The Genome Aggregation Database (gnomAD) - Structural Variants track set shows structural variants calls (>=50 nucleotides) from the gnomAD v2.1
release on 10,847 unrelated genomes. It mostly (but not entirely) overlaps with the genome set used
for the gnomAD short variant release. For more information see the following blog post,
Structural variants in gnomAD.
There are three subtracks in this track set:
- All SV's: The full set of variant annotations from all 10,847 samples.
- Control Only SV's: Only samples from individuals not selected as a case in a
case/control study of common disease (5,192 samples).
- Non-neuro SV's: Only samples from individuals not selected as having a neurological
condition in a case/control study (8,342 samples).
Display Conventions and Configuration
Items in all subtracks follow the same conventions: items are shaded according to variant type,
mouseover on items indicates affected protein-coding genes, size of the variant (which may differ
from the chromosomal coordinates in cases like insertions), variant type (insertion, duplication,
etc), Allele Count, Allele Number, and Allele Frequency. When more than 2 genes are affected by a
variant, the full list can be obtained by clicking on the item and reading the details page. A short
summary of the 3 datasets is available in the below table:
|Multi-Allele CNV (MCNV)
Detailed information on the CNV color code is described
here. All tracks can be
filtered according to the size of the variant and variant type, using the track Configure
Bed files were obtained from the gnomAD Google Storage bucket:
gsutil cp gs://gnomad-public/papers/2019-sv/gnomad_v2.1_sv.*.bed*
These data were then transformed into bigBed tracks. For the full list of commands used to make this
track please see the "gnomAD Structural Variants v2.1" section of the
The raw data can be explored interactively with the Table Browser, or
the Data Integrator. For automated access, this track, like all
others, is available via our API. However, for bulk
processing, it is recommended to download the dataset. The genome annotation is stored in a bigBed
file that can be downloaded from the
The exact filenames can be found in the track configuration file. Annotations can be converted to
ASCII text by our tool
bigBedToBed which can be compiled from the source code or
downloaded as a precompiled binary for your system. Instructions for downloading source code and
binaries can be found
here. The tool can
also be used to obtain only features within a given range, for example:
bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg19/gnomAD/structuralVariants/gnomad_v2.1_sv.sites.bb -chrom=chr6 -start=0 -end=1000000 stdout
Please refer to our
mailing list archives
for questions and example queries, or our
Data Access FAQ
for more information.
More information about using and understanding the gnomAD data can be found in the
gnomAD FAQ site.
Thanks to the Genome Aggregation
Database Consortium for making these data available. The data are released under the ODC Open Database License
(ODbL) as described here.
Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill
AJ, Cummings BB et al.
Analysis of protein-coding genetic variation in 60,706 humans.
Nature. 2016 Aug 18;536(7616):285-91.
PMID: 27535533; PMC: PMC5018207
Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, Collins RL, Laricchia KM,
Ganna A, Birnbaum DP et al.
The mutational constraint spectrum quantified from variation in 141,456 humans.
Nature. 2020 May;581(7809):434-443.
PMID: 32461654; PMC: PMC7334197
Collins RL, Brand H, Karczewski KJ, Zhao X, Alföldi J, Francioli LC, Khera AV, Lowther C,
Gauthier LD, Wang H et al.
A structural variation reference for medical and population genetics.
Nature. 2020 May;581(7809):444-451.
PMID: 32461652; PMC: PMC7334194
Cummings BB, Karczewski KJ, Kosmicki JA, Seaby EG, Watts NA, Singer-Berk M, Mudge JM, Karjalainen J,
Satterstrom FK, O'Donnell-Luria AH et al.
Transcript expression-aware annotation improves rare variant interpretation.
Nature. 2020 May;581(7809):452-458.
PMID: 32461655; PMC: PMC7334198