New database turns lab-scale gene tests into clinical evidence for uncertain variants

Every year, millions of people have their genomes sequenced in search of answers to unexplained disease, and every year a large fraction of them receive a result that is simultaneously a finding and a non-answer: a variant of uncertain significance, or VUS. These are changes in DNA that look as though they could matter but that no one can yet confidently link to disease or confidently dismiss as harmless. The uncertainty is not a minor nuisance. It can stall diagnoses, leave families in limbo, and prevent the kind of early interventions that genomic medicine was supposed to deliver. A new resource described in Genome Medicine aims to shrink that category of ambiguity by making a powerful class of laboratory experiments usable at the bedside.

The resource is called MaveMD, short for MAVEs for Medicine, and it is a new interface built on top of MaveDB, a public repository for multiplexed assays of variant effect, or MAVEs. MAVEs are experiments that measure, in a single sweep, the functional consequences of thousands to tens of thousands of variants of a gene. Instead of testing one mutation at a time, researchers synthesize a library of variant versions of a gene, introduce them into cells en masse, and use high-throughput sequencing to count how each variant performs under a functional selection. The output is a quantitative effect score for nearly every possible single-letter change in a gene, and sometimes for many multi-letter changes as well. In principle, this is exactly the kind of evidence a clinical geneticist needs: a direct measurement of how badly a patient’s variant damages the protein it encodes.

In practice, MAVE data have struggled to cross the gap between bench and clinic, and the authors of the MaveMD paper are unusually candid about why. Raw MAVE scores are assay-specific numbers with no intrinsic clinical meaning; a score of minus two in one experiment is not comparable to minus two in another. Clinical variant interpretation, governed by frameworks such as the American College of Medical Genetics and Genomics criteria, demands calibrated evidence in which functional measurements are anchored to variants of known pathogenicity. Most MAVE datasets also live in formats designed for computational biologists, with dense matrices and command-line tooling that offer little to a genetic counselor trying to interpret a patient report. And without careful curation, it can be hard to know whether a given assay actually measured the biological function relevant to the disease in question.

MaveMD tackles these barriers one by one. The interface displays calibrated evidence strength, meaning that variant effect scores are translated into a form that maps onto the functional evidence categories used in clinical classification, so a clinician can see not just a number but how much weight that number can legitimately carry. It provides intuitive visualizations that let users place a variant in the context of the full distribution of measured effects across the protein, a picture that is often far more informative than a single score. It integrates with established clinical and genomic resources so that MAVE evidence can be viewed alongside the annotations clinicians already use. And it exports ready-to-use clinical evidence, packaging the functional data in a form that can be dropped directly into variant classification workflows rather than requiring bespoke analysis for every case.

The scale of what has been assembled is substantial. MaveMD currently contains 476,076 variant effect measurements spanning 39 genes with established associations to human disease. According to the authors, this body of functional data is sufficient to enable classification of 75 percent of variants of uncertain significance within the genes covered. That figure deserves emphasis. Three-quarters of the VUS in these disease genes, variants that would otherwise sit in the ambiguous middle of a clinical report, can now be assigned functional evidence that moves them toward a definitive interpretation, whether pathogenic or benign. For the families waiting on those interpretations, the difference between an uncertain result and a resolved one can determine whether relatives get tested, whether surveillance begins, and whether a diagnosis is finally named.

The technical architecture behind the resource reflects a deliberate design philosophy. MaveMD is built as an interface layer over MaveDB rather than a separate silo, which means the underlying variant effect scores remain in the community repository where they were deposited, versioned and credited to the original experimentalists. The clinical layer adds calibration, curation, and presentation on top of that foundation. Supplementary material accompanying the paper details the per-assay and per-publication curation decisions that went into the curated datasets, an important transparency measure because the clinical value of a MAVE depends heavily on the quality and relevance of the underlying experiment. A schematic of the system’s architecture and additional examples of the web interface are also provided, showing how a user moves from a gene to a specific variant to its calibrated functional evidence.

The collaboration itself spans two continents and multiple disciplines. The work was led by researchers at the University of Washington, including the Brotman Baty Institute for Precision Medicine and the departments of Laboratory Medicine and Pathology, Genome Sciences, and Bioengineering, together with colleagues at the Collaborative Centre for Genomic Cancer Medicine, the Walter and Eliza Hall Institute of Medical Research, and the University of Melbourne. Corresponding authors Douglas M. Fowler and Alan F. Rubin anchor teams that include clinical genetics expertise alongside deep experience in multiplexed functional assays, a combination that is essential for a resource whose entire purpose is to translate between laboratory measurement and clinical decision-making. The project was supported by the National Human Genome Research Institute, including an Advancing Medical Genomics award, along with funding from the Australian Government and several foundation grants.

What makes MaveMD notable in the broader landscape of genomic medicine is its timing. The cost of genome sequencing has collapsed to the point where sequencing is no longer the bottleneck; interpretation is. Clinical laboratories report VUS rates that can exceed a third of findings in some genes, and each VUS represents not only diagnostic uncertainty but also a recurring burden: the same variant gets re-evaluated independently by different laboratories, families receive periodic letters saying the classification has not changed, and cascade testing stalls. Functional data at scale offers a way to resolve large blocks of that uncertainty systematically rather than case by case. MAVEs have been maturing rapidly over the past decade, with assays now routine for well-studied genes, but the clinical community has lacked the connective tissue needed to use them routinely. MaveMD is positioned as exactly that connective tissue.

The resource also has implications for how future functional data get generated. By providing a clear clinical destination for MAVE experiments, MaveMD creates an incentive structure for researchers to design assays that measure disease-relevant functions, calibrate their scores against known pathogenic and benign variants, and deposit results in shareable, standardized form. The authors frame the platform as supporting future data generation efforts, and the feedback loop is straightforward: the more clinically usable the interface, the more valuable new datasets become, and the more the community is encouraged to fill gaps in the 39 genes currently covered. Genes beyond the current set, particularly those where the relevant function is hard to assay in a high-throughput format, remain a challenge, and functional evidence will always be one strand of evidence among many, to be weighed alongside segregation data, computational predictions, and case reports.

Still, the trajectory is clear. Genomic medicine has spent two decades building the ability to read genomes cheaply and completely, and it is now in the harder business of understanding what the readings mean. Resources like MaveMD represent the infrastructure of that second phase: databases that do not merely store experimental results but actively translate them into the evidentiary currency of clinical practice. If the 75 percent classification figure can be extended across more of the disease-associated genome, the population of patients left holding an uncertain result will shrink correspondingly. For a field whose promise has always been precision, that is the kind of progress that matters most, measured not in sequenced bases but in diagnoses finally delivered.

Subject of Research: A clinical database interface for translating multiplexed assays of variant effect into genomic medicine evidence

Article Title: MaveMD: a functional data resource for genomic medicine

Article References: McEwen, A. E., Stone, J., Tejura, M., Gupta, P., Capodanno, B. J., Da, E. Y., Grindstaff, S. B., Moore, N., Reinhart, D., Snyder, A. E., Stergachis, A. B., Starita, L. M., Fowler, D. M., & Rubin, A. F. (2026). MaveMD: a functional data resource for genomic medicine. Genome Medicine. https://doi.org/10.1186/s13073-026-01782-z

Image Credits: AI Generated

DOI: 10.1186/s13073-026-01782-z

Keywords: MaveMD, MaveDB, variants of uncertain significance, multiplexed assays of variant effect, genomic medicine, variant interpretation, functional genomics, clinical genetics, precision medicine, genetic databases, disease-associated genes, variant classification