The database of Genotypes and Phenotypes, dbGaP,
is one of NIH's central data sharing tools for human genomic data. It offers
a searchable archive of studies that measure the association between genotype and phenotype.
DbGaP data sources are diverse, coming from genome-wide association studies;
whole genome and targeted sequencing, epigenetics;
and studies include statistical measures of the associations between phenotypes and genotypes.
You have two levels of access into dbGaP.
Public access provides study protocols and summaries of the associations. This
information can help inform your current research, and also help you decide if you want to apply for,
the Controlled access level, so that you can view and analyze individual-level data.
Let’s look at dbGaP’s Advanced Search web interface to better
understand how the data are organized.
Of course, the primary purpose of this page is to
help you find studies of interest, based on the filters on the left.
The Study is the top-level container for data. We see that dbGaP currently has over 2200 studies,
which, by the way, represents nearly 3.5 million subjects and trillions of genotypes.
Also note that each study has an accession number beginning with phs.
We’ll see that the other study components are also accessioned.
The next container is the phenotype data, with a pht accession.
These data are represented in a simple, rectangular table where rows are individuals,
and columns are phenotypic variables; note the phv accession number.
A study also includes molecular datasets.
Much of the data are from genotyping arrays, plus genomic sequence, and expression data.
In the large genotyping tables, individuals and variants are the axes,
and the cells are the called or imputed genotypes,
often with associated data like quality scores and allele intensity.
Note the phg accession for these genomics data.
Other genomics sets include data from various epigenetic assays, microRNA-seq, and many more.
The last two data containers are the association analyses and documents. Nearly all studies provide
public access to the documents, and many allow public access to summary association data.
You can access this summary association data in a few ways, including,
the dbGaP Genomic Analysis Navigator. Here, you can focus on chromosomal regions of interest,
link to SNP records for a given hit,
and view the selected range in our genome browser, the Genome Data Viewer,
where you can view the study's association results, in the context of other data tracks.
As mentioned earlier, the Advanced Search page is a great way to find studies of interest.
Once you’ve located a study, you have links to other resources, often including an ancestry
profile generated by our GrafPop tool, which infers subject ancestry using genotypes.
Finally, we welcome submission of your human genome data from a large-scale study.
To get started, see our recent NCBI Insights post. And for any questions or feedback about dbGaP,
you can write to dbgap-help@ncbi.nlm.nih.gov, or use the Feedback button found on most dbGaP pages.