Variant Annotation (VA)

Aims to define a modelling framework and machine-readable schema to represent statements of knowledge about genetic variations

A key step to analysing genomic sequencing data is variant annotation — the process of drafting knowledge statements about a genetic variation. A given annotation may assert knowledge about a variant’s molecular consequence, impact on gene function, population frequency, pathogenicity, or impact on therapeutic response to treatment. These statements add to our shared understanding of the impact of certain genetic variants on human health and disease.

However, the genomics and health community lacks a precise method for structuring variant annotations, hindering our ability to effectively share and analyse this knowledge across contexts and borders. To address this challenge, the GA4GH Genomic Knowledge Standards (GKS) Work Stream is developing the Variant Annotation (VA) specification, which will include a community-driven definition of compatible and computable models for specific types of variation knowledge.

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  • Aims to provide a machine-readable structure to support efficient and precise sharing of knowledge
  • Will offer a flexible framework to define new models for new variant annotation types

Target users

Researchers, clinicians, and clinical laboratories

Community resources

Dive deeper into this product! VA aims to provide a flexible modelling framework to represent different types of statements made about genetic variations — each built on a common core information model. The specification will contain infrastructure for developing, documenting, and sharing computable schema for representing diverse types of variation knowledge. These products will draw from and align with other GA4GH standards, including other GKS efforts such as the Variation Representation Specification (VRS) and Sequence Annotation, and promote compatibility with external frameworks and models such as the Scientific Evidence and Provenance Information Ontology (SEPIO), and the ACMG Variant Interpretation Guidelines







This meeting will focus on the VA roadmap and will attempt to get feedback from interested Driver Projects and team members.

15:00 UTC
1 Hour


Related Driver Projects and Organisations

European Joint Programme on Rare Disease (EJP RD)

Don't see your name? Get in touch:

  • Irina Armean
    EMBL's European Bioinformatics Institute (EBI)
  • Larry Babb
    Broad Institute of MIT and Harvard
  • Michael Baudis
    University of Zurich
  • Jacqui Beckmann
    Université de Lausanne
  • Steven Brenner
    University of California, Berkeley
  • Matt Brush
    Oregon Health & Science University
  • Daniel Cameron
    Walter and Eliza Hall Institute of Medical Research
  • Raymond Dalgleish
    University of Leicester
  • Ramon Felciano
    Digital Alchemy
  • Kyle Ferriter
    Broad Institute of MIT and Harvard
  • Robert Freimuth
    Mayo Clinic
  • Malachi Griffith
    The Genome Institute at Washington University, Variant Interpretation for Cancer Consortium (VICC)
  • Melissa Haendel
    University of Colorado Anschutz Medical Campus
  • Reece Hart
  • Ammar Husami
    Cincinnati Children's Hospital Medical Center
  • Andrew Jesaitis
    Ginkgo Bioworks
  • Xuelu (Jeff) Liu
    Dana-Farber Cancer Institute
  • Javier Lopez
    Genomics England
  • Tristan Nelson
    Geisinger Health System
  • Rahel Paloots
    University of Zurich
  • Heidi Rehm
    Massachusetts General Hospital, Broad Institute of MIT and Harvard
  • Alan Rubin
    Walter and Eliza Hall Institute of Medical Research
  • Dmitriy Sonkin
    NIH National Cancer Institute (NCI)
  • Jing Su
    Wellcome Sanger Institute (WSI)
  • David Tamborero
    Karolinska Institutet
  • Brian Walsh
    Knight Diagnostic Laboratories, Oregon Health & Science University
  • Andy Yates
    EMBL's European Bioinformatics Institute (EBI)
  • Zhenyu Zhang
    University of Chicago

News, events, and more

Catch up with all news and articles associated with Variant Annotation (VA).

8 Jul 2021
GA4GH standards in a global learning health system
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4 Jun 2020
Evidence and provenance for Variant Annotations
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