GDI - Genomic Data Infrastructure

Last updated on 30-5-2023 by Lieke Vervoort
November 1, 2022
October 31, 2026

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In short

The Genomics Data Infrastructure (GDI) project enables access to genomic and related phenotypic and clinical data across Europe. This is done by establishing a federated, sustainable and secure infrastructure to access the data. It builds on the outputs of the Beyond 1 Million Genomes (B1MG) project and realises the ambition of the 1+Million Genomes (1+MG) initiative.


Project summary

This project accomplishes the 1+MG initiative’s ambition to enable secure access to genomics and corresponding clinical data across Europe by creating data infrastructure. The project involves a consortium of partners from 20 European countries and facilitates a cross-border federated network of national genome collections for biomedical research and personalised medicine solutions. The GDI project aims to unlock a data network of over one million genome sequences for research and clinical reference. This creates unprecedented opportunities for transnational and multi-stakeholder actions in personalised medicine for common, rare and infectious diseases. Authorised data users, such as clinicians, researchers and innovators, will be able to advance understanding of genomics for more precise and faster clinical decision-making, diagnostics, treatments and predictive medicine, and for improved public health measures to benefit European citizens, healthcare systems and the overall economy. 
As a critical component of Europe’s ambition to lead the integration of genomics into healthcare, the GDI project makes data accessible for research, clinical reference and policy development uses through three key ‘pillars’:

  1. Long term sustainability (governance model, legal framework, financial plan for the infrastructure)
  2. Infrastructure deployment (Interoperability of European data resources)
  3. Use cases (e.g. cancer and infectious disease data)

Sciensano, through the Belgian Cancer Centre, co-leads pillar 3 (use case on cancer) and contributes to the development of data-driven models for cancer research.

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