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Data Sharing in the PRIMED Consortium: Design, implementation, and recommendations for future policymaking.

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Summary
This summary is machine-generated.

The PRIMED Consortium developed data sharing policies for diverse genomic data to improve polygenic risk scores across global populations. This facilitates equitable health advancements by enabling secure data aggregation and analysis.

Keywords:
cloud platformsconsortiumdata access and usedata sharinggenomic summary resultspolygenic risk scores

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Area of Science:

  • Genomics and Bioinformatics
  • Population Health
  • Data Science

Background:

  • Sharing diverse biomedical datasets is crucial for scientific advancement and equitable health translation.
  • Challenges in data sharing include legacy data, evolving policies, multi-institutional collaborations, and international data governance.
  • The Polygenic Risk Methods in Diverse Populations (PRIMED) Consortium aims to enhance polygenic risk score performance globally.

Purpose of the Study:

  • To design and implement data sharing policies and procedures for the PRIMED Consortium.
  • To aggregate and analyze data from multiple, heterogeneous sources while respecting existing data sharing policies.
  • To facilitate the secondary use of pre-existing data for improving polygenic risk estimates in diverse populations.

Main Methods:

  • Developed coordinated database of Genotypes and Phenotypes (dbGaP) applications and a Consortium Data Sharing Agreement.
  • Utilized federated analyses as an alternative when individual-level data sharing is not feasible.
  • Implemented data sharing infrastructure within the NHGRI Analysis Visualization and Informatics Lab-space (AnVIL) cloud platform.

Main Results:

  • Successfully aggregated and analyzed data from multiple, heterogeneous sources.
  • Enabled sharing of derived individual-level data, genomic summary results, and methods workflows via the AnVIL platform.
  • Addressed challenges and proposed solutions for releasing individual- and summary-level data products to the research community.

Conclusions:

  • Established effective data sharing mechanisms for a large, multi-institutional genomic consortium.
  • Provided a framework and recommendations for future data sharing policies and consortia.
  • Advanced the goal of improving polygenic risk scores for diverse global populations to enhance human health.