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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.

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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.