gnomAD Tracks
 
Genome Aggregation Database (gnomAD) - Variants, Coverage, and Constraint tracks   (All Variation tracks)

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gnomAD Constraint Metrics  Genome Aggregation Database (gnomAD) - Predicted Constraint Metrics (pLI and Z-scores)  Source data version: Release 2.1.1 (March 6, 2019)
gnomAD Coverage  Genome Aggregation Database (gnomAD) - Genome and Exome Sample Coverage  Source data version: Release 2.0.2
gnomAD Exomes Variants  Genome Aggregation Database (gnomAD) Exome Variants v2.1.1  Source data version: Release 2.1.1
gnomAD Genomes Variants  Genome Aggregation Database (gnomAD) Genome Variants v2.1.1  Source data version: Release 2.1.1
gnomAD pext  gnomAD Proportion Expression Across Transcript Scores (pext)  Source data version: Release 2.1.1 (March 6, 2019)
gnomAD Structural Variants  Genome Aggregation Database (gnomAD) - Structural Variants  Source data version: Release 2.1
Assembly: Human Feb. 2009 (GRCh37/hg19)

Description

The tracks that are listed here contain data from unrelated individuals sequenced as part of various population-genetic and disease-specific studies collected by the Genome Aggregation Database (gnomAD). Individuals affected by severe pediatric diseases and first-degree relatives were excluded from the studies. However, some individuals with severe disease may still have remained in the datasets, although probably at an equivalent or lower frequency than observed in the general population. Raw data from all studies have been reprocessed using a standardized pipeline and jointly variant-called process, which aims to increase consistency between projects. For more information on the processing pipeline and population annotations, see the following blog post gnomAD, gnomAD v2.1 and the 2.0.2 README.

The available data tracks are:

Display Conventions

These tracks are multi-view composite tracks that contain multiple data types (views). Each view within a track has separate display controls, as described here. Most gnomAD tracks contain multiple subtracks, corresponding to subsets of data. If a track contains many subtracks, only some subracks will be displayed by default. The user can select which subtracks are displayed via the display controls on the track details page.

Data Access

The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API, and the genome annotations are stored in files that can be downloaded from our download server, subject to the conditions set forth by the gnomAD consortium (see below). Coverage values for the genome are in bigWig files in the coverage/ subdirectory. Variant VCFs can be found in the vcf/ subdirectory.

The data can also be found directly from the gnomAD downloads page. Please refer to our mailing list archives for questions, 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.

Credits

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.

References

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