Variant interpretation using population databases: lessons from gnomAD

The American College of Medical Genetics and Genomics, and the Association for Molecular Pathology (ACMG/AMP) have proposed a set of evidence-based guidelines to support sequence variant interpretation. The ClinGen hearing loss expert panel (HL-EP) introduced further specifications into the ACMG/AMP framework for genetic hearing loss. This study developed a tool named VIP-HL, aiming to semi-automate the HL ACMG/AMP rules. VIP-HL aggregates information from external databases to automate 13 out of 24 ACMG/AMP rules specified by HL-EP, namely PVS1, PS1, PM1, PM2, PM4, PM5, PP3, BA1, BS1, BS2, BP3, BP4, and BP7. We benchmarked VIP-HL using 50 variants where 83 rules were activated by the ClinGen HL-EP. VIP-HL concordantly activated 96% (80/83) rules, significantly higher than that of by InterVar (47%; 39/83). Of 4948 ClinVar star 2+ variants from 142 deafness-related genes, VIP-HL achieved an overall variant interpretation concordance in 88.0% (4353/4948). VIP-HL is an integrated online tool for reliable automated variant classification in hearing loss genes. It assists curators in variant interpretation and provides a platform for users to share classifications with each other. VIP-HL is available with a user-friendly web interface at http://hearing.genetics.bgi.com/.

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Variant Selection and Interpretation: An Example of Modified VarSome Classifier of ACMG Guidelines in the Diagnostic Setting

Genes ◽

10.3390/genes12121885 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1885

Author(s):

Francesca Cristofoli ◽

Elisa Sorrentino ◽

Giulia Guerri ◽

Roberta Miotto ◽

Roberta Romanelli ◽

...

Keyword(s):

Medical Genetics ◽

Patient Specific ◽

Specific Information ◽

Variant Selection ◽

Variant Classification ◽

Variant Interpretation ◽

Bioinformatics Pipeline ◽

Different Levels ◽

Diagnostic Setting

Variant interpretation is challenging as it involves combining different levels of evidence in order to evaluate the role of a specific variant in the context of a patient’s disease. Many in-depth refinements followed the original 2015 American College of Medical Genetics (ACMG) guidelines to overcome subjective interpretation of criteria and classification inconsistencies. Here, we developed an ACMG-based classifier that retrieves information for variant interpretation from the VarSome Stable-API environment and allows molecular geneticists involved in clinical reporting to introduce the necessary changes to criterion strength and to add or exclude criteria assigned automatically, ultimately leading to the final variant classification. We also developed a modified ACMG checklist to assist molecular geneticists in adjusting criterion strength and in adding literature-retrieved or patient-specific information, when available. The proposed classifier is an example of integration of automation and human expertise in variant curation, while maintaining the laboratory analytical workflow and the established bioinformatics pipeline.

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