Automating the ACMG Guidelines with VSClinical

Abstract

Clinical Genetic testing requires a complex analysis using the totality of our knowledge about the clinical relevance of a variant and a gene. This includes bioinformatic evidence as well as clinical evidence. The ACMG Guidelines provided a framework in which to score variants based on this evidence, and while some of those scoring criteria require close consultation of the clinical context for a given patient, much of it can be automated.

In this webcast, we review how VSClinical automates the ACMG scoring guidelines while integrating the collective lab expertise from previously classified variants and preferences about genes. We will cover:

  • Using the ACMG Auto Classifier as part the filtering strategy for gene panels and trio workflows
  • How gene preferences such as the default transcript, inheritance model, and disorder are updated and saved from VSClinical and used in all future analysis
  • How the per-variant recommendation engine builds on the auto-classification with descriptive reasons for answering each criterion yes or no
  • Using the auto-interpretation to present the evidence for all scored criteria in a human-readable paragraph
  • Working with VSClinical’s self-learning knowledgebase that incorporates previously classified variants and genes inform the interpretation of new variants!

About the Presenter

Gabe Rudy

Gabe Rudy is Golden Helix's Vice President of Product and Engineering and team member since 2002. Gabe thrives in the dynamic and fast-changing field of bioinformatics and genetic analysis. Leading a killer team of Computer Scientists and Statisticians in building powerful products and providing world-class support, Gabe puts his passion into enabling Golden Helix's customers to accelerate their research. When not reading or blogging, Gabe enjoys the outdoor Montana lifestyle. But most importantly, Gabe truly loves spending time with his sons, daughter, and wife.