All of Us Research Program

Access genomic data from All of Us participants with and without diabetes.

Perform genome-wide association studies (GWAS) to identify potential genetic variants.

Validate findings in independent cohorts.

Investigate the functional role of identified variants in diabetes pathogenesis.

Link EHR data on asthma diagnoses and medications with environmental exposure data (e.g., air quality measurements).

Analyze the correlation between environmental exposures and asthma outcomes.

Identify specific environmental factors that exacerbate asthma symptoms.

Develop targeted interventions to reduce environmental exposures and improve asthma control.

Analyze EHR data on hypertension diagnoses, medications, and blood pressure measurements.

Identify patient subgroups based on demographics, genetics, and lifestyle factors.

Determine which antihypertensive medications are most effective for each subgroup.

Develop personalized treatment algorithms to optimize blood pressure control.

Integrate data from EHRs, surveys, physical measurements, and genomic data.

Train machine learning models to predict cardiovascular events (e.g., heart attack, stroke).

Validate the models in independent datasets.

Implement the models in clinical practice to identify high-risk individuals and guide preventive interventions.

Collect data from participants using digital health interventions (e.g., activity trackers, diet apps).

Compare health behaviors and outcomes between participants using the interventions and those who are not.

Identify factors that influence the effectiveness of the interventions.

Optimize the design and implementation of digital health interventions to maximize their impact.