The technical advances and declining costs for high-throughput genotyping afford investigators fresh opportunities to do increasingly complex analyses of genetic associations with phenotypic and disease characteristics. The leading candidates for such genome wide association studies (GWAS) are existing large-scale cohort and clinical studies that collected rich sets of phenotype data. To support investigator access to data from these initiatives at the National Institutes of Health (NIH) and elsewhere, the National Center for Biotechnology Information (NCBI) has created a database of Genotypes and Phenotypes (dbGaP) with stable identifiers that make it possible for published studies to discuss or cite the primary data in a specific and uniform way. dbGaP provides unprecedented access to the large-scale genetic and phenotypic datasets required for GWAS designs, including public access to study documents linked to summary data on specific phenotype variables, statistical overviews of the genetic information, position of published associations on the genome, and authorized access to individual-level data.
The purposes of this description of dbGaP are three-fold: (1) to describe dbGaP's functionality for users and submitters; (2) to describe dbGaP's design and operational processes for database methodologists to emulate or improve upon; and (3) to reassure the lay and scientific public that individual-level phenotype and genotype data are securely and responsibly managed.
dbGaP accommodates studies of varying design. It contains four basic types of data: (1) Study documentation, including study descriptions, protocol documents, and data collection instruments, such as questionnaires; (2) Phenotypic data for each variable assessed, at both an individual level and in summary form; (3) Genetic data, including study subjects' individual genotypes, pedigree information, fine mapping results, and resequencing traces; and (4) Statistical results, including association and linkage analyses, when available.
To protect the confidentiality of study subjects, dbGaP accepts only de-identified data and requires investigators to go through an authorization process in order to access individual-level phenotype and genotype datasets. Summary phenotype and genotype data, as well as study documents, are available without restriction.