Now Published "ClinGen’s RASopathy Expert Panel Consensus Methods for Variant Interpretation"


Gelb, B.D., Cavé, H., Dillon, M.W., Gripp, K.W., Lee, J.A., Mason-Suares, H., Rauen, K.A., Williams, B., Zenker, M. and Vincent, L.M., 2018. ClinGen’s RASopathy Expert Panel consensus methods for variant interpretation. Genetics in Medicine.

PMID: 29493581

Full text available here 

Abstract

Purpose

Standardized and accurate variant assessment is essential for effective medical care. To that end, Clinical Genome (ClinGen) Resource clinical domain working groups (CDWGs) are systematically reviewing disease-associated genes for sufficient evidence to support disease causality and creating disease-specific specifications of American College of Medical Genetics and Genomics–Association for Molecular Pathology (ACMG-AMP) guidelines for consistent and accurate variant classification.

Methods

The ClinGen RASopathy CDWG established an expert panel to curate gene information and generate gene- and disease-specific specifications to ACMG-AMP variant classification framework. These specifications were tested by classifying 37 exemplar pathogenic variants plus an additional 66 variants in ClinVar distributed across nine RASopathy genes.

Results

RASopathy-related specifications were applied to 16 ACMG-AMP criteria, with 5 also having adjustable strength with availability of additional evidence. Another 5 criteria were deemed not applicable. Key adjustments to minor allele frequency thresholds, multiple de novo occurrence events and/or segregation, and strength adjustments impacted 60% of variant classifications. Unpublished case-level data from participating laboratories impacted 45% of classifications supporting the need for data sharing.

Conclusion

RAS-specific ACMG-AMP specifications optimized the utility of available clinical evidence and Ras/MAPK pathway–specific characteristics to consistently classify RASopathy-associated variants. These specifications highlight how grouping genes by shared features promotes rapid multigenic variant assessment without sacrificing specificity and accuracy.