Abstract
We promote a shared vision and guide for how and when to federate genomic and health-related data sharing, enabling connections and insights across independent, secure databases. The GA4GH encourages a federated approach wherein data providers have the mandate and resources to share, but where data cannot move for legal or technical reasons. We recommend a federated approach to connect national genomics initiatives into a global network and precision medicine resource.
Highlights
National-scale genomic sequencing initiatives are emerging worldwide to promote personalized healthcare and innovation
Federated approaches to data sharing are flexible, involving design choices about data provider independence and secure access mechanisms
Federated approaches come with costs and limitations, but they provide opportunities to improve privacy protection, accessibility, and interoperability
Summary
National-scale genomic sequencing initiatives are emerging worldwide to promote personalized healthcare and innovation. The European Union General Data Protection Regulation 2016/679 (GDPR) has set a global standard for robust protection of personal data, which includes mandating limitations on international transfers of personal data outside the EU/EEA It has triggered a strong shift toward federated approaches for large scientific data infrastructure, in projects like the European Genome-Phenome Archive, European Open Science Cloud, the European 1+ Million Genomes Initiative, and the European Health Data Space. Research ethics oversight may be a greater challenge for federated approaches than alternatives, Commentary as data are analyzed across many different institutions and countries To address this challenge, the GA4GH Ethics Review Equivalency Policy promotes international standards for ethics review, alongside cross-border coordination and recognition mechanisms.[1] Incentives A lack of incentives to provide data is a well-known barrier to data sharing. The GA4GH Federated Analysis Systems Project (FASP) brings all these pieces together into endto-end test scenarios, aiming to simulate how a researcher would search, access, and analyze genomic data across a network of real-world projects.[1]
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