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  1. Home
  3. Building An Interoperable Rare Disease Multi-omic Resource: The Gregor Data Model And Dataset.
  1. Home
  3. Building An Interoperable Rare Disease Multi-omic Resource: The Gregor Data Model And Dataset.

Related Experiment Video

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
09:37

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

Building an Interoperable Rare Disease Multi-omic Resource: The GREGoR Data Model and Dataset.

Benjamin D Heavner1, Marsha M Wheeler1, Jesse D Bengtsson2

  • 1Biostatistics, University of Washington, Seattle, WA, 98195, USA.

Biorxiv : the Preprint Server for Biology
|June 4, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

A new data model standardizes genomic and phenotypic data for rare disease research, improving data sharing and analysis across institutions. This enables better integration and reuse of multi-omic data for rare disease discovery.

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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
06:41

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

Area of Science:

  • Genomics
  • Rare Disease Research
  • Data Science

Background:

  • Rare disease research faces challenges in data interoperability due to a lack of standardized genomic data representation.
  • Integrating genomic and phenotypic data across diverse clinical sites is crucial for diagnosis and research.

Purpose of the Study:

  • To develop a common data model for the Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium.
  • To standardize the capture of participant, family, phenotype, and assay-level metadata for rare disease research.
  • To facilitate data integration and interoperability across distributed research sites.

Main Methods:

  • Developed a modular Consortium Data Model in partnership with domain experts.
  • Standardized metadata capture for participant, family, phenotype, and assay data.
  • Enabled linking of multiple omic data versions to individuals and attribution of genetic findings.

    • Main Results:

    • The GREGoR Data Model adoption enabled the generation and public release of a harmonized, analysis-ready Consortium Dataset.
    • The latest dataset includes phenotypic, family, and multi-omic data from 12,292 participants in 5,029 families.
    • The data model is being adopted by other rare disease data sharing initiatives.

    Conclusions:

    • A flexible and scalable data model can enable large-scale rare disease research.
    • The GREGoR Data Model facilitates cross-center data harmonization and interoperability.
    • This approach empowers rare disease research through collaborative data integration.