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Structural variants (SVs) were discovered in 1,760 donors by running a combination of seven different tools to capture the main classes of variation, including deletions (DEL), duplications (DUP), insertions (INS), inversions (INV), mobile element insertions (MEI), and complex rearrangements (CPX). We mapped associations of 25,421 SVs with MAF ≥ 0.01 in the ROS/MAP cohorts to four different molecular phenotypes in the DLPFC. These molecular phenotypes were measured for a partially overlapping set of samples and included gene expression for 15,582 genes (n=456), 110,092 splicing junctions proportions measured by “percent spliced in” values (PSI) (n=505), histone acetylation (H3K9ac) peaks (n=571), and proteomic data for 7,960 proteins (n=272). We refer to these analyses as SV-xQTL, in which we map differences in measurements of each molecular phenotype associated with specific SV’s. Therefore, each SV-xQTL is an SV-phenotype pair (i.e., SV-eQTL, SV-sQTL, SV-haQTL, or SV-pQTL). All phenotype measurements were adjusted prior to associations to account for known (e.g., sex and ancestry principal components) and unknown covariates, determined either with PEER (probabilistic estimation of expression residuals) or PCA (principal component analysis), and the allele alternative to the genome of reference was considered as effect allele. This identified 3,191 SV-eQTL, 2,866 SV-sQTL, 399 SV-pQTL, and 1,454 SV-haQTL (FDR < 0.05). Refer to the Related Publications tab for more details on the dataset.

Sample Summary per Data Type

Sample SetAccessionData TypeNumber of Samples
AMP-AD WGS - SV Calls snd10042WGS1,369

Available Filesets

NameAccessionLatest ReleaseDescription
AMP-AD SV Genotyping Datafsa000041NG00118.v1SV Genotyping Data
AMP-AD SV-xQTL Association Summary Statisticsfsa000042NG00118.v1SV-xQTL Association Summary Statistics

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

The AMP-AD WGS sampleset was sequenced on the Illumina HiSeqX sequencer (v2.5 chemistry) and made available from four aging and Alzheimer's disease cohorts: Religious Orders Study (ROS) and Memory and Aging Project (MAP), Mayo Clinic, and Mount Sinai Brain Bank (MSBB). Structural variation discovery quality control identified 46,197 SVs in Mayo Clinic (349 samples), 52,451 SVs in MSBB (305 samples), and 72,348 SVs in ROS/MAP (715 samples), totaling 170,966 across 1,369 samples. Subjects in ROS/MAP not consented for individual SV calls were removed from the individual level data submission but are still part of the aggregate association summary statistics.

Sample SetAccession NumberNumber of Subjects
AMP-AD WGS - SV Calls snd100411,369
Consent LevelNumber of Subjects

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

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 NG00118.

For investigators using AMP-AD SV-xQTL (sa000028) data:

We thank the participants of AMP-AD cohorts for their essential contributions and gift to these projects. ROSMAP study data were provided by the Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago. Data collection was supported through funding by National Institute on Aging (NIA) grants P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG36836, U01AG32984, U01AG46152, U01AG61356, and the Illinois Department of Public Health. Mayo RNA-seq study data were provided by the following sources: the Mayo Clinic Alzheimer's Disease Genetic Studies, led by Dr. Nilufer Ertekin-Taner and Dr. Steven G. Younkin, Mayo Clinic, Jacksonville, Florida, using samples from the Mayo Clinic Study of Aging, the Mayo Clinic Alzheimer's Disease Research Center, and the Mayo Clinic Brain Bank. Data collection was supported through funding by NIA grants P50 AG016574, R01 AG032990, U01 AG046139, R01 AG018023, U01 AG006576, U01 AG006786, R01 AG025711, R01 AG017216, and R01 AG003949; National Institute of Neurological Disorders and Stroke (NINDS) grant R01 NS080820; the CurePSP Foundation; and support from Mayo Foundation. Study data include samples collected through the Sun Health Research Institute Brain and Body Donation Program of Sun City, Arizona. The Brain and Body Donation Program is supported by the National Institute of Neurological Disorders and Stroke (U24 NS072026, National Brain and Tissue Resource for Parkinson's Disease and Related Disorders), the NIA (P30 AG19610, Arizona Alzheimer's Disease Core Center), the Arizona Department of Health Services (contract 211002, Arizona Alzheimer's Research Center), the Arizona Biomedical Research Commission (contracts 4001, 0011, 05-901, and 1001 to the Arizona Parkinson's Disease Consortium), and the Michael J. Fox Foundation for Parkinson's Research. Mount Sinai Brain Bank (MSBB) data were generated from post-mortem brain tissue collected through the Mount Sinai VA Medical Center Brain Bank and were provided by Dr. Eric Schadt of the Mount Sinai School of Medicine through funding from NIA grant U01AG046170. The authors thank Dr. Bin Zhang and Dr. Erming Wang for assistance with data sharing, and members of the Raj and Crary labs for their feedback on the manuscript. We thank Jack Humphrey for his insightful comments and suggestions during this work. This work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. We thank the Mount Sinai Technology Development core for help and support with performing long-read sequencing. The research reported in this paper was supported by the Office of Research Infrastructure of the National Institutes of Health under award number S10OD026880.

  • Vialle RA, de Paiva Lopes K, Bennett DA, Crary JF, Raj T. Integrating whole-genome sequencing with multi-omic data reveals the impact of structural variants on gene regulation in the human brain. Nat Neurosci. 2022 Apr. doi: 10.1038/s41593-022-01031-7 PubMed link