Data Sharing in the PRIMED Consortium: Design, implementation, and recommendations for future policymaking
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arXiv
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| Natura: | Preprint |
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2025
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| author | Smith, Johanna L. Wong, Quenna Hornsby, Whitney Conomos, Matthew P. Heavner, Benjamin D. Kullo, Iftikhar J. Psaty, Bruce M. Rich, Stephen S. Tayo, Bamidele Natarajan, Pradeep Nelson, Sarah C. Group, Polygenic Risk Methods in Diverse Populations PRIMED Consortium Data Sharing Working Consortium, Polygenic Risk Methods in Diverse Populations PRIMED |
| author_facet | Smith, Johanna L. Wong, Quenna Hornsby, Whitney Conomos, Matthew P. Heavner, Benjamin D. Kullo, Iftikhar J. Psaty, Bruce M. Rich, Stephen S. Tayo, Bamidele Natarajan, Pradeep Nelson, Sarah C. Group, Polygenic Risk Methods in Diverse Populations PRIMED Consortium Data Sharing Working Consortium, Polygenic Risk Methods in Diverse Populations PRIMED |
| contents | Sharing diverse genomic and other biomedical datasets is critical to advance scientific discoveries and their equitable translation to improve human health. However, data sharing remains challenging in the context of legacy datasets, evolving policies, multi-institutional consortium science, and international stakeholders. The NIH-funded Polygenic Risk Methods in Diverse Populations (PRIMED) Consortium was established to improve the performance of polygenic risk estimates for a broad range of health and disease outcomes with global impacts. Improving polygenic risk score performance across genetically diverse populations requires access to large, diverse cohorts. We report on the design and implementation of data sharing policies and procedures developed in PRIMED to aggregate and analyze data from multiple, heterogeneous sources while adhering to existing data sharing policies for each integrated dataset. We describe two primary data sharing mechanisms: coordinated dbGaP applications and a Consortium Data Sharing Agreement, as well as provide alternatives when individual-level data cannot be shared within the Consortium (e.g., federated analyses). We also describe technical implementation of Consortium data sharing in the NHGRI Analysis Visualization and Informatics Lab-space (AnVIL) cloud platform, to share derived individual-level data, genomic summary results, and methods workflows with appropriate permissions. As a Consortium making secondary use of pre-existing data sources, we also discuss challenges and propose solutions for release of individual- and summary-level data products to the broader scientific community. We make recommendations for ongoing and future policymaking with the goal of informing future consortia and other research activities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09351 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data Sharing in the PRIMED Consortium: Design, implementation, and recommendations for future policymaking Smith, Johanna L. Wong, Quenna Hornsby, Whitney Conomos, Matthew P. Heavner, Benjamin D. Kullo, Iftikhar J. Psaty, Bruce M. Rich, Stephen S. Tayo, Bamidele Natarajan, Pradeep Nelson, Sarah C. Group, Polygenic Risk Methods in Diverse Populations PRIMED Consortium Data Sharing Working Consortium, Polygenic Risk Methods in Diverse Populations PRIMED Other Quantitative Biology Sharing diverse genomic and other biomedical datasets is critical to advance scientific discoveries and their equitable translation to improve human health. However, data sharing remains challenging in the context of legacy datasets, evolving policies, multi-institutional consortium science, and international stakeholders. The NIH-funded Polygenic Risk Methods in Diverse Populations (PRIMED) Consortium was established to improve the performance of polygenic risk estimates for a broad range of health and disease outcomes with global impacts. Improving polygenic risk score performance across genetically diverse populations requires access to large, diverse cohorts. We report on the design and implementation of data sharing policies and procedures developed in PRIMED to aggregate and analyze data from multiple, heterogeneous sources while adhering to existing data sharing policies for each integrated dataset. We describe two primary data sharing mechanisms: coordinated dbGaP applications and a Consortium Data Sharing Agreement, as well as provide alternatives when individual-level data cannot be shared within the Consortium (e.g., federated analyses). We also describe technical implementation of Consortium data sharing in the NHGRI Analysis Visualization and Informatics Lab-space (AnVIL) cloud platform, to share derived individual-level data, genomic summary results, and methods workflows with appropriate permissions. As a Consortium making secondary use of pre-existing data sources, we also discuss challenges and propose solutions for release of individual- and summary-level data products to the broader scientific community. We make recommendations for ongoing and future policymaking with the goal of informing future consortia and other research activities. |
| title | Data Sharing in the PRIMED Consortium: Design, implementation, and recommendations for future policymaking |
| topic | Other Quantitative Biology |
| url | https://arxiv.org/abs/2502.09351 |