MAVEs for clinical variant interpretation – opportunities and challenges

MAVEs for clinical variant interpretation – opportunities and challenges

In our last blog post, we introduced Multiplexed Assays of Variant Effect (MAVEs) and gave an overview of how they can be applied towards clinical variant interpretation. In this post, we will highlight some of the opportunities and challenges that arise from working with this valuable and complex source of information. As you’ll see, MAVEs play an important role in resolving VUS, often making the difference between a VUS and a definitive benign or pathogenic classification1. Nevertheless, they are widely underutilized due to challenges of data curation and analysis, evaluation of assay quality, and conversion of data into evidence that can be applied within the variant classification framework set forth by the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP)2. Let’s start with opportunities.

Opportunities

  1. Proactive evidence on a massive scale. By design, MAVEs collect data for hundreds to thousands of variants in a single experiment. Under the current ACMG/AMP guidelines, these data are admissible as “functional evidence” towards clinical variant classification2,3. For example, a single MAVE performed on the BRCA1 gene generated functional data for 4,000 variants4, enabling reclassification of 49% of VUS observed by a clinical laboratory1. To date, MAVEs have generated data for 11 million variants, including for high-impact disease genes, such as BRCA1/2, TP53, MSH2, and many more5.
  2. Leveling the playing field: rare variants and underrepresented populations. Unlike other lines of evidence that are used to classify variants – such as population and family segregation data – MAVEs generate data irrespective of variant prevalence in the population and without bias towards ancestry. While other lines of evidence are limited by the ability to observe variants in populations, MAVEs are typically designed to cover all or almost all possible variants in a gene or gene region. This design enables collection of evidence on variants that are rare in the general population or are underrepresented in population databases due to bias towards European ancestry. Thus, MAVEs are the best hope to level the playing field for rare variants and those found in underrepresented populations. 
  3. Further innovation: New technologies enabling genome-scale MAVEs. Recent advances in gene editing technologies – such as base-editing and PRIME editing – are expanding the repertoire and scale of variants that can be tested. A recent base-editing study probed more than 50,000 variants across 3,500 genes6. Furthermore, a coordinated, international effort has proposed an Atlas of Variant Effects (AVE) covering every protein-coding gene and regulatory element5. As high-quality functional evidence becomes available for large swaths of the genome, these data will play an increasingly important role in clinical genetic variant interpretation.

Challenges

  1. Finding the data. In general, most MAVEs are published in academic journals, and the datasets are attached as supplemental files or deposited in repositories such as the Gene Expression Omnibus (GEO) or the Sequence Read Archive (SRA). Importantly, efforts are underway to establish a centralized repository of MAVE data through MaveDB7. Nevertheless, identifying all relevant studies for a given gene and finding the associated data can be challenging and time-consuming. For example, by our count there are 10 MAVEs for BRCA1 associated with 8 different publications. MAVEvidence addresses this challenge by making all MAVE data available in a single platform, searchable by variant.
  2. Evaluating assay quality. Although all MAVEs are defined by high-throughput assessment of variant function, assays vary widely with respect to experimental model system (i.e. cell type), method for generating the variant pool, and strategy for sorting variants and measuring functional impact. Furthermore, research groups differ in experimental protocols, standards for rigor and reproducibility, and methods for data analysis. It is therefore challenging to assess the relative quality of different assays. MAVEvidence solves this by performing an independent evaluation of each assay based on its ability to distinguish known pathogenic and benign variants. On MAVEvidence reports, assays are ranked by overall performance.
  3. Converting data into evidence. Perhaps the greatest challenge of leveraging MAVEs is deciding what strength of evidence to apply towards the final classification. Published guidelines recommend assigning an evidence strength (e.g. supporting, moderate, strong) in accordance with the odds of pathogenicity corresponding to the variant effect score3. How this is done in practice, however, varies widely among clinical laboratories, is time-consuming, and commonly leads to under- or over-weighting of evidence. To maximize accuracy, determining the appropriate strength requires careful calibration of the dataset with known clinical reference variants (“controls”). MAVEvidence performs a rigorous and standardized calibration using the latest clinical annotations, ensuring that evidence is weighted appropriately in line with ACMG/AMP guidelines.

How can I get started using MAVE-derived evidence 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 [email protected]. Our team is always available to assist you with integrating MAVE-derived evidence into your variant classification workflows.

 

  1. 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).
  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. 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).
  4. Findlay, G. M. et al. Accurate classification of BRCA1 variants with saturation genome editing. Nature vol. 562 217–222 Preprint at https://doi.org/10.1038/s41586-018-0461-z (2018).
  5. Fowler, D. M. et al. An Atlas of Variant Effects to understand the genome at nucleotide resolution. Genome Biol. 24, 147 (2023).
  6. Hanna, R. E. et al. Massively parallel assessment of human variants with base editor screens. Cell 184, 1064–1080.e20 (2021).
  7. Esposito, D. et al. MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect. Genome Biol. 20, 223 (2019).