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Predicted Effector Gene Aggregation, Standards and Unified Schema (PEGASUS): A Community Framework for Effector Gene
Aoife McMahon1, Yue Ji1, Maria Costanzo2
1European Bioinformatics Institute, European Molecular Biology Laboratory, Hinxton, UK.
Biorxiv : the Preprint Server for Biology
|June 29, 2026
Summary
Genome-wide association studies (GWAS) generate predicted effector genes (PEGs), but lack reporting standards. The new PEGASUS framework offers a unified schema for transparently reporting PEGs and their evidence, improving data reuse and biological inference.
Area of Science:
- Genetics and Genomics
- Bioinformatics
- Data Science
Background:
- Genome-wide association studies (GWAS) identify genetic variants associated with traits, but pinpointing causal genes remains challenging.
- Predicted effector genes (PEGs) are crucial for linking variants to biological mechanisms and therapeutic targets.
- Current PEG lists lack standardized reporting, hindering interoperability, reproducibility, and data reuse.
Purpose of the Study:
- To develop a standardized framework for reporting predicted effector genes (PEGs) and their supporting evidence.
- To address the lack of common standards in organizing, annotating, and reporting PEG predictions from GWAS.
- To enhance the FAIRness (Findability, Accessibility, Interoperability, Reusability) of PEG data.
Main Methods:
- Convened an international multi-stakeholder community workshop and working groups.
- Developed the Predicted Effector Gene Aggregation, Standards and Unified Schema (PEGASUS) framework.
- PEGASUS specifies a metadata standard, a structured evidence matrix, and a concise PEG list.
Main Results:
- The PEGASUS framework provides a community-developed schema for reporting PEGs and their evidence.
- It balances transparency, machine readability, and alignment with existing standards.
- PEGASUS-compliant data can be shared via the PEG Data Registry for enhanced reuse.
Conclusions:
- The PEGASUS framework establishes the first community standard for reporting predicted effector genes.
- Adoption will improve comparability, reproducibility, and reusability of PEG data across studies.
- Facilitates robust biological inference, benchmarking of prioritization methods, and integration with downstream resources.
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