Recent research has uncovered an abundance of structural variants within the human genome. These variations are characterized as large gains and losses (e.g. insertions, deletions, inversions, copy number variations, etc) of DNA sequence. The Database of Genomic Variants (DGV) aims to provide a comprehensive summary of these structural variations in the human genome. The database currently defines structural variation as genomic alterations that involve segments of DNA that are larger than 1kb. Insertions and deletions in the 100bp-1kb range are also annotated. The content of this database represents structural variation identified in healthy control samples. The DGV was developed within the laboratory of Dr. Stephen Scherer and hosted through The Centre for Applied Genomics (TCAG), both based at The Hospital for Sick Children, Toronto, Ontario. An Advisory Board for the database was formed in 2008 and is made up of international researchers, including from the UK, USA and Canada. This database is supported by funding from Genome Canada through the Ontario Genomics Institute, the McLaughlin Centre, the Wellcome Trust and the Canadian Institutes for Health Research.

The DGV is a curated database that is continuously updated with data from the Scherer laboratory and from other peer reviewed research articles that is openly accessible at: http://projects.tcag.ca/variation/. When citing the Database of Genomic Variants, please refer to: Iafrate et al, Detection of large-scale variation in the human genome. Nat Genet. 2004 Sep;36(9):949-51. Structural variation data from submitted or accepted manuscripts can be submitted to the DGV for inclusion in the database. The data submission process is outlined at: http://projects.tcag.ca/variation/submissions.php

Following the discovery that structural variations, including insertions, deletions, inversions and CNVs, are more common in the human genome than originally thought, screening for variation has become an important aspect of genomic characterization of patient and control cohorts. Due to their abundance and size, they are thought to play an important role in human health and disease. The aim of the DGV is to provide a catalog of data to help correlate genomic variation with phenotypic data. The database provides the opportunity to correlate your data with several (if not all) variants that have been identified in control samples to date.