InterVar

InterVar automates application of ACMG/AMP criteria to classify the clinical significance of human genetic variants for disease interpretation.


Key Features:

  • ACMG/AMP criteria automation: Automates application of 18 of the 28 ACMG/AMP evidence criteria for variant classification.
  • Input formats: Accepts pre-annotated input files or Variant Call Format (VCF) files.
  • Guideline-aligned classifications: Generates automated variant interpretations aligned with ACMG/AMP clinical guidelines.
  • Companion wInterVar web server: Provides the automated interpretation step with an option for manual adjustment of results.
  • Validated in sequencing studies: Demonstrated in published sequencing studies to reduce the time required for clinical variant interpretation.

Scientific Applications:

  • Clinical variant interpretation: Supports classification of the clinical significance of sequence variants in human genetic disease.
  • Severe congenital and early-onset developmental disorders: Applied to investigate severe congenital disorders and very early-onset developmental conditions characterized by high penetrance.
  • Standardization across studies: Promotes more consistent and efficient variant classification by standardizing the application of ACMG/AMP criteria.

Methodology:

Applies 18 of 28 ACMG/AMP criteria to pre-annotated or Variant Call Format (VCF) inputs to produce automated ACMG/AMP-aligned variant classifications; the wInterVar web server implements the automated interpretation step and permits manual adjustment of results.

Topics

Collections

Details

License:
Other
Maturity:
Mature
Cost:
Free of charge
Tool Type:
command-line tool
Operating Systems:
Windows, Mac
Programming Languages:
Python
Added:
8/6/2018
Last Updated:
8/20/2020

Operations

Publications

Li Q, Wang K. InterVar: Clinical Interpretation of Genetic Variants by the 2015 ACMG-AMP Guidelines. The American Journal of Human Genetics. 2017;100(2):267-280. doi:10.1016/j.ajhg.2017.01.004. PMID:28132688. PMCID:PMC5294755.

PMID: 28132688
PMCID: PMC5294755
Funding: - NIH: HG006465, MH108728

Documentation

Links