Examples
Worked examples
- Is an instance
A health-disparities researcher uses the All of Us Researcher Workbench to compare cardiovascular risk-factor prevalence across self-reported race/ethnicity groups in a cohort deliberately recruited to be more representative of the U.S. population than most historical genomic cohorts.
- Is an instance
A pharmacogenomics researcher queries linked genomic and electronic-health-record data within the Researcher Workbench to study how a specific drug-metabolizing gene variant relates to real-world medication response, using All of Us specifically because it links genotype to longitudinal clinical outcomes in one dataset.
Counter-examples
Looks similar, but isn't
- Not an instance
A standalone genomic dataset with no linked health-record or survey data -- gnomAD, for instance -- is not comparable to All of Us despite both being large-scale population genomic resources: All of Us's defining feature is the longitudinal linkage of genomic, clinical, and lifestyle data per individual participant, not sequencing scale alone.
Editorial commentary
The All of Us Research Program is an NIH precision-medicine initiative that grew out of the 2015 Precision Medicine Initiative and began enrolling participants in May 2018. Its goal is to build one of the most diverse biomedical research resources of its kind by recruiting at least one million U.S. volunteers, with deliberate oversampling of communities historically underrepresented in genomic and biomedical research. As of mid-2026, the program reports data from more than 747,000 participants, making it — per NIH’s own description — the largest integrated genomics and health database of its kind in the world.
What makes All of Us distinct from a genomic reference database
Unlike a pure sequencing resource such as gnomAD or the 1000 Genomes Project, All of Us’s defining feature is linkage: each participant’s whole-genome sequencing data, electronic health record data, physical measurements, biospecimens, and survey responses (on lifestyle, environment, and health history) are all tied to the same individual within the dataset. That linkage is what lets researchers study genotype-to-real-world-outcome questions — not just how common a variant is, but how it actually relates to a participant’s diagnoses and health trajectory over time.
How researchers access the data
All of Us data is not distributed as downloadable raw files. Instead, qualified researchers register for and work inside the Researcher Workbench, a secure cloud-based analysis environment where the linked data stays in place and analysis code runs against it — a design chosen specifically to protect participant privacy on a dataset this identifiable (genomic plus clinical plus survey data together carries meaningfully higher re-identification risk than any single data type alone).
Examples
- A health-disparities researcher uses the All of Us Researcher Workbench to compare cardiovascular risk-factor prevalence across self-reported race/ethnicity groups in a cohort deliberately recruited to be more representative of the U.S. population than most historical genomic cohorts.
- A pharmacogenomics researcher queries linked genomic and electronic-health-record data within the Researcher Workbench to study how a specific drug-metabolizing gene variant relates to real-world medication response, using All of Us specifically because it links genotype to longitudinal clinical outcomes in one dataset.
Counter-example
A standalone genomic dataset with no linked health-record or survey data — gnomAD, for instance — is not comparable to All of Us despite both being large-scale population genomic resources: All of Us’s defining feature is the longitudinal linkage of genomic, clinical, and lifestyle data per individual participant, not sequencing scale alone.
Related infrastructure
See the companion guide, All of Us Researcher Workbench: How Tiered Access Works, for the practical registration and access-tier mechanics, and the UK Biobank Data Access guide for a comparable international linked-cohort model.
Machine-readable encodings
Use in your systems
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