Related Experiment Video
Updated: Jul 4, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
Published on: December 9, 2015
Availability of benign missense variant "truthsets" for validation of functional assays: Current status and a
Charlie F Rowlands1, Sophie Allen1, Alice Garrett2
1Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK.
Abstract:
Multiplex assays of variant effect (MAVEs) provide promising new sources of functional evidence, potentially empowering improved classification of germline genomic variants, particularly rare missense variants, which are commonly assigned as variants of uncertain significance (VUSs). However, paradoxically, quantification of clinically applicable evidence strengths for MAVEs requires construction of "truthsets" comprising missense variants already robustly classified as pathogenic and benign. In this study, we demonstrate how benign truthset size is the primary driver of applicable functional evidence toward pathogenicity (PS3). We demonstrate, when using existing ClinVar classifications as a source of benign missense truthset variants, that only 19.8% (23/116) of established cancer susceptibility genes had a PS3 evidence strength of "strong" attainable when simulating validation for a hypothetical new MAVE (also applying favorable assumption of perfect concordance). We describe a systematic framework for benign truthset construction in which all possible missense variants in a gene of interest are concurrently assessed for assignation of (likely) benignity via established ACMG/AMP combination rules, including population frequency, in silico evidence codes, and case-control signal. We apply this framework to eight hereditary breast and ovarian cancer genes, demonstrating that systematically generated benign missense truthsets allow maximum application of PS3 at greater (or equivalent) strength-reaching "moderate" for CHEK2 and "strong" for the other seven genes-than those derived from ClinVar ≥2∗ classifications alone. We propose, given many genes have few existing benign-classified missense variants, that the application of this systematic framework to disease genes more broadly will be important for leveraging full value from MAVEs.
More Related Videos
11:35Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
Published on: August 21, 2016
06:41In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
Published on: August 20, 2019
Related Concept Videos
Reliability and Validity
Data Validation
Nursing assessment guides are generally based on holistic models rather than medical...
Types of Errors: Detection and Minimization
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Significance Testing: Overview
Detection of Gross Error: The Q Test
Data Validation
Key parameters for method validation include: