Genetic variants produce complex phenotypic effects that confound current assays and predictive models. We developed variant in situ sequencing (VIS-seq), a pooled, image-based method measuring variant effects on molecular and cellular phenotypes in diverse cell types. Applying VIS-seq to ∼3,000 LMNA and PTEN variants yielded high-dimensional morphological profiles capturing changes in protein abundance, localization, activity, and cell architecture. VIS-seq identified a subset of linker-subdomain LMNA variants that increase nuclear circularity, in contrast to aggregating or low-abundance rod-subdomain variants that decrease circularity. VIS-seq also identified autism-associated PTEN variants that mislocalize and accurately distinguished autism-linked from tumor syndrome-linked and gnomAD control variants. Most variants impacted a multidimensional phenotypic continuum not recapitulated by any single functional readout. By linking variants to cell images at scale, VIS-seq illuminates how variant effects cascade from molecules to subcellular structures to cells, providing a framework for resolving the complexity of variant function.

Purpose:Genetic variant reclassification is increasingly common in clinical genomics, yet limited data describe how patients experience re-contact and variant reclassification in routine clinical care. Methods:We conducted semi-structured qualitative interviews with 20 adult patients who received a variant reclassification following routine clinical genetic testing. Interviews explored emotional responses, communication experiences, and perceived value of genetic testing. Data were analyzed using Template Analysis, a form of thematic analysis. Results:Three overarching themes were identified. Participants identified a need for improved communication of reclassified results, particularly with respect to timing, modality, and contextualization (Theme 1). Experiences with reclassification also shaped perceptions of the value of genetic testing, with most participants viewing testing as worthwhile despite its evolving nature (Theme 2). Finally, many participants interpreted reclassification as evidence of personalized and ongoing care, reinforcing trust in genetic testing and biomedical research (Theme 3). Participants generally preferred to be informed of reclassified results regardless of reclassification type, although the direction of reclassification influenced emotional responses and preferred modes of communication. Downgrades from variants of uncertain significance to benign or likely benign were widely viewed as meaningful by participants. Conclusion:Variant reclassification was experienced as a signal of personalized, ongoing care. Timely, contextualized, patient-centered re-contact practices may reduce uncertainty, strengthen trust, and help patients not feel forgotten.

Obtaining a precise genetic tuberous sclerosis diagnosis is a challenge as many missense TSC2 variants are variants of uncertain significance (VUS). VUS in TSC2 have been resolved by one-at-a-time functional assays, but these assays cannot scale to the 3,634 TSC2 missense VUS observed so far. To address this challenge, we used massively parallel sequencing to measure the steady-state abundance of almost 9,000 TSC2 missense variants and developed an mTOR pathway activity assay using genome editing and cell sorting to generate activity scores for 391 missense variants. 1,288 of 8,891 (14.49%) missense variants assayed had altered TSC2 abundance, and 69 of 391 (17.65%) missense variants assayed had altered mTOR pathway activity. Calibration and integration of these data into classification of variants identified in a clinical cohort putatively reclassified 212 of 276 (76.8%) TSC2 missense VUS. These datasets will lead to improved genetic diagnosis of tuberous sclerosis with potential positive impacts on the clinical management of patients and their families.

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.

Over 90% of missense variants across ~4,000 disease-associated genes are variants of uncertain significance (VUS). Experimental variant effect measurements provide critical evidence about pathogenicity and inform disease biology, but most variants lack data and clinical translation has been limited. The Impact of Genomic Variation on Function Consortium generated experimental data for 62,215 variants across ten genes using multiplexed assays and 1,407 variants across 163 genes using arrayed assays, curated 193,139 additional community-generated variant effect measurements across 30 additional genes, and developed automated calibration methods for translating experimental data and variant effect predictions into clinical evidence. To reduce current VUS, we developed a scalable workflow using only experimental and predictive evidence, enabling reclassification of 75% of the 16,115 VUS in these genes as pathogenic or benign with <1% error. To minimize future VUS, we analyzed >90,000 unobserved variants; 62% had enough evidence to be "preclassified" as pathogenic or benign. We validated our data, evidence and classifications using All of Us and created interactive resources to enable clinical use of the calibrated data. Thus, for 40 genes, representing 1% of the clinical genome, we resolve most existing VUS and future variants, illustrating how systematic use of scalable evidence can empower genomic medicine.

Purpose:Multiplexed assays of variant effect (MAVEs) are transforming clinical variant interpretation. However, many genes are associated with more than one disease, making it unclear whether functional data generated in one disease context may be directly applicable to another. For example, germline BAP1 missense variants are associated with both BAP1 tumor predisposition syndrome ( BAP1 -TPDS) and Küry-Isidor syndrome (KURIS), a rare neurodevelopmental disorder. Here, we demonstrate how phenotype-specific calibration of BAP1 MAVE data enables disease-specific variant classification. Methods:Saturation genome editing (SGE) data for BAP1 were recalibrated using either BAP1 -TPDS- or KURIS-associated missense variants as pathogenic controls. Functional evidence strength was quantified using the Odds of Pathogenicity (OddsPath) framework and mapped to ACMG/AMP PS3/BS3 criteria. Recalibrated functional evidence was integrated with standard clinical criteria for variant classification. A workshop was developed to teach phenotype-specific MAVE recalibration to clinicians and variant curators and evaluated for educational impact. Results:Phenotype-specific recalibration using BAP1 -TPDS and KURIS controls yielded OddsPath values consistent with PS3_Strong evidence in both contexts. Application of KURIS-specific recalibration enabled the diagnosis of KURIS in an individual with a previously uncertain BAP1 missense variant. The educational workshop enabled quantitatively improved understanding in applying functional evidence. Conclusion:Phenotype-specific recalibration enables appropriately calibrated reuse of MAVE datasets across distinct disease contexts, increasing the clinical utility of MAVE datasets and the interpretability of variants in pleiotropic genes. This framework expands the diagnostic utility of existing functional datasets without requiring new experimental assays.

PURPOSE:Variants of uncertain significance (VUS) are frequently encountered during clinical genetic testing. To explore the clinical burden of VUS, we developed the Brotman Baty Institute Clinical Variant Database, which is an electronic health record (EHR)-linked database of clinical germline genetic variant information from patients with rare genetic disorders seen at 2 tertiary academic medical centers. METHODS:We retrospectively reviewed EHRs and genetic testing reports from 5158 patients seen across diverse adult genetics practices at these institutions from 2015 to 2024. We also compared these EHR-based variant classifications with those in ClinVar. RESULTS:The number of reported VUS relative to pathogenic or likely pathogenic variants can vary by over 14-fold depending on the primary indication for genetic testing and 3-fold depending on self-reported race. Furthermore, at least 1.6% of variant classifications used in the EHR for clinical care are outdated based on ClinVar variant classifications, including 26 instances in which the testing lab updated ClinVar, but the reclassification was never communicated to the patient. CONCLUSION:Our findings reveal that the clinical burden of VUS in adult medical genetics is unequally distributed across patients. We also highlight a deficiency in existing systems for communicating variant reclassifications to ClinVar, patients, and providers.

Variant-level functional data are a core component of clinical variant classification and can aid in reinterpreting variants of uncertain significance (VUSs). However, the usage of functional data by genetics professionals is currently unknown. An online survey was developed and distributed in the spring of 2024 to individuals actively engaged in variant interpretation. Quantitative and qualitative methods were used to assess responses. 190 eligible individuals responded, with 93% reporting interpreting 26 or more variants per year. The median respondent reported 11-20 years of experience. The most common professional roles were laboratory medical geneticists (23%) and variant review scientists (23%). 77% reported using functional data for variant interpretation in a clinical setting, and overall, respondents felt confident assessing functional data. However, 67% indicated that functional data for variants of interest were rarely or never available, and 91% considered insufficient quality metrics or confidence in the accuracy of data as barriers to their use. 94% of respondents noted that better access to primary functional data and standardized interpretation of functional data would improve usage. Respondents also indicated that handling conflicting functional data is a common challenge in variant interpretation that is not performed in a systematic manner across institutions. The results from this survey showed a demand for a comprehensive database with reliable quality metrics to support the use of functional evidence in clinical variant interpretation. The results also highlight a need for guidelines regarding how putatively conflicting functional data should be used for variant classification.

When investigating whether a variant identified by diagnostic genetic testing is causal for disease, applied genetics professionals evaluate all available evidence to assign a clinical classification. Functional assays of higher and higher throughput are increasingly being generated and, when appropriate, can provide strong functional evidence for or against pathogenicity in variant classification. Despite functional assay data representing unprecedented value for genomic diagnostics, challenges remain around the application of functional evidence in variant curation. To investigate a growing gap articulated in recent international studies, we surveyed genetic diagnostic professionals in Australasia to assess their application of functional evidence in clinical practice. The survey results echo the universal difficulty in evaluating functional evidence but expand on this by indicating that even self-proclaimed expert respondents are not confident to apply functional evidence, mainly due to uncertainty around practice recommendations. Respondents also identified the need for support resources and educational opportunities, and in particular requested expert recommendations and updated practice guidelines to improve translation of experimental data to curation evidence. We then collated a list of 226 functional assays and the evidence strength recommended by 19 ClinGen Variant Curation Expert Panels. Specific assays for more than 45,000 variants were evaluated, but evidence recommendations were generally limited to lower throughput and strength. As an initial step, we provide our collated list of assay evidence as a source of international expert opinion on the evaluation of functional- evidence and conclude that these results highlight an opportunity to develop additional support resources to fully utilize functional evidence in clinical practice.

BARD1 variants are associated with hereditary breast cancer and neuroblastoma, yet, 98% of missense variants remain variants of uncertain significance (VUS). We applied Saturation Genome Editing (SGE) to assess 8,818 SNVs and 2,097 3-base pair deletions for their effects on cell survival and RNA abundance. We found that 13% of missense variants are loss-of-function (LoF), with 98% in BARD1’s three folded domains. LoF missense variants in the ankyrin repeat and BRCT domains were enriched in breast cancer cohorts, linking their molecular functions to cancer risk. SGE discriminated known pathogenic from benign variants with exceptional accuracy (AUC = 0.99) and resolved 95.4% of VUS, demonstrating high clinical utility. The single nucleotide resolution allowed discrimination of variant effects on RNA abundance from those affecting protein function and provided further evidence linking BARD1’s function in homology directed repair to tumor suppression. This comprehensive variant effect dataset informs inherited cancer risk and treatment decisions.

High-throughput functional assays measure the effects of variants on macromolecular function and can aid in reclassifying the rapidly growing number of variants of uncertain significance. Under the current clinical variant classification guidelines, using functional data as a line of evidence to assert pathogenicity relies on determining assay score thresholds that define variants as functionally normal or functionally abnormal. These thresholds are designed to maximize the separation of variants with known clinical effects (benign, pathogenic) and often incorporate expert opinion. However, this approach lacks the rigor of calibration, in which a variant's posterior probability of pathogenicity must be estimated from the raw experimental score and mapped to discrete evidence strengths. To build upon the existing guidelines, we introduce and evaluate a method for calibrating continuous high-throughput functional data as a line of evidence in clinical variant classification. Assay score distributions of synonymous variants and variants appearing in gnomAD for a given functional scoreset are jointly modeled with score distributions of known pathogenic and benign variants using a multi-sample skew normal mixture of distributions. This model is learned using a constrained expectation-maximization algorithm that provably preserves the monotonicity of pathogenicity posteriors and is subsequently used to calculate variant-specific evidence strengths for use in the clinic. Using 24 datasets from 14 genes, we first assess the model's ability to capture assay score distributions. We then demonstrate its potential impact on reclassifying variants by comparing the evidence strengths assigned at the variant-level with those assigned uniformly to all functionally normal and abnormal variants under the existing ClinGen guidelines. An improved classification of variants will directly improve the accuracy of genetic diagnosis and subsequent medical management for individuals affected by Mendelian disorders. Availability:https://github.com/dzeiberg/mave_calibration.

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. Multiplexed assays of variant effect (MAVEs) are highly scalable experimental approaches used to generate functional data for genetic variants. In this Review, McEwen et al. discuss the advances in MAVE technologies and guidance on how to use MAVE data in the clinic, which is helping to reveal variant pathogenicity, develop personalized drugs and inform targeted therapies.

Variant interpretation remains one of the most significant challenges in clinical genetics. Variants of uncertain significance (VUS) undermine precision medicine implementation because they have an unknown relationship to disease and cannot be used for clinical decision-making. While evidence from multiplexed assays of variant effect (MAVEs) and other functional assays can help classify variants, major barriers prevent routine use in clinical variant classification, including fragmentation across multiple repositories, insufficient data standards, and the need to calibrate assays clinically. Here we address these challenges by presenting a new interface for the MaveDB database called MaveMD (MAVEs for MeDicine) that integrates with external resources such as ClinVar and the ClinGen Allele Registry, displays clinical evidence calibrations, provides intuitive visualizations, and exports structured evidence compatible with ACMG/AMP variant classification guidelines. MaveMD implements automatic mapping of MaveDB datasets to the human reference genome, dramatically simplifying the clinical translation of new MAVE data. We also defined a new metadata model after curating 438,318 variant effect measurements from 74 MAVE datasets spanning 32 disease-associated genes, and created an interface aimed at enabling effective clinical decision-making. Thus, MaveMD makes MAVE data accessible and easily usable for variant classification, and will scale seamlessly with future data generation efforts to empower the use of MAVE evidence in clinical practice.

Multiplexed assays of variant effect (MAVEs) are a critical tool for researchers and clinicians to understand genetic variants. Here we describe the 2024 update to MaveDB ( https://www.mavedb.org/ ) with four key improvements to the MAVE community's database of record: more available data including over 7 million variant effect measurements, an improved data model supporting assays such as saturation genome editing, new built-in exploration and visualization tools, and powerful APIs for data federation and streamlined submission and access. Together these changes support MaveDB's role as a hub for the analysis and dissemination of MAVEs now and into the future.

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 source of evidence which can be used for clinical gene variant classification. Increasingly, there are multiple published MAVEs for the same gene, sometimes measuring different aspects of variant impact. Where multiple functional consequences may need to be considered to get a more complete understanding of variant effects for a given gene, combining data from multiple MAVEs may lead to the assignment of increased evidence strength which could impact variant classifications. 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 illustrate the potential of this approach by showing the integration of multiplexed functional data from four MAVEs for the gene TP53. By following these steps, researchers can maximize the value of MAVEs, strengthen the functional evidence for clinical variant classification, reclassify more VUS, and potentially uncover novel mechanisms of pathogenicity for clinically relevant genes.

In silico variant effect predictions are available for nearly all missense variants but played a minimal role in clinical variant classification because they were deemed to provide only supporting evidence. Recently, the ClinGen Sequence Variant Interpretation (SVI) Working Group updated recommendations for variant effect prediction use. By analyzing control pathogenic and benign variants across all genes, they were able to compute evidence strength for predictor score intervals with some intervals generating moderate, strong, or even very strong evidence. However, this genome-wide approach could obscure heterogeneous predictor performance in different genes. We quantified the gene-by-gene performance of two top predictors, REVEL and BayesDel, by analyzing control variants in each predictor score interval in 3,668 disease-relevant genes. Approximately 10% of intervals had sufficient control variants for analysis, and ∼70% of these intervals exceeded the maximum number of incorrect predictions implied by the SVI recommendations. These trending discordant intervals arose owing to the divergence of the gene-specific distribution of predictions from the genome-wide distribution, suggesting that gene-specific calibration is needed in many cases. Approximately 22% of ClinVar missense variants of uncertain significance in genes we analyzed (REVEL = 100,629, BayesDel = 71,928) had predictions in trending discordant intervals. Thus, genome-wide calibrations could result in many variants receiving inappropriate evidence strength. To facilitate a review of the SVI’s calibrations, we developed a web application enabling visualization of gene-specific predictions and trending concordant and discordant intervals.

To determine if a variant identified by diagnostic genetic testing is causal for disease, applied genetics professionals evaluate all available evidence to assign a clinical classification. Experimental assay data can provide strong functional evidence for or against pathogenicity in variant classification, but appears to be underutilised. We surveyed genetic diagnostic professionals in Australasia to assess their application of functional evidence in clinical practice. Results indicated that survey respondents are not confident to apply functional evidence, mainly due to uncertainty around practice recommendations. Respondents also identified need for support resources, educational opportunities, and in particular requested expert recommendations and updated practice guidelines to improve translation of experimental data to curation evidence. As an initial step, we have collated a list of functional assays recommended by 19 ClinGen Variant Curation Expert Panels as a source of international expert opinion on functional evidence evaluation. Additional support resources for diagnostic practice are in development.

Radiation-associated sarcomas are an uncommon complication of therapeutic radiation. However, their prevalence has increased with the more widespread use of this treatment modality. The clinical, pathologic and genetic characteristics of radiation-associated sarcomas are not fully understood. In this study we describe the features of 94 radiation-associated sarcomas reviewed at our institution between 1993 and 2018, evaluate their overall survival (OS) and progression-free survival (PFS) outcomes, and compare them with their sporadic counterparts reviewed within the same time period. Histologic subtypes of all radiation-associated sarcomas included 31 (33%) undifferentiated sarcomas, 20 (21%) osteosarcomas, 17 (18%) angiosarcomas, 10 (11%) malignant peripheral nerve sheath tumor (MPNST), 9 (10%) leiomyosarcomas, 4 (4%) myxofibrosarcomas, and 3 (3%) rhabdomyosarcomas. Six patients had a documented cancer predisposition syndrome. The most common preceding neoplasms included adenocarcinoma (47%) and squamous cell carcinoma (19%), with a mean latency of 13 years. Multivariable Cox survival analysis demonstrated that advanced stage at diagnosis based on pT category (AJCC eighth edition) and fragmented resection were associated with worse survival outcomes. In addition, there was a statistically significant difference in PFS between radiation-associated undifferentiated sarcomas and MPNST when compared to their sporadic counterparts using the Kaplan-Meier method and Log-rank analysis. Overall, our study shows that radiation-associated sarcomas comprise a wide clinico-pathologic spectrum of disease, with a tendency for aggressive clinical behavior. This study further delineates the understanding of these uncommon diseases. Future studies are necessary to better understand the genetic and epigenetic changes that drive the differences in behavior between these tumors and their sporadic counterparts, and to offer better treatment options.

AbstractAcross 20 vaccine breakthrough cases detected at our institution, all 20 (100%) infections were due to variants of concern (VOC) and had a median Ct of 20.2 (IQR=17.1-23.3). When compared to 5174 contemporaneous samples sequenced in our laboratory, VOC were significantly enriched among breakthrough infections (p < .05).

A central problem in genomics is understanding the effect of individual DNA variants. Multiplexed Assays of Variant Effect (MAVEs) can help address this challenge by measuring all possible single nucleotide variant effects in a gene or regulatory sequence simultaneously. Here we describe MaveDB v2, which has become the database of record for MAVEs. MaveDB now contains a large fraction of published studies, comprising over two hundred datasets and three million variant effect measurements. We created tools and APIs to streamline data submission and access, transforming MaveDB into a hub for the analysis and dissemination of these impactful datasets.