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Description

Single-neuron amplified genome libraries were sequenced (WGS) on Illumina instruments and aligned to the human reference genome GRCh37. Linked-read analysis (LiRA) and SCAN-SNV were used to identify single-cell somatic mutations. The single-cell dataset includes a total of 319 single-neuron genome sequences (172 PFC-MDA, 78 HC-MDA, 69 PFC-PTA). Sequence files are in .bam format.

Sample Summary per Data Type

Sample SetAccessionData TypeNumber of Samples
Alzheimer’s disease single-neuron whole-genome sequencing – Miller 2022snd10029Single-Neuron WGS29

Available Filesets

NameAccessionLatest ReleaseDescription
AD WGS – Miller: Single-neuron WGS BAM filesfsa000024NG00121.v1Single-neuron WGS BAM files
AD WGS – Miller: Phenotype filesfsa000025NG00121.v1Phenotype files

View the File Manifest for a full list of files released in this dataset.

This study performed whole-genome sequencing of single neurons isolated from prefrontal cortex (PFC) and hippocampus CA1 (HC) of postmortem human brain of individuals with Alzheimer’s disease (AD)(Braak stage V-VI) or non-disease control, along with bulk whole-genome sequencing performed on each individual as a reference genome for variant calling. Single neuron genomes were amplified using multiple-displacement amplification (MDA) or primary template-directed amplification (PTA).

Sample SetAccessionNumber of Subjects
Alzheimer’s disease single-neuron whole-genome sequencing – Miller 2022snd10029n = 29
Consent LevelNumber of Subjects
HMB-IRB-PUBn = 22
DS-NEURO-IRB-PUBn = 7

Visit the Data Use Limitations page for definitions of the consent levels above.

Total number of approved DARs: 2
  • Investigator:
    Pendergrass, Rion
    Institution:
    Genentech
    Project Title:
    Genetic Analyses Using Data from the Alzheimer’s Disease Sequencing Project (ADSP) and related studies
    Date of Approval:
    August 1, 2022
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    The purpose of our study is to identify novel genetic factors associated with Alzheimer’s Disease, corticobasal degeneration (CBD) and progressive supranuclear palsy (PSP). This includes identifying genetic factors associated with the risk of these conditions, as well as genetic risk factors associated with age-at-onset (AAO) for these conditions. We will also evaluate genetic associations with sub-phenotypes individuals have within these broad disease categories, such as their Braak staging results which provide insights into the level of severity of Alzheimer’s. Thus we are requesting access to the set of genomic Whole Exome and Whole Genome Sequences (WES and WGS) have just been released through the National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (DSS NIAGADS). The findings from our genetic association testing have the potential for identification of new therapeutic targets for Alzheimer's Disease, CBD, and PSP. The findings from our studies also have the potential for identification of genetic and phenotypic biomarkers that will be beneficial for subsetting patients in new ways. We will use standard genetic epidemiological methods to handle the WGS and WES data. We will also analyze cell type-specific expression differences in AD to identify biomarkers and disease pathways using standard gene expression analysis methods currently in use. We will also use other multi-omic and other genetic data that has now become available to further understand genetic association results we have found in AD.All data will remain anonymized and securely stored, and only those listed on our application and their staff will have access to these data. We will not share any of the individual level data outside of Genentech nor beyond the researchers on our application. We will adhere to all data use agreement stipulations through the DSS NIAGADS. We have a secure computational environment called Rosalind within Genentech where we will use these data. We have IT security staff that constantly monitor all our research computing, assuring safety and privacy of all of our stored data. We will not collaborate with researchers at other institutions.
    Non-Technical Research Use Statement:
    Genetic variation and gene expression data allows us to understand more of the genetic contribution to risk and protection from diseases such as Alzheimer’s and dementia. This information also allows us to identify important biological contributors to disease for developing effective treatment strategies, and identifying groups of individuals that would benefit most from new treatments. Our exploration of this relationship between genotype, disease traits, gene expression, and outcomes, through these datasets will allow us to pursue important new findings for disease treatment.
  • Investigator:
    Zhao, Jinying
    Institution:
    University of Florida
    Project Title:
    Identifying novel biomarkers for human complex diseases using an integrated multi-omics approach
    Date of Approval:
    October 18, 2022
    Request status:
    Approved
    Research use statements:
    Show statements
    Technical Research Use Statement:
    GWAS, WES and WGS have identified many genes associated with Alzheimer’s Dementia (AD) and its related traits. However, the identified genes thus far collectively explain only a small proportion of disease heritability, suggesting that more genes remained to be identified. Moreover, there is a clear gender and ethnic disparity for AD susceptibility, but little research has been done to identify gender- and ethnic-specific variants associated with AD. Of the many challenges for deciphering AD pathology, lacking of efficient and power statistical methods for genetic association mapping and causal inference represents a major bottleneck. To tackle this challenge, we have developed a set of novel statistical and bioinformatics approaches for genetic association mapping and multi-omics causation inference in large-scale ethnicity-specific epidemiological studies. The goal of this project is to leverage the multi-omics and clinical data archived by the ADSP, ADNI, ADGC as well as other AD-related data repositories to identify novel genes and molecular markers for AD. Specifically, we will (1) validate our novel methods for identifying novel risk and protective genomic variants and multi-omics causal pathways of AD; (2) identify novel ethnicity- and gender-specific genes and molecular causal pathways of AD. We will share our results, statistical methods and computational software with the scientific community.
    Non-Technical Research Use Statement:
    Although many genes have been associated with Alzheimer’s Dementia (AD), these genes altogether explain only a small fraction of disease etiology, suggesting more genes remained to be identified. Of the many challenges for deciphering AD pathology, lacking of power statistical methods represents a major bottleneck. To tackle this challenge, we have developed a set of novel statistical and bioinformatics approaches for genetic association mapping and multi-omics causation inference in large-scale ethnicity-specific epidemiological studies. The goal of this project is to leverage the rich genetic and other omic data along with clinical data archived by the ADSP, ADNI, ADGC as well as other AD-related data repositories to identify novel genes and molecular markers for AD. Such results will enhance our understanding of AD pathogenesis and may also serve as biomarkers for early diagnosis and therapeutic targets.

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.

Use the study-specific acknowledgement statements below (as applicable):

For investigators using any data from this dataset:

Please cite/reference the use of NIAGADS data by including the accession NG00121.

For investigators using AD Single-Neuron WGS - Miller (sa000022) data:

NIH Sponsoring Institute: NIA

Grant funding: NIH K08 AG065502

Doris Duke Charitable Foundation Clinical Scientist Development Award 2021183