Examples
Worked examples
- Is an instance
A population geneticist studying allele-frequency differences across ancestry groups downloads 1000 Genomes Project VCF files from IGSR to use as a public reference panel for imputing untyped variants in a genotyping-array study.
- Is an instance
A bioinformatics tool developer benchmarks a new variant-calling pipeline against the 1000 Genomes Project's well-characterized, multiply-validated variant set precisely because its ground truth is independently confirmed and stable, unlike a live, still-changing clinical dataset.
Counter-examples
Looks similar, but isn't
- Not an instance
A rare, disease-causing variant found in only one family is not the kind of variant the 1000 Genomes Project set out to catalogue -- the project specifically targeted variants at or above roughly 1% population frequency, so a rare variant's absence from the 1000 Genomes dataset says nothing about its real-world frequency or pathogenicity.
Editorial commentary
The 1000 Genomes Project (1KGP) was an international research effort, running from 2008 to 2015, to build the most detailed catalogue of common human genetic variation available at the time. Researchers sequenced the genomes of 2,504 individuals from 26 populations across Africa, the Americas, East Asia, Europe, and South Asia, and discovered more than 88 million genetic variants — single nucleotide polymorphisms, short insertions/deletions, and structural variants — in the process. All sequence data was made freely available to the global research community as it was generated, one of the earliest large-scale demonstrations of fully open genomic-data release.
What the project deliberately targeted
The 1000 Genomes Project set out specifically to catalogue common variation — variants present at roughly 1% frequency or higher across the populations studied — rather than to find every rare or private variant in any one individual’s genome. That design choice is what makes the resulting dataset so useful as a background reference panel: because it captures the common variation that most people share, it lets researchers distinguish a genuinely rare, potentially significant variant in a new sample from an already-common, likely benign one.
Where the project’s data lives now
The 1000 Genomes Project itself concluded in 2015, but its data did not stop being used. The International Genome Sample Resource (IGSR) now maintains, updates, and re-releases the project’s data collections and reference resources — including realigning the original samples to newer human reference genome builds as those are released, so the dataset stays usable by modern pipelines years after the original sequencing was completed. The project’s catalogued SNPs and short indels were also submitted to dbSNP, and its structural variant calls to the companion Database of Genomic Structural Variation (DGVa).
Examples
- A population geneticist studying allele-frequency differences across ancestry groups downloads 1000 Genomes Project VCF files from IGSR to use as a public reference panel for imputing untyped variants in a genotyping-array study.
- A bioinformatics tool developer benchmarks a new variant-calling pipeline against the 1000 Genomes Project’s well-characterized, multiply-validated variant set precisely because its ground truth is independently confirmed and stable, unlike a live, still-changing clinical dataset.
Counter-example
A rare, disease-causing variant found in only one family is not the kind of variant the 1000 Genomes Project set out to catalogue — the project specifically targeted variants at or above roughly 1% population frequency, so a rare variant’s absence from the 1000 Genomes dataset says nothing about its real-world frequency or pathogenicity.
Related infrastructure
See dbSNP, where the project’s short variants were catalogued, and gnomAD, the much larger, ongoing successor-scale resource for population allele frequencies today.
Machine-readable encodings
Use in your systems
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