Quantifying the potential of functional evidence to reclassify variants of uncertain significance in the categorical and Bayesian interpretation frameworks

Abstract

Additional variant interpretation tools are required to effectively harness genomic sequencing for clinical applications. The American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP) published guidelines for clinical sequence variant interpretation, incorporating different types of data that lend varying levels of support towards a benign or pathogenic interpretation. Variants of uncertain significance (VUS) are those with either contradictory or insufficient evidence, and their uncertainty complicates patient counseling and management. Functional assays may provide a solution to evidence gaps relegating variants to the VUS category, but the impact of functional evidence in this framework has not been assessed. We employ an algorithmic analysis of the ACMG/AMP combining rules to assess how the availability of strong functional evidence could theoretically improve the ability to make a benign or pathogenic assertion. We follow this with analysis of actual evidence combinations met by variants through expert curations as part of the Clinical Genome Resource (ClinGen). We also examine the impact of functional evidence in a Bayesian adaptation of the ACMG/AMP framework. This lays the groundwork for an evidence-based prioritization of assay development and variant assessment by identifying genes and variants that may benefit the most from functional data.

How I curate: applying American Society of Hematology-Clinical Genome Resource Myeloid Malignancy Variant Curation Expert Panel rules for RUNX1 variant curation for germline predisposition to myeloid malignancies

“…78 Additionally, systematic mutagenesis of PTEN has provided a wealth of functional data to inform the classification of PTEN variants, 79 in conjunction with published rules developed by the PTEN-VCEP. 80 In the future, focused functional assays targeting specific VUS 16 and deep mutational scanning of genes should contribute to variant curation to resolve VUS.…”

“…During sequence variant interpretation, laboratories systematically review the supporting criteria of a genomic variant, such as: minor allele frequencies (MAF), computational predictions, functional experiments and segregation with disease in order to determine the five-tier classification. [14][15][16] Although the ACMG/AMP guidelines provide a comprehensive framework for sequence variant interpretation, the high rate of VUS and curation discrepancies continue to be an impediment to accurate clinical annotation and interpretation of genomic variants. 6,7 To encourage genomic and phenotypic data sharing, and engage experts in consensus-driven variant interpretation, the Clinical Genome Resource (ClinGen) convened Variant Curation Expert Panels (VCEP) to develop gene-and disease-specific modifications of the original guidelines and provide expert-reviewed variant classification for depositing into ClinVar (Online Supplementary Figure S1).…”

Clinical genetic testing in endocrinology: Current concepts and contemporary challenges

“…For example, the rare exome variant ensemble learner (REVEL) tool combines 13 individual prediction algorithms into a single score, resulting in improved reliability . Similarly, statistical modelling, employing large population data sets, coupled with disease‐specific information (eg penetrance, genetic heterogeneity, mode of inheritance) enables more stringent allele frequency cut‐offs to be used in variant filtering, whilst Bayesian modelling has also been used in the application of components of the ACMG recommendations to enable a more quantitative framework for analysis …”

“…For example, the rare exome variant ensemble learner (REVEL) tool combines 13 individual prediction algorithms into a single score, resulting in improved reliability 78,79. Similarly, statistical modelling, employing large population data sets, coupled with disease-specific information (eg penetrance, genetic heterogeneity, mode of inheritance) enables more stringent allele frequency cut-offs to be used in variant filtering, whilst Bayesian modelling has also been used in the application of components of the ACMG recommendations to enable a more quantitative framework for analysis 71,[80][81][82][83]. Although the clinician does not require a detailed knowledge of the methods employed for variant classification, it is important to recognize the limitations of such analysis and in particular, that an assertion of variant pathogenicity arising from molecular classification does not equate to a clinical diagnosis for the patient 3.…”

ClinGen and ClinVar – Enabling Genomics in Precision Medicine

“…As a related effort, Brnich et al. also reflect on the ability to enhance variant interpretation through the provision of functional data (Brnich et al., ). Enlarging on data types currently in use, a paper provides guidance on the use of somatic data from cancer studies in the classification of germline cancer variants (Walsh et al., ).…”