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Many regions of the human genome present challenges that prohibit scientists from discovering potential disease-causing mutations. We developed methods to characterize mutations in these regions to rescue mutations that are otherwise overlooked. PMID: 31104630

Provided here are variant calls in VCF format for 14,526 samples derived from the ADSP whole-exome and whole-genome sequencing dataset (available via DSS: NG00067). For phenotypic information for the participants in this dataset, please also apply for access to the ADSP. A crosswalk file that maps the IDs between this dataset and the ADSP IDs is provided within this dataset.

Sample Summary per Data Type

Sample SetAccessionData TypeNumber of Samples
Camouflaged Variants snd10074Camouflaged Variants14,526

Available Filesets

NameAccessionLatest ReleaseDescription
Camouflaged Variants in VCF Formatfsa000090NG00116.v1Camouflaged Variants in VCF Format, README

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

This sample set includes a .vcf file containing camouflaged variants from the 14,526 ADSP samples that were described in Ebbert et al. 2019. PMID: 31104630 This dataset was originally derived from the primary ADSP data available in NG00067.

Sample SetAccession NumberNumber of Subjects
Camouflaged Variants snd1007414,314
Consent LevelNumber of Subjects

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

Total number of approved DARs: 1
  • Investigator:
    Cruchaga, Carlos
    Washington University School of Medicine
    Project Title:
    The Familial Alzheimer Sequencing (FASe) Project
    Date of Approval:
    May 9, 2024
    Request status:
    Research use statements:
    Show statements
    Technical Research Use Statement:
    The goal of this study is to identify new genes and mutations that cause or increase risk for Alzheimer disease (AD), as well as protective factors. Individuals and families were selected from the Knight-ADRC (Washington University) and the NIA-LOAD study. Only families with at least three first-degree affected individuals were included. Families with pathogenic variants in the known AD or FTD genes, or in which APOE4 segregated with disease were excluded. At least two cases and one control were selected per family. Cases had an age at onset (AAO) after 65 yo and controls had a larger age at last assessment than the latest AAO within the family. Whole exome (WES) and whole genome sequencing (WGS) was generated for 1,235 individuals (285 families) that together with data from our collaborators and the ADSP family-based cohort (3,449 individuals and 757 families) will provide enough statistical power to identify new genes for AD. Dr. Tanzi (Harvard Medical School) will provide WGS from 400 families from the NIMH Alzheimer disease genetics initiative study. We will perform single variant and gene-based analyses to identify genes and variants that increase risk for disease in AD families. Single variant analysis will consist of a combination of association and segregation analyses. We will run family-based gene-based methods to identify genes that show and overall enrichment of variants in AD cases. We will also look for protective and modifier variants. To do this we will identify families loaded with AD cases, that also include individuals with a high burden of known risk variants but that do not develop the disease (escapees). We will use the sequence data and the family structure to identify variants that segregate with the escapee phenotype. The most promising variants and genes will be replicated in independent datasets (ADSP case-control, ADNI, Knight-ADRC, NIA-LOAD ). We will perform single variant and gene-based analyses to replicate the initial findings, and survival analysis to replicate the protective variants. We will select the most promising variants/genes for functional studies
    Non-Technical Research Use Statement:
    Family-based approaches led to the identification of disease-causing Alzheimer’s Disease (AD) variants in the genes encoding APP, PSEN1 and PSEN2. The identification of these genes led to the A?-cascade hypothesis and to the development of drugs that target this pathway. Recently, we have identified rare coding variants in TREM2, ABCA7, PLD3 and SORL1 with large effect sizes for risk for AD, confirming that rare coding variants play a role in the etiology of AD. In this proposal, we will identify rare risk and protective alleles using sequence data from families densely affected by AD. We hypothesize that these families are enriched for genetic risk factors. We already have sequence data from 695 families (2,462 individuals), that combined with the ADSP and the NIMH dataset will lead to a dataset of more than 1,042 families (4,684 individuals). Our preliminary results support the flexibility of this approach and strongly suggest that protective and risk variants with large effect size will be found, which will lead to a better understanding of the biology of the disease.

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 NG00116.

For investigators using Resolving mutations in challenging genomic regions to test association with disease phenotypes (sa000042) data:

This work was funded by the following grants awarded to Dr. Mark Ebbert from: (1) the NIH (NIA: R01AG068331 and NIGMS: R35GM138636), the Alzheimer’s Association (2019-AARG-644082), and the BrightFocus Foundation (A2020161S). Additional support from the NIH/NCATS awarded to Dr. P. Kern (UL1 TR001998).

Ebbert, M.T.W., Jensen, T.D., Jansen-West, K. et al. Systematic analysis of dark and camouflaged genes reveals disease-relevant genes hiding in plain sight. Genome Biol. 2019 May. doi: 10.1186/s13059-019-1707-2. PubMed link