Combining multiplexed functional data to improve variant classification - Genome Medicine

Background With the surge in the number of variants of uncertain significance (VUS) reported in ClinVar in recent years, there is an imperative to resolve VUS at scale. Multiplexed assays of variant effect (MAVEs), which allow the functional consequence of 100s to 1000s of genetic variants to be measured in a single experiment, are emerging as a powerful source of evidence which can be used in clinical variant classification. Increasingly, multiple published MAVEs are available for the same gene, sometimes measuring different aspects of variant impact. When multiple functional roles of a gene need to be considered, combining data from multiple MAVEs may provide a more comprehensive measure of the consequence of a genetic variant, which could impact variant classifications. Methods We curated published datasets from five MAVEs for the gene TP53, two MAVEs for LDLR and two MAVEs for PTEN. Statistical methods (principal component analysis), unsupervised learning (k-means clustering), and supervised learning (Naïve Bayes and random forest classifiers) were used to integrate multiple MAVE datasets. The utility of MAVE integration methods were assessed using standard metrics (sensitivity, specificity, etc) as well as evidence strength in a putative variant classification framework. Results Here, we provide guidance for combining such multiplexed functional data, incorporating a stepwise process from data curation and collection to model generation and validation. We also present a web applet that allows users to test various methods for combining score sets from multiple assays, calculate integrated functional scores for all variants, and assess whether combining data enables the application of stronger evidence for pathogenicity or benignity. In general, supervised learning methods such as random forest led to improved variant classification as compared to any individual MAVE dataset. Conclusions By following the steps outlined herein with appropriate guardrails, researchers can maximize the value of MAVEs, strengthen the functional evidence for clinical variant classification, and potentially uncover novel mechanisms of pathogenicity for clinically relevant genes.

doi.org

Combining multiplexed functional data to improve variant classification - Genome Medicine

Background With the surge in the number of variants of uncertain significance (VUS) reported in ClinVar in recent years, there is an imperative to resolve VUS at scale. Multiplexed assays of variant effect (MAVEs), which allow the functional consequence of 100s to 1000s of genetic variants to be measured in a single experiment, are emerging as a powerful source of evidence which can be used in clinical variant classification. Increasingly, multiple published MAVEs are available for the same gene, sometimes measuring different aspects of variant impact. When multiple functional roles of a gene need to be considered, combining data from multiple MAVEs may provide a more comprehensive measure of the consequence of a genetic variant, which could impact variant classifications. Methods We curated published datasets from five MAVEs for the gene TP53, two MAVEs for LDLR and two MAVEs for PTEN. Statistical methods (principal component analysis), unsupervised learning (k-means clustering), and supervised learning (Naïve Bayes and random forest classifiers) were used to integrate multiple MAVE datasets. The utility of MAVE integration methods were assessed using standard metrics (sensitivity, specificity, etc) as well as evidence strength in a putative variant classification framework. Results Here, we provide guidance for combining such multiplexed functional data, incorporating a stepwise process from data curation and collection to model generation and validation. We also present a web applet that allows users to test various methods for combining score sets from multiple assays, calculate integrated functional scores for all variants, and assess whether combining data enables the application of stronger evidence for pathogenicity or benignity. In general, supervised learning methods such as random forest led to improved variant classification as compared to any individual MAVE dataset. Conclusions By following the steps outlined herein with appropriate guardrails, researchers can maximize the value of MAVEs, strengthen the functional evidence for clinical variant classification, and potentially uncover novel mechanisms of pathogenicity for clinically relevant genes.

doi.org

An integrated, scaled approach to resolve TSC2 variants of uncertain significance - Nature Communications

Variants of uncertain significance pose a significant challenge for genetic diagnosis of tuberous sclerosis complex. Here, the authors present scalable variant effect assay data which will improve gen...

nature.com

An integrated, scaled approach to resolve TSC2 variants of uncertain significance - Nature Communications

Variants of uncertain significance pose a significant challenge for genetic diagnosis of tuberous sclerosis complex. Here, the authors present scalable variant effect assay data which will improve gen...

nature.com

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ALT: a man is talking on a cell phone with the words britbox and shetland behind him

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a woman wearing a hat and a trench coat is looking at the camera .

ALT: a woman wearing a hat and a trench coat is looking at the camera .

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Childs Play - "I Am a Game Tech"

This is "Childs Play - "I Am a Game Tech"" by Chris Coleman on Vimeo, the home for high quality videos and the people who love them.

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Landscapes of missense variant impact for human superoxide dismutase 1

SOD1 variants cause the motor neuron disease amyotrophic lateral sclerosis. Axakova et al. functionally assay ∼86% of all possible SOD1 missense variants, producing a variant-effect map resource that ...

cell.com

Search LibGen, the Pirated-Books Database That Meta Used to Train AI

Millions of books and scientific papers are captured in the collection’s current iteration.

theatlantic.com

Protocol to perform multiplexed assays of variant effect using curated loci prime editing

Multiplexed assays of variant effect (MAVEs) perform simultaneous characterization of many variants. Here, we present a protocol to perform MAVEs usin…

sciencedirect.com

Protocol to perform multiplexed assays of variant effect using curated loci prime editing

Multiplexed assays of variant effect (MAVEs) perform simultaneous characterization of many variants. Here, we present a protocol to perform MAVEs usin…

sciencedirect.com