Abstract
The rapid expansion of clinical genetic testing has markedly improved the detection of genetic variants. However, most variants lack the evidence needed to classify them as pathogenic or benign, resulting in the accumulation of variants of uncertain significance that cannot be used to diagnose or guide treatment of disease. Moreover, targeted therapy for cancer treatment increasingly depends on correctly identifying oncogenic driver mutations, but the oncogenicity of many variants identified in tumours remains unclear. To address these challenges, efforts to classify variants are increasingly using multiplexed assays of variant effect (MAVEs), which are massively scaled experiments that can generate functional data for thousands of variants simultaneously. The rise of MAVEs is accompanied by better guidance on the use of MAVE data for classifying germline variants to aid their clinical implementation. Here, we overview MAVE technologies from their inception to their increased use in the clinic, including their roles in uncovering mechanisms for variant pathogenicity and guiding targeted therapy and drug development.
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A.E.M. researched the literature. A.E.M., D.M.F. and L.M.S. contributed substantially to discussion of the content. All authors wrote the article. A.E.M., D.M.F., L.M.S. and M.T. reviewed and/or edited the manuscript before submission.
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Related links
ACMG list of reportable secondary findings: https://search.clinicalgenome.org/kb/genes/acmgsf?page=1&size=25&order=asc&sort=symbol&search
Atlas of Variant Effects Alliance: https://www.varianteffect.org/
ClinGen: https://clinicalgenome.org/
ClinVar: https://www.ncbi.nlm.nih.gov/clinvar/
gnomAD: https://gnomad.broadinstitute.org/
Impact of Genomic Variation on Function Consortium: https://igvf.org/
MaveDB: https://www.mavedb.org/
Sequence Variant Interpretation: https://clinicalgenome.org/working-groups/sequence-variant-interpretation/
Supplementary information
Glossary
- Acute myeloid leukaemia
-
(AML). An aggressive haematological malignancy characterized by the clonal proliferation of immature myeloid cells in the bone marrow, blood and other tissues, leading to bone marrow failure, anaemia, infection and bleeding. AML is noted for its rapid progression and poor prognosis, particularly in older adults.
- CADINS
-
(CARD11-associated atopy with dominant interference of nuclear factor-κB signalling). A rare immunological disorder characterized by severe atopic dermatitis, asthma, food allergies and recurrent infections caused by dominant-negative mutations in the CARD11 gene that disrupt nuclear factor-κB signalling.
- Deep neural network
-
A complex machine-learning model with multiple layers of interconnected nodes (neurons) that can learn hierarchical representations of data. These networks are particularly effective at handling large and high-dimensional data sets.
- Dominant-negative
-
A type of mutation in which the altered protein interferes with the function of the normal (wild-type) protein produced by the other allele. Dominant-negative variants often occur in genes encoding proteins that function as part of a multimeric complex, in which the mutant protein can incorporate into the complex and disrupt its normal function.
- Etoposide
-
A chemotherapeutic agent that inhibits the enzyme topoisomerase II, which is essential for DNA unwinding and replication. By stabilizing the transient DNA–topoisomerase II complex, etoposide prevents the re-ligation of DNA strands, leading to the accumulation of DNA double-stranded breaks. This disruption in DNA replication and repair ultimately results in cytotoxicity and cell death.
- Long QT syndrome
-
A cardiac disorder characterized by prolonged QT intervals on electrocardiograms, which can lead to arrhythmias, syncope, seizures and sudden death. It can be caused by variants in genes encoding cardiac ion channels, including SCN5A.
- Naive Bayes classifier
-
A probabilistic machine-learning algorithm based on Bayes’ theorem, which assumes that the features used for classification are conditionally independent. This assumption simplifies the computation of the posterior probabilities, making the algorithm computationally efficient and easy to implement.
- Pathomechanism
-
The specific molecular process by which a genetic variant causes disease, including alterations in protein structure, function, localization, abundance or interactions with other macromolecules that lead to disease.
- Probands
-
The first affected family member who brings a genetic disorder to medical attention and serves as the starting point for a genetic study of a family.
- Random forest classifier
-
A machine-learning algorithm that constructs multiple decision trees during training and outputs the class that is the mode of the classes of the individual trees. It is particularly well suited for high-dimensional data analysis — owing to its ability to handle large numbers of predictor variables and model complex interactions among them — and is known for its high prediction accuracy and robustness against overfitting.
- Yeast display
-
A protein engineering technique that uses yeast cells to display proteins of interest on their cell surface, allowing for high-throughput screening of protein–ligand interactions, protein stability and other biochemical properties. Yeast display leverages the advantages of eukaryotic systems, such as proper protein folding and post-translational modifications, while enabling easy genetic manipulation and culturing.
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McEwen, A.E., Tejura, M., Fayer, S. et al. Multiplexed assays of variant effect for clinical variant interpretation. Nat Rev Genet 27, 137–154 (2026). https://doi.org/10.1038/s41576-025-00870-x
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DOI: https://doi.org/10.1038/s41576-025-00870-x
