Introduction

Maximizing the personal, public, research, and clinical value of genomic information will require that clinicians, researchers, and testing laboratories to widely and reliably exchange genetic variation data and knowledge. The Variant Annotation Specifiction (VA-Spec) — written by a partnership of national information resource providers, major public initiatives, and diagnostic testing laboratories — is an modeling framework of processes and formal specifications to standardize the exchange of variation knowledge. It consistens of four primary components, and several supporting resources guiding their implementation.

Specifiction Components

  1. Domain Analysis Model: a conceptual framework for understanding the processes and participants involved in evidence-based knowledge generation.

  2. Core Information Model: a domain-agnostic information model for structuring knowledge statements and their supporting evidence and provenance.

  3. Profile Catalog: A repository of sharable and extensible Profiles, including Implementation Profiles tailored for specific data systems, and GA4GH Standard Profiles for broader community use.

  4. Reference Implementation: a library of software and services that demonstrate the creation, validation, and exchange of standards compliant data.

Implementation Support

  1. Profiling Methodology: guidance and tools for executing the profiling process to produce implementaton profiles and application models.

  2. Implementation Sandbox: a community testbed for defining, adapting, and sharing implementation profiles.

  3. Standards Development Process: a formally defined process through which GA4GH Standard Profiles evolve from implementation models through collaboration and community consensus.

The machine readable schema definitions and example code are available online at the va-spec repository (https://github.com/ga4gh/va-spec) (coming soon).

Readers may wish to view some examples of framework implementation and data models before reading the specification (coming soon).