What are MAVEs and how can they be used in clinical variant classification?

What are MAVEs and how can they be used in clinical variant classification?

Multiplexed Assays of Variant Effect (or MAVEs) are a type of functional evidence that can be applied within the ACMG/AMP framework for classifying genetic variants. In this post, we cover what you need to know to start applying MAVEs to your genetic variant classification workflows.

What is functional evidence?

Functional evidence in the context of genetic variant classification is empirical data that provides insights into the role and impact of specific genetic variants. This type of evidence is collected using model systems, such as cell cultures, yeast, or animal models, during controlled laboratory experiments. Scientists perform these experiments to understand how a genetic variant affects the function of a gene or gene product. For example, if a particular genetic variant is suspected to cause a disease, researchers might create a model system that carries this variant and then observe the system for any abnormalities in cellular processes, gene expression, or protein function that could be linked to the disease.

How are MAVEs performed and how do they differ from historical approaches to generating functional evidence?

MAVEs represent a significant leap forward in the creation of functional evidence for variant classification. Unlike traditional methods, which often focus on a single or a small number of variants, MAVEs are high-throughput assays that simultaneously assess the functional impacts of hundreds or even thousands of genetic variants within a single experiment1. MAVEs typically employ several DNA sequencing steps and a selection or sorting step to characterize a large, pooled set of variants simultaneously, thereby reducing the time and resources needed for functional characterization per variant. As a result, MAVEs have greatly accelerated the pace at which we can generate functional evidence.

Where does functional evidence fit in the ACMG/AMP Guidelines?

The American College of Medical Genetics and Genomics (ACMG), together with the Association of Molecular Pathology (AMP), has set forth guidelines for the classification of genetic variants2, which have been widely adopted in the field.  In these guidelines, functional evidence is categorized as a strong line of evidence and is denoted as PS3 (Strong evidence of pathogenicity) or BS3 (Strong evidence of benign impact) depending on the functional outcome. The weight given to functional evidence in these guidelines underlines its importance in genetic variant classification and clinical practice.

Several studies and recommendations have endorsed the utility of MAVE-derived evidence in the ACMG/AMP framework3,4. By incorporating MAVE data, clinicians can more confidently interpret the clinical significance of genetic variants, thereby enhancing the accuracy and reliability of diagnoses and treatments.

Functional evidence, while powerful, is often not sufficient on its own for variant classification. It is generally combined with other lines of evidence such as population data, computational predictions, and clinical observations to arrive at a comprehensive understanding of a variant's impact. The ACMG guidelines advocate for a multi-faceted approach in which different types of evidence are weighed collectively. This integrated assessment ensures a more robust and accurate classification of genetic variants.

How can MAVEs help with variant classification?

One of the most promising applications of MAVE technologies is their ability to resolve Variants of Uncertain Significance (VUS). These are genetic variants for which the clinical implications are not yet clear. The addition of MAVE-based functional evidence can tip the scales, providing the necessary data to classify these variants as either benign or pathogenic5. We will delve into this topic in greater detail in our next blog post, discussing how MAVEs have been instrumental in resolving many VUS, thereby improving the accuracy and reliability of genetic diagnoses.

How can I get started with using MAVE data in my variant classification workflows?

If you're interested in integrating MAVE-derived evidence into your variant classification workflows, head over to the MAVEvidence page to learn more and sign up for a free trial today. For more personalized guidance or if you have specific questions, feel free to reach out to us at inquiries@constantiambio.com. Our team is always available to assist you with integrating MAVE-derived evidence into your variant classification workflows.

Where can I go to learn more about MAVEs?

For those interested in diving deeper into the world of MAVEs, here are several resources to get you started:

●     AVE Alliance

The Atlas of Variant Effects (AVE) Alliance is an organization focused on “bring[ing] together [MAVE] data generators, curators and consumers, along with funders and other stakeholders, to set standards, share tools and develop strategy”.

●     MaveRegistry6

MaveRegistry is “a collaborative resource for sharing progress on Multiplexed Assays of Variant Effect (MAVE)”.

●     MaveDB7

MaveDB is “a public repository for datasets from Multiplexed Assays of Variant Effect (MAVEs)”.

References

1.         Fowler, D. M. et al. An Atlas of Variant Effects to understand the genome at nucleotide resolution. Genome Biol. 24, 147 (2023).

2.         Richards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405–424 (2015).

3.         Gelman, H. et al. Recommendations for the collection and use of multiplexed functional data for clinical variant interpretation. Genome Med. 11, 85 (2019).

4.         Brnich, S. E. et al. Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework. Genome Med. 12, 3 (2019).

5.         Fayer, S. et al. Closing the gap: Systematic integration of multiplexed functional data resolves variants of uncertain significance in BRCA1, TP53, and PTEN. Am. J. Hum. Genet. 108, 2248–2258 (2021).

6.         Da Kuang et al. MaveRegistry: a collaboration platform for multiplexed assays of variant effect. Bioinformatics (2021) doi:10.1093/bioinformatics/btab215.

7.         Rubin, A. F. et al. MaveDB v2: a curated community database with over three million variant effects from multiplexed functional assays. bioRxiv 2021.11.29.470445 (2021) doi:10.1101/2021.11.29.470445.