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

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.