0 56 144 90 144 0 0
Sample sizes reflect harmonized data from individuals with ADSP sequencing.

Anti-Amyloid Treatment in Asymptomatic Alzheimer’s study (A4 Study)
Adult Changes in Thought (ACT)
Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Case Western Reserve University (CWRU)
Estudio Familiar de Influencia Genetica en Alzheimer (EFIGA)
Health and Aging Brain Study – Health Disparities (HABS-HD)
Korean Brain Aging Study for the Early Diagnosis and Prediction of AD (KBASE)
Memory & Aging Project at Knight Alzheimer’s Disease Research Center (MAP at Knight ADRC)
University of Miami HIHG (MIA)
Minority Aging Research Study (MARS)
University of Miami Brain Bank (MBB)
National Alzheimer’s Coordinating Center (NACC)
National Institute on Aging Alzheimer’s Disease Family Based Study (NIA-AD FBS)
Religious Orders Study/Memory and Aging Project (ROS, MAP – Rush)
Texas Alzheimer’s Research and Care Consortium (TARCC)
Washington Heights/Inwood Columbia Aging Project (WHICAP)
Wisconsin Registry for Alzheimer’s Prevention (WRAP)

Grants

PAR-20-099 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)

  • Alzheimer’s Disease Sequencing Project Phenotype Harmonization Consortium (ADSP-PHC), U24-AG074855; MPI: Timothy J. Hohman, Michael Cuccaro, Arthur Toga; Project Period: September 30, 2021 – August 31, 2026

PAR-22-110 NIA Renewal and Revision Cooperative Agreements in AD/ADRD Research (U24 Clinical Trial Not Allowed) 

  • MVP Data Integration into the ADSP Phenotype Harmonization Consortium (ADSP-PHC), U24-AG074855; PI: Timothy J. Hohman; Project Period: May 15, 2024 – August 31, 2026

Acknowledgment

The ADSP Phenotype Harmonization Consortium (ADSP-PHC) is funded by NIA (U24 AG074855, U01 AG068057 and R01 AG059716). The harmonized cohorts within the ADSP-PHC include: the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s study (A4 Study), a secondary prevention trial in preclinical Alzheimer’s disease, aiming to slow cognitive decline associated with brain amyloid accumulation in clinically normal older individuals. The A4 Study is funded by a public-private-philanthropic partnership, including funding from the National Institutes of Health-National Institute on Aging, Eli Lilly and Company, Alzheimer’s Association, Accelerating Medicines Partnership, GHR Foundation, an anonymous foundation and additional private donors, with in-kind support from Avid and Cogstate. The companion observational Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) Study is funded by the Alzheimer’s Association and GHR Foundation. The A4 and A4-LEARN Studies are led by Dr. Reisa Sperling at Brigham and Women’s Hospital, Harvard Medical School and Dr. Paul Aisen at the Alzheimer’s Therapeutic Research Institute (ATRI), University of Southern California. The A4 and LEARN Studies are coordinated by ATRI at the University of Southern California, and the data are made available through the Laboratory for Neuro Imaging at the University of Southern California. The participants screening for the A4 Study provided permission to share their de-identified data in order to advance the quest to find a successful treatment for Alzheimer’s disease. We would like to acknowledge the dedication of all the participants, the site personnel, and all of the partnership team members who continue to make the A4 and LEARN Studies possible. The complete A4 Study Team list is available on: a4study.org/a4-study-team.; the Adult Changes in Thought study (ACT), U01 AG006781, U19 AG066567; Alzheimer’s Disease Neuroimaging Initiative (ADNI): Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.;Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.;Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California; Brain tissue and additional biological samples were collected from individuals who were assessed and followed in the Case Western ADC which was funded by NIA P50AG008012; Estudio Familiar de Influencia Genetica en Alzheimer (EFIGA): 5R37AG015473, RF1AG015473, R56AG051876; the Health & Aging Brain Study – Health Disparities (HABS-HD), supported by the National Institute on Aging of the National Institutes of Health under Award Numbers R01AG054073, R01AG058533, R01AG070862, P41EB015922, and U19AG078109; brain tissues were obtained from the Brain Bank at the John P. Hussman Institute for Human Genomics at the University of Miami Miller School of Medicine, with funding provided by Louis D. Scientific Prize from the Institut de France; the Indiana Alzheimer’s Disease Research Center (IADRC) supported by National Institutes of Health (NIH) grant P30AG072976; the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s disease (KBASE), which was supported by a grant from Ministry of Science, ICT and Future Planning (Grant No: NRF-2014M3C7A1046042); Memory & Aging Project at Knight Alzheimer’s Disease Research Center (MAP at Knight ADRC): The Memory and Aging Project at the Knight-ADRC (Knight-ADRC). This work was supported by the National Institutes of Health (NIH) grants R01AG064614, R01AG044546, RF1AG053303, RF1AG058501, U01AG058922 and R01AG064877 to Carlos Cruchaga. The recruitment and clinical characterization of research participants at Washington University was supported by NIH grants P30AG066444, P01AG03991, and P01AG026276. Data collection and sharing for this project was supported by NIH grants RF1AG054080, P30AG066462, R01AG064614 and U01AG052410. We thank the contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, the Neurogenomics and Informatics Center (NGI: https://neurogenomics.wustl.edu/) and the Departments of Neurology and Psychiatry at Washington University School of Medicine; the University of Miami Brain Endowment Bank (Miami Brain Bank); National Alzheimer’s Coordinating Center (NACC): The NACC database is funded by NIA/NIH Grant U24 AG072122. SCAN is a multi-institutional project that was funded as a U24 grant (AG067418) by the National Institute on Aging in May 2020. Data collected by SCAN and shared by NACC are contributed by the NIA-funded ADRCs as follows: P30 AG062429 (PI James Brewer, MD, PhD), P30 AG066468 (PI Oscar Lopez, MD), P30 AG062421 (PI Bradley Hyman, MD, PhD), P30 AG066509 (PI Thomas Grabowski, MD), P30 AG066514 (PI Mary Sano, PhD), P30 AG066530 (PI Helena Chui, MD), P30 AG066507 (PI Marilyn Albert, PhD), P30 AG066444 (PI John Morris, MD), P30 AG066518 (PI Jeffrey Kaye, MD), P30 AG066512 (PI Thomas Wisniewski, MD), P30 AG066462 (PI Scott Small, MD), P30 AG072979 (PI David Wolk, MD), P30 AG072972 (PI Charles DeCarli, MD), P30 AG072976 (PI Andrew Saykin, PsyD), P30 AG072975 (PI David Bennett, MD), P30 AG072978 (PI Neil Kowall, MD), P30 AG072977 (PI Robert Vassar, PhD), P30 AG066519 (PI Frank LaFerla, PhD), P30 AG062677 (PI Ronald Petersen, MD, PhD), P30 AG079280 (PI Eric Reiman, MD), P30 AG062422 (PI Gil Rabinovici, MD), P30 AG066511 (PI Allan Levey, MD, PhD), P30 AG072946 (PI Linda Van Eldik, PhD), P30 AG062715 (PI Sanjay Asthana, MD, FRCP), P30 AG072973 (PI Russell Swerdlow, MD), P30 AG066506 (PI Todd Golde, MD, PhD), P30 AG066508 (PI Stephen Strittmatter, MD, PhD), P30 AG066515 (PI Victor Henderson, MD, MS), P30 AG072947 (PI Suzanne Craft, PhD), P30 AG072931 (PI Henry Paulson, MD, PhD), P30 AG066546 (PI Sudha Seshadri, MD), P20 AG068024 (PI Erik Roberson, MD, PhD), P20 AG068053 (PI Justin Miller, PhD), P20 AG068077 (PI Gary Rosenberg, MD), P20 AG068082 (PI Angela Jefferson, PhD), P30 AG072958 (PI Heather Whitson, MD), P30 AG072959 (PI James Leverenz, MD); National Institute on Aging Alzheimer’s Disease Family Based Study (NIA-AD FBS): U24 AG056270; Religious Orders Study (ROS): P30AG10161,R01AG15819, R01AG42210; Memory and Aging Project (MAP – Rush): R01AG017917, R01AG42210; Minority Aging Research Study (MARS): R01AG22018, R01AG42210; the Texas Alzheimer’s Research and Care Consortium (TARCC), funded by the Darrell K Royal Texas Alzheimer’s Initiative, directed by the Texas Council on Alzheimer’s Disease and Related Disorders; Washington Heights/Inwood Columbia Aging Project (WHICAP): RF1 AG054023;and Wisconsin Registry for Alzheimer’s Prevention (WRAP): R01AG027161 and R01AG054047. Additional acknowledgments include the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS, U24AG041689) at the University of Pennsylvania, funded by NIA.

Related Publications

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(2) Kim ME, Rudravaram G, Saunders A, et al. Analytic Bounds on GAMLSS Model Variability of Normative White Matter Brain Charts. SPIE Medical Imaging: Image Processing [In Press] 2026. PMC in process.

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(4) Durant A, Mukherjee S, Lee ML, et al. Evaluating the association of APOE genotype and cognitive resilience in SuperAgers. Alzheimer’s & Dementia 2026 Jan;22(1);e71024. PMC12809718. 

(5) Marino FR, Rogers G, Miller JW, et al. Higher vitamin B12 from mid-to-late life is related to slower rates of cognitive decline. Alzheimer’s & Dementia 2025; 21(10): e70864. PMC12568389.

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(11) Kim ME, Gao C, Newlin NR, et al. Scalable quality control on processing of large diffusion-weighted and structural magnetic resonance imaging datasets. PLoS One 2025; 20(8):e0327388. PMC12316263.

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(40) Charisis S, Rashid T, Dintica C, et al. Assessing the Global Impact of Brain Small Vessel Disease on Cognition: The Multi-Ethnic Study of Athereosclerosis. Alzheimer’s & Dementia 2025; 21(6): e70326. PMC12136095.

(41) Xu Y, Western D, Heo G, et al. Protein-based Diagnosis and Analysis of Co-pathologies Across Neurodegenerative Diseases: Large-Scale AI-Boosted CSF and Plasma Classification. Research Square [Preprint]2025. PMC12324596.

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(44) Guo MH, Lee WP, Vardarajan B, et al. Polygenic burden of short tandem repeat expansions promotes risk for Alzheimer’s disease. Nature Communications 2025; 16(1): 1126. PMC1175329.

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(47) Schilling KG, Ramadass K, Sairanen V, et al. Head Motion in Diffusion Magnetic Resonance Imaging: Quantification, Mitigation, and Structural Associations in Large, Cross-Sectional Datasets Across the Lifespan. Human Brain Mapping 2025; 46(3): e70143. PMC11814480.

(48) Olivier-Jimenez D, Derks RJ, Harari O, et al. iSODA: A Comprehensive Tool for Integrative Omics Data Analysis in Single- and Multi-Omics Experiments. Analytical Chemistry 2025; 97(5): 2689-2697. PMC11822744.

(49) Kim ME, Ramadass K, Gao C, et al. Scalable, reproducible, and cost-effective processing of large-scale medical imaging datasets. Proceedings of SPIE – The International Society for Optical Engineering 2025; 12411. PMC12734404.

(50) Li Z, Kim ME, Yu T, et al. Approximate diffusion tractography from FLAIR MRI and anatomical context using recurrent neural networks. Proceedings of SPIE – The International Society for Optical Engineering 2025; 13406. PMC12726966.

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(52) McMaster E, Puglisi L, Gao C, et al. A technical assessment of latent diffusion for Alzheimer’s Disease progression. Proceedings of SPIE – The International Society for Optical Engineering 2025; 13406. PMC12726967.

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(56) Maina MB, Isah MB, Marsh JA, et al. Somatic and Stem Cell Bank to study the contribution of African ancestry to dementia: African iPSC Initiative. Alzheimer’s & Dementia 2025; 21(4): PMC11992592.

(57) Gallée J, Volkmer A, Whitworth A, et al. Applications of the R.A.I.S.E. Assessment Framework to Support the Process of Assessment in Primary Progressive Aphasia. American Journal of Speech-Language Pathology 2024; 33(5): 2280-2290. PMC11427735.

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