Background: Genomic studies of Alzheimer’s disease (AD) have primarily focused on non-Hispanic White (NHW) participants affected by the late-onset form of the disease (LOAD; onset age: >65), or the study of early onset AD (EOAD; onset age <=65) cases from families showing Mendelian inheritance patterns associated with mutations in the APP, PSEN1 and PSEN2 genes. However, mutations in these three genes explain ~10% of EOAD cases. There are no large-scale efforts to collect and study EOAD cases not explained by these genes, even though this unexplained EOAD category accounts for ~90% of cases. The few smaller studies that have been conducted suggest that the genetic architecture of EOAD overlaps with the late-onset form only partially. Thus, studying EOAD in subjects without APP, PSEN1 and PSEN2 mutations is a critical gap that provides a unique opportunity for discovering novel therapeutic targets and molecular pathways. To address this issue we aim to identify additional EOAD-associated variants through a large-scale whole-genome sequencing (WGS) study of unexplained EOAD. We will include cases from several well-established AD cohorts including the Resource for Early-onset Alzheimer Disease Research (READR), the Knight-ADRC at Washington University, the Alzheimer’s Disease Genetics Consortium (ADGC), and others.
Inclusion/Populations: Inclusion criteria include AD cases with early onset (<65) and near-early (<70) onset, as well as cognitive controls. If APP, PSEN1, PSEN2 mutations have been typed the individual must be negative. Samples are not excluded by race/ethnicity, so the total sample set includes non-Hispanic whites, Hispanic, and (limited) African American. Samples are derived from ascertainment at Washington University, Columbia University, University of Miami, the ADCs/NCRAD, as well as collaborating institutions.
Disease Type: Primary phenotypes are AD, MCI, and cognitive controls. Phenotypes related to neurodegeneration and dementia may also be considered (neuropsychiatric phenotypes especially)
Autopsy: Autopsy may be available for a limited number of individuals but will not be a primary phenotype in this study.
Dataset Description: The overall dataset consists of whole genome sequence derived from early-onset Alzheimer disease, MCI, and cognitive controls. Most “case” samples have onset 65 and under, though some up to 70 were included. All participants (AD, MCI, and cognitively intact) have standard neurocognitive/psychiatric exams and are evaluated under standard AD criteria. While the majority of participants are of non-Hispanic white ancestry, we also include Hispanic and African American samples as available. The overall dataset will consist of approximately 4,000 EOAD plus cognitive controls; additional EOAD (~1,000) and cognitive controls will be utilized from the ADSP/ADSP-FUS datasets.
Samples originate from four primary sources:
Alzheimer Disease Research Centers: The majority of samples were derived from the ADRCs, a nation-wide, NIH-funded program working to advance AD research, diagnosis, and clinical care. These samples and data are available through a collaboration between the ADGC, the National Centralized Repository for Alzheimer Disease (NCRAD, Foroud PI), and the National Alzheimer’s Coordinating Center (NACC, Kukull PI). The majority of the samples (>80%) have detailed phenotyping using the NACC Uniform Dataset (UDS).
University of Miami: Samples from the University of Miami, Hussman Institute for Human Genomics (HIHG) were provided through two efforts: READR (Beecham, PI) and the Collaborative Alzheimer Project (Pericak-Vance, PI). Samples have detailed cognitive phenotyping and often include strong family-history of AD/EOAD.
Columbia University: Dr. Reitz contributed additional EOAD samples from Columbia University. These include samples belonging to the READR collection as well as additional cases and controls. Additional data will be included from EFIGA and the NIA-LOAD study (Mayeux).
Knight ADRC: Through the Knight ADRC we will have access to approximately 400 additional EOAD with matched cognitive controls. Many of these also present with family history of AD. Additional samples came from collaborators at Cardiff University (Williams, Simms): these include 400 samples, approximately 270 EOAD cases and 130 cognitive controls.
Gary Beecham, Ph.D.
University of Miami
Carlos Cruchaga, Ph.D.
Washington University in St. Louis
Christiane Reitz, MD, Ph.D.
Acknowledgment statement for any data distributed by NIAGADS:
Data for this study were prepared, archived, and distributed by the National Institute on Aging Alzheimer’s Disease Data Storage Site (NIAGADS) at the University of Pennsylvania (U24-AG041689), funded by the National Institute on Aging.
For investigators using EOAD- WGS data:
This work was supported by the National Institutes of Health (NIH) grant R01AG064614. The ADSP-FUS is supported by U01AG057659.
The National Institutes of Health, National Institute on Aging (NIH-NIA) supported this work through the following grants: ADGC, U01 AG032984, RC2 AG036528; samples from the National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD), which receives government support under a cooperative agreement grant (U24 AG21886) awarded by the National Institute on Aging (NIA), were used in this study. Sequencing data generation and harmonization is supported by the Genome Center for Alzheimer’s Disease, U54AG052427, and data sharing is supported by NIAGADS, U24AG041689. We thank contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible.
NIH grants supported enrollment and data collection for the individual studies including the Alzheimer’s Disease Centers (ADC, 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). The Miami ascertainment and research were supported in part through: RF1AG054080, R01AG027944, R01AG019085, R01AG028786-02, RC2AG036528. The Columbia ascertainment and research were supported in part through: R37AG015473 and U24AG056270. The University of Washington ascertainment and research were supported in part through R01AG044546, RF1AG053303, RF1AG058501, U01AG058922 and R01AG064877.