Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines

Abstract Background The American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) clinical variant interpretation guidelines established criteria for different types of evidence. This includes the strong evidence codes PS3 and BS3 for “well-established” functional assays demonstrating a variant has abnormal or normal gene/protein function, respectively. However, they did not provide detailed guidance on how functional evidence should be evaluated, and differences in the application of the PS3/BS3 codes are a contributor to variant interpretation discordance between laboratories. This recommendation seeks to provide a more structured approach to the assessment of functional assays for variant interpretation and guidance on the use of various levels of strength based on assay validation. Methods The Clinical Genome Resource (ClinGen) Sequence Variant Interpretation (SVI) Working Group used curated functional evidence from ClinGen Variant Curation Expert Panel-developed rule specifications and expert opinions to refine the PS3/BS3 criteria over multiple in-person and virtual meetings. We estimated the odds of pathogenicity for assays using various numbers of variant controls to determine the minimum controls required to reach moderate level evidence. Feedback from the ClinGen Steering Committee and outside experts were incorporated into the recommendations at multiple stages of development. Results The SVI Working Group developed recommendations for evaluators regarding the assessment of the clinical validity of functional data and a four-step provisional framework to determine the appropriate strength of evidence that can be applied in clinical variant interpretation. These steps are as follows: (1) define the disease mechanism, (2) evaluate the applicability of general classes of assays used in the field, (3) evaluate the validity of specific instances of assays, and (4) apply evidence to individual variant interpretation. We found that a minimum of 11 total pathogenic and benign variant controls are required to reach moderate-level evidence in the absence of rigorous statistical analysis. Conclusions The recommendations and approach to functional evidence evaluation described here should help clarify the clinical variant interpretation process for functional assays. Further, we hope that these recommendations will help develop productive partnerships with basic scientists who have developed functional assays that are useful for interrogating the function of a variety of genes.

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Pathogenic variant burden in the ExAC database: an empirical approach to evaluating population data for clinical variant interpretation

Genome Medicine ◽

10.1186/s13073-017-0403-7 ◽

2017 ◽

Vol 9 (1) ◽

Cited By ~ 98

Author(s):

Yuya Kobayashi ◽

Shan Yang ◽

Keith Nykamp ◽

John Garcia ◽

Stephen E. Lincoln ◽

...

Keyword(s):

Population Data ◽

Pathogenic Variant ◽

Empirical Approach ◽

Variant Interpretation ◽

Clinical Variant

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Prioritizing genes for systematic variant effect mapping

Bioinformatics ◽

10.1093/bioinformatics/btaa1008 ◽

2020 ◽

Author(s):

Da Kuang ◽

Rebecca Truty ◽

Jochen Weile ◽

Britt Johnson ◽

Keith Nykamp ◽

...

Keyword(s):

Protein Function ◽

Supplementary Information ◽

Functional Testing ◽

Variant Interpretation ◽

Missense Variants ◽

Clinical Variant ◽

Uncertain Significance ◽

Missense Variation ◽

Variant Effect ◽

Extra Weight

Abstract Motivation When rare missense variants are clinically interpreted as to their pathogenicity, most are classified as variants of uncertain significance (VUS). Although functional assays can provide strong evidence for variant classification, such results are generally unavailable. Multiplexed assays of variant effect can generate experimental ‘variant effect maps’ that score nearly all possible missense variants in selected protein targets for their impact on protein function. However, these efforts have not always prioritized proteins for which variant effect maps would have the greatest impact on clinical variant interpretation. Results Here, we mined databases of clinically interpreted variants and applied three strategies, each building on the previous, to prioritize genes for systematic functional testing of missense variation. The strategies ranked genes (i) by the number of unique missense VUS that had been reported to ClinVar; (ii) by movability- and reappearance-weighted impact scores, to give extra weight to reappearing, movable VUS and (iii) by difficulty-adjusted impact scores, to account for the more resource-intensive nature of generating variant effect maps for longer genes. Our results could be used to guide systematic functional testing of missense variation toward greater impact on clinical variant interpretation. Availability and implementation Source code available at: https://github.com/rothlab/mave-gene-prioritization Supplementary information Supplementary data are available at Bioinformatics online.

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Evaluating the impact of in silico predictors on clinical variant classification

Genetics in Medicine ◽

10.1016/j.gim.2021.11.018 ◽

2021 ◽

Author(s):

Emma H. Wilcox ◽

Mahdi Sarmady ◽

Bryan Wulf ◽

Matt W. Wright ◽

Heidi L. Rehm ◽

...

Keyword(s):

In Silico ◽

Variant Classification ◽

Clinical Variant ◽

The Impact

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A calibrated functional patch clamp assay to enhance clinical variant interpretation in KCNH2-related long QT syndrome

10.1101/2021.12.13.472492 ◽

2021 ◽

Author(s):

Connie Jiang ◽

Ebony Richardson ◽

Jessica Farr ◽

Adam P Hill ◽

Rizwan Ullah ◽

...

Keyword(s):

Patch Clamp ◽

Current Density ◽

Hek293 Cells ◽

Functional Assay ◽

Sequence Variant ◽

Supporting Evidence ◽

Variant Classification ◽

Variant Interpretation ◽

Clinical Variant ◽

Automated Patch Clamp

Purpose: Modern sequencing technologies have revolutionised our detection of gene variants. In most genes, including KCNH2, the majority of missense variants are currently classified as variants of uncertain significance (VUS). The aim of this study is to investigate the utility of an automated patch-clamp assay for aiding clinical variant classification in the KCNH2 gene. Methods: The assay was designed according to recommendations of the ClinGen sequence variant interpretation framework. Thirty-one control variants of known clinical significance (17 pathogenic/likely pathogenic, 14 benign/likely benign) were heterozygously expressed in Flp-In HEK293 cells. Variants were analysed for effects on current density and channel gating. A panel of 44 VUS was then assessed for reclassification. Results: All 17 pathogenic variant controls had reduced current density and 13/14 benign variant controls had normal current density, which enabled determination of normal and abnormal ranges for applying moderate or supporting evidence strength for variant classification. Inclusion of KCNH2 functional assay evidence enabled us to reclassify 6 out of 44 VUS as likely pathogenic. Conclusion: The high-throughput patch clamp assay can provide moderate strength evidence for clinical interpretation of clinical KCNH2 variants and demonstrates the value proposition for developing automated patch clamp assays for other ion channel genes.

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In-silico phenotype prediction by normal mode variant analysis in TUBB4A-related disease

Scientific Reports ◽

10.1038/s41598-021-04337-x ◽

2022 ◽

Vol 12 (1) ◽

Author(s):

Avi Fellner ◽

Yael Goldberg ◽

Dorit Lev ◽

Lina Basel-Salmon ◽

Oded Shor ◽

...

Keyword(s):

Normal Mode ◽

In Silico ◽

Coarse Grained ◽

Mode Analysis ◽

Variant Interpretation ◽

Wild Type ◽

Phenotype Prediction ◽

Missense Variants ◽

Related Disease ◽

Pathogenic Variants

AbstractTUBB4A-associated disorder is a rare condition affecting the central nervous system. It displays a wide phenotypic spectrum, ranging from isolated late-onset torsion dystonia to a severe early-onset disease with developmental delay, neurological deficits, and atrophy of the basal ganglia and cerebellum, therefore complicating variant interpretation and phenotype prediction in patients carrying TUBB4A variants. We applied entropy-based normal mode analysis (NMA) to investigate genotype–phenotype correlations in TUBB4A-releated disease and to develop an in-silico approach to assist in variant interpretation and phenotype prediction in this disorder. Variants included in our analysis were those reported prior to the conclusion of data collection for this study in October 2019. All TUBB4A pathogenic missense variants reported in ClinVar and Pubmed, for which associated clinical information was available, and all benign/likely benign TUBB4A missense variants reported in ClinVar, were included in the analysis. Pathogenic variants were divided into five phenotypic subgroups. In-silico point mutagenesis in the wild-type modeled protein structure was performed for each variant. Wild-type and mutated structures were analyzed by coarse-grained NMA to quantify protein stability as entropy difference value (ΔG) for each variant. Pairwise ΔG differences between all variant pairs in each structural cluster were calculated and clustered into dendrograms. Our search yielded 41 TUBB4A pathogenic variants in 126 patients, divided into 11 partially overlapping structural clusters across the TUBB4A protein. ΔG-based cluster analysis of the NMA results revealed a continuum of genotype–phenotype correlation across each structural cluster, as well as in transition areas of partially overlapping structural clusters. Benign/likely benign variants were integrated into the genotype–phenotype continuum as expected and were clearly separated from pathogenic variants. We conclude that our results support the incorporation of the NMA-based approach used in this study in the interpretation of variant pathogenicity and phenotype prediction in TUBB4A-related disease. Moreover, our results suggest that NMA may be of value in variant interpretation in additional monogenic conditions.

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