Combining multiplexed functional data to improve variant classification.

Authors

Jeffrey D Calhoun, Moez Dawood, Charlie F Rowlands, Shawn Fayer, Elizabeth J Radford, Abbye E McEwen, Malvika Tejura, Clare Turnbull, Amanda B Spurdle, Lea M Starita, Sujatha Jagannathan

Published in

Genome medicine. Jul 11, 2026. Epub Jul 11, 2026.

Abstract

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.
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.
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.
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.

PMID:
42432739
Bibliographic data and abstract were imported from PubMed on 11 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.