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At least 19 recordsLinked to original sources
PubMed2026

Development of RS1-specific ACMG/AMP variant classification criteria with pilot variant curation.

Gene-based therapies are being developed for retinal diseases, including RS1-related X-linked retinoschisis. Therefore it is essential to determine which variants are pathogenic and which are benign when enrolling patients. The Clinical Genome Resource (ClinGen) X-Linked Inherited Retinal Diseases (XLRD) Variant Curation Expert Panel (VCEP) brings together clinician scientists, molecular biologists, and geneticists to apply their expertise and review the clinical, genetic, population, and functional evidence for variants. American College of Medical Genetics (ACMG) guidelines have been modified for RS1 to develop a highly systematic and conservative framework for evaluating variants. The curation process involves applying 28 different codes, each with 4 strength levels (very strong, strong, moderate, supporting) across different domains of phenotype, population data, computational assessment, functional impact, and segregation. With RS1-specific rules, a total of 54 pilot variants were tested. These included 47 variants in ClinVar. Of these 21 variants were re-classified: 2 likely pathogenic variants and one likely benign were changed to variants of uncertain significance and 4 previously unclassified variants were changed to pathogenic, likely pathogenic and likely benign. Other changes resolved conflicts or multiple classifications.

Humans↗
PubMed2026

The Animal Variant Classification Guidelines v2: An Update With New Criteria and Improved Clarifications.

The Animal Variant Classification Guidelines (AVCG) were developed to standardize and objectify the classification of putative disease-causing variants. These guidelines are sufficiently reproducible and are used to classify previously published and new disease-causing variants across species. Here, the guidelines are updated (AVCG.v2), based on a three-phase decision process. Overall, four new criteria and seven clarifying comments were added. The number of criteria has increased from 23 to 27, with three new criteria supporting pathogenicity and one new criterion supporting benign classification. Pharmacogenomic variants were determined to fall within the scope of the guidelines. These updated guidelines are being used by the Variant Pathogenicity Working Group (VPWG), part of the Animal Genetic Testing Standardization standing committee, which is a committee of elected members of the International Society for Animal Genetics (ISAG). Under the auspices of ISAG, the VPWG retrospectively classifies published putative disease-causing variants. The pathogenicity label for a variant will be presented in the variant tables of Online Mendelian Inheritance in Animals (OMIA; https://omia.org/). The AVCGv.2 criteria and recommendations were developed by the expertise of the animal genetics community and the ISAG Executive Committee through the Animal Genetics Testing Standardization Committee endorses and strongly encourages their use to evaluate the evidence supporting pathogenicity of putative disease-causing variants.

Animals↗
PubMed2025

Calibration of additional computational tools expands ClinGen recommendation options for variant classification with PP3/BP4 criteria.

PURPOSE: We previously developed an approach to calibrate computational tools for clinical variant classification, updating recommendations for the reliable use of variant impact predictors to provide evidence strength up to Strong. A new generation of tools using distinctive approaches has since been released, and these methods must be independently calibrated for clinical application. METHODS: Using our local posterior probability-based calibration and our established data set of ClinVar pathogenic and benign variants, we determined the strength of evidence provided by 3 new tools (AlphaMissense, ESM1b, and VARITY) and calibrated scores meeting each evidence strength. RESULTS: All 3 tools reached the Strong level of evidence for variant pathogenicity and Moderate for benignity, although sometimes for few variants. Compared with previously recommended tools, these yielded at best only modest improvements in the trade-offs between evidence strength and false-positive predictions. CONCLUSION: At calibrated thresholds, 3 new computational predictors provided evidence for variant pathogenicity at similar strength to the 4 previously recommended predictors (and comparable with functional assays for some variants). This calibration broadens the scope of computational tools for application in clinical variant classification. Their new approaches offer promise for future advancement of the field.

Humans↗
PubMed2026

LDLR Variant Classification Through Activity-Normalized Prime Editing Screening.

BACKGROUND: Inherited variants in the LDL (low-density lipoprotein) receptor (LDLR) gene are the most common cause of familial hypercholesterolemia, significantly increasing coronary artery disease risk. Early identification of pathogenic LDLR variants enables prompt lipid-lowering therapy and cascade testing of at-risk relatives; however, most LDLR variants observed in the population have uncertain or absent clinical classifications, leaving many patients without actionable information. METHODS: We developed the first activity-normalized prime editing screening pipeline to measure the impact of 5184 LDLR coding variants on LDL-cholesterol (LDL-C) uptake. Each prime editing guide RNA is paired with a genotypic outcome reporter to correct for variable editing efficiency, overcoming a key limitation of previous pooled genome editing screens. A statistical framework further improves variant effect estimates by jointly analyzing all missense variants at each amino acid position. RESULTS: We show that prime editing of the reporter construct correlates with endogenous variant installation frequency, validating the activity normalization approach. The resulting scores capture a continuous spectrum of functional effects, robustly separate pathogenic versus benign ClinVar variants, and show concordance with LDL-C levels in UK Biobank participants. We calibrate functional evidence strengths to the ACMG/AMP variant interpretation framework, enabling integration into a clinical variant classification workflow. By combining functional, computational, population, and contextual evidence, 322 of 434 LDLR variants currently classified as variants of uncertain significance, conflicting, or absent from ClinVar appear to meet evidence thresholds for reclassification and can be prioritized for expert review, substantially expanding the pool of actionable variant classifications. The screen also reveals a cluster of gain-of-function variants in LDLR class A repeat 5, at least some of which enhance LDL-C uptake through increased apolipoprotein B interaction, with implications for therapeutic genome editing. Last, prime editing uniquely detects splice-altering coding variants missed by cDNA-based screens and pathogenicity predictors, revealing an advantage of endogenous variant installation. CONCLUSIONS: Altogether, activity-normalized prime editing provides a scalable framework for LDLR variant classification that substantially expands the proportion of variants with evidence for genetic diagnosis and reveals novel biology with therapeutic relevance.

CRISPR screening↗
PubMed2026

Targeted reflex RNA sequencing for enhanced variant classification on exome and genome sequencing improves patient outcomes.

RNA sequencing (RNA-seq) has been utilized to provide functional evidence regarding the impact of splicing variants. This study explores the utility of targeted reflex RNA-seq to inform classification of predicted splicing variants identified through clinical exome sequencing (ES) and genome sequencing (GS). A retrospective analysis was conducted on consecutive ES/GS cases completed at a single center in which targeted reflex RNA-seq was performed following identification of eligible variants. There were 131 cases (4.1%) that had at least one RNA-seq eligible variant reported, with eight of these cases having two unique eligible variants. Of the 139 eligible variants, 125 were classified as variants of uncertain significance (VUS). Sixty-four cases had targeted reflex RNA-seq completed with 27 cases having at least one variant reclassified (42.2%). After reclassification, 23 cases had positive results, and two cases had a likely diagnosis of an autosomal recessive condition. Clinical outcomes data regarding positive RNA-seq cases showed that 71% (10/14) had clinical management changes and 43% (6/14) had treatment changes. Incorporation of targeted reflex RNA-seq analysis into the diagnostic pipeline of rare diseases enhances variant classification and resolves uncertainty regarding predicted splice variants, leading to an estimated 1.6% increase in diagnostic yield of clinical ES/GS.

Journal Article↗
PubMed2026

Automated patch clamp data improve variant classification and penetrance stratification for SCN5A-Brugada syndrome.

BACKGROUND AND AIMS: Brugada Syndrome (BrS) is an inherited arrhythmia disorder that causes an elevated risk of sudden cardiac death. Approximately 20% of patients with BrS have rare variants in SCN5A, which encodes the cardiac sodium channel NaV1.5. Genetic workup of BrS is often complicated by SCN5A variants of uncertain significance (VUS) and/or incomplete penetrance. This study deployed an SCN5A-BrS functional assay at cohort scale to facilitate the implementation of genetic and precision medicine. METHODS: All 252 missense and in-frame insertion/deletion SCN5A variants from a previously published large cohort of BrS cases (n = 3335 patients) were analysed using a calibrated high-throughput automated patch-clamp (APC) assay. Variant functional Z-scores were assigned evidence levels ranging from BS3_moderate (normal function) to PS3_strong (loss-of-function), as defined by American College of Medical Genetics and Genomics criteria. Functional evidence was combined with population frequency, hotspot, case counts, protein-length changes, and in silico predictions. Odds ratios of BrS case-control enrichment and penetrance for BrS were calculated from variant frequencies in the BrS cohort and in gnomAD. RESULTS: Most variants (146/252) were functionally abnormal (Z ≤ -2), with 100 having severe loss-of-function (Z ≤ -4). Functional evidence enabled the reclassification of 110 of 225 VUS; 104 to likely pathogenic and 6 to likely benign. SCN5A variants with loss-of-function were mainly localized to the transmembrane domains, especially the regions comprising the central pore. SCN5A variant penetrance was proportional to the severity of loss-of-function; variants with Z ≤ -6 had penetrance of 24.5% (15.9%-37.7% CI) and an odds ratio of 501 for BrS. CONCLUSIONS: This cohort-scale APC dataset stratifies SCN5A variants found in BrS patients into normal function 'bystander' variants that have a low risk of BrS and loss-of-function variants that have a high risk for BrS. Functional data can be integrated with other criteria to reclassify a substantial fraction of VUS. The dataset helps clarify the SCN5A-BrS relationship and will improve the diagnosis and clinical management of BrS probands and their families.

Humans↗
PubMed2026

Comprehensive evaluation of ACMG/AMP-based variant classification tools.

MOTIVATION: The American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines represent the gold standard for clinical variant interpretation. Despite the widespread adoption of ACMG/AMP guidelines, a comprehensive comparison of the software tools designed to implement them has been lacking. This represents a significant gap, as clinicians require evidence-based guidance on which tools to use in their practice. RESULTS: We benchmarked four ACMG/AMP-based tools (Franklin, InterVar, TAPES, Genebe) selected from 22 tools, and compared their performance with LIRICAL, a top-performing phenotype-driven tool, using 151 expert-curated datasets from Mendelian disorders. Selection criteria included free availability, VCF compatibility, operational reliability, and not being disease-specific. Our evaluation framework assessed top-N accuracy (N = 1, 5, 10, 20, 50), retention rates, precision, recall, F1 scores, and area under the curve (AUC). Statistical validation employed bootstrap confidence intervals (n = 1000) and Friedman tests. LIRICAL (68.21%) and Franklin (61.59%) demonstrated superior top-10 variant prioritization accuracy in Mendelian disorders, significantly outperforming other tools (P = .0000). Results demonstrate that tools with advanced phenotypic integration significantly outperform those relying primarily on genomic features. AVAILABILITY AND IMPLEMENTATION: All data and source code required to reproduce the findings of this study are openly available in the Code Ocean repository at https://doi.org/10.24433/CO.6562438.v1.

Software↗
PubMed2025

The ClinGen Severe Combined Immunodeficiency Disease Variant Curation Expert Panel: Specifications for classification of variants in ADA, DCLRE1C, IL2RG, IL7R, JAK3, RAG1, and RAG2.

PURPOSE: This collaborative study, led by the Clinical Genome Resource Severe Combined Immunodeficiency Disease Variant Curation Expert Panel (ClinGen SCID-VCEP), implemented and adapted the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines for interpreting germline variants in genes with established relationships to SCID. The effort focused on the 7 most common SCID-related genes identified by SCID newborn screening in North America: ADA, DCLRE1C, IL2RG, IL7R, JAK3, RAG1, and RAG2. METHODS: The SCID-VCEP conducted a rigorous review of variants that involved database analyses, literature review, and expert feedback to derive gene-specific modifications to the ACMG/AMP guidelines. These specifications were validated using a pilot set of 90 variants. RESULTS: Of these 90 variants, 25 were classified as pathogenic, 21 as likely pathogenic, 14 as variants of uncertain significance, 18 as likely benign, and 12 as benign. Seventeen variants with conflicting classifications in ClinVar were successfully resolved. The criteria included modifications to 20 of the 28 original ACMG/AMP criteria specific to SCID-related genes. CONCLUSION: The SCID-specific variant curation guidelines developed by the SCID-VCEP will enhance the precision of SCID genetic diagnosis and provide a robust framework for interpreting variants in SCID-related genes, contributing to appropriate treatment of SCID.

Humans↗
PubMed2025

Imprecision medicine: Systematic gaps in reporting variants of uncertain significance (VUS) and their reclassifications.

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.

Humans↗
PubMed2025

HCSeeker: A classification tool for human genetic variant hot and cold spots designed for PM1 and benign criteria in the ACMG-AMP guideline.

PURPOSE: The PM1 criterion, which states that a variant is located in a mutational hot spot and/or critical and well-established functional domain without benign variation (such as the active site of an enzyme), is considered moderate evidence for assessing its pathogenicity. Although guidelines from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology are widely adopted, the PM1 criterion remains limited from lacking a reliable database of variant hot spots. Compared with hot spots, cold spots are neglected by the guidelines. To improve variant classification, we suggest including cold spots for supporting benign classifications. Consequently, we have developed the HCSeeker to provide data support for PM1 and the "Benign" criteria. METHODS: HCSeeker uses the Kernel Density Estimation and the Expectation-Maximization algorithm to identify hot- and cold-spot regions. RESULTS: Through HCSeeker, we identified 988 hot spots and 682 cold spots across 889 genes and provided a public database (http://www.genemed.tech/hcseeker/) for researchers and clinicians to query variant locations, facilitating the application of American College of Medical Genetics and Genomics and the Association for Molecular Pathology PM1 or "Benign" criteria. CONCLUSION: We developed the HCSeeker tool, which can effectively identify variant hot and cold spots within genes to enhance the interpretability of gene variants.

Humans↗
PubMed2024

Classification of variants of reduced penetrance in high-penetrance cancer susceptibility genes: Framework for genetics clinicians and clinical scientists by CanVIG-UK (Cancer Variant Interpretation Group-UK).

PURPOSE: Current practice is to report and manage likely pathogenic/pathogenic variants in a given cancer susceptibility gene as though having equivalent penetrance, despite increasing evidence of intervariant variability in risk associations. Using existing variant interpretation approaches, largely based on full-penetrance models, variants in which reduced penetrance is suspected may be classified inconsistently and/or as variants of uncertain significance. We aimed to develop a national consensus approach for such variants within the Cancer Variant Interpretation Group UK (CanVIG-UK) multidisciplinary network. METHODS: A series of surveys and live polls were conducted during and between CanVIG-UK monthly meetings on various scenarios potentially indicating reduced penetrance. These informed the iterative development of a framework for the classification of variants of reduced penetrance by the CanVIG-UK Steering and Advisory Group working group. RESULTS: CanVIG-UK recommendations for amendment of the 2015 ACMG/AMP variant interpretation framework were developed for variants in which (A) active evidence suggests a reduced-penetrance effect size (eg, from case-control or segregation data) and (B) reduced penetrance effect is inferred from weaker/potentially inconsistent observed data. CONCLUSION: CanVIG-UK propose a framework for the classification of variants of reduced penetrance in high-penetrance genes. These principles, although developed for cancer susceptibility genes, are potentially applicable to other clinical contexts.

Humans↗
PubMed2025

Quantifying evidence for phenotypic specificity (PP4) for syndromic phenotypes: Large-scale integration of rare germline FH variants from diagnostic laboratory testing for HLRCC and renal cancer.

PURPOSE: Hereditary leiomyomatosis and renal cell cancer (HLRCC) is a rare cancer susceptibility syndrome exclusively attributable to pathogenic variants in FH (HGNC:3700). This article quantitatively weights the phenotypic context (PP4/PS4) of such very rare variants in FH. METHODS: We collated clinical diagnostic testing data on germline FH variants from 387 individuals with HLRCC and 1780 individuals with renal cancer and compared the frequency of "very-rare" variants in each phenotypic cohort with 562,295 population controls. We generated pan-gene very rare variant likelihood ratios (PG-VRV-LRs), domain-specific likelihood ratios for missense variants (DS-VRMV-LR) using spatial clustering analysis, and log2.08 likelihood ratios (LLRs) as applicable within the updated American College of Medical Genetics and Genomics/Association for Molecular Pathology variant classification framework. RESULTS: For HLRCC, the PG-VRV-LR was estimated to be 2669.4 (95% CI 1843.4-3881.2, LLR 10.77) for truncating variants and 214.7 (95% CI 185.0-246.9, LLR 7.33) for missense variants. For renal cancer, the PG-VRV-LR was 95.5 (95% CI 48.9-183.0, LLR 6.23) for truncating variants and 5.8 (95% CI 3.5-9.3, LLR 2.39) for missense variants. Clustering analysis in HLRCC cases revealed 3 "hotspot" regions wherein the DS-VRMV-LR increased to 1226.9. CONCLUSION: These data provide quantitative measures for very rare missense and truncating variants in FH, which reflect the differing phenotypic specificity of HLRCC and renal cancer and may be applicable in clinical variant classification.

Humans↗
PubMed2026

CanVar-UK: A collaborative platform for germline interpretation in cancer susceptibility genes.

Germline variants in cancer susceptibility genes (CSGs) are typically inherited rather than arising de novo. Hence, wide cascade testing of families across geographies is common, meaning consistency in variant classification is particularly critical. Variant interpretation requires collation of variant-level data from diverse sources, as well as assembly of comprehensive clinical data, often necessitating sharing of information between genomic testing centers. Here, we describe CanVar-UK, a freely accessible web platform bespoke designed to support interpretation of germline CSG variants. CanVar-UK contains variant-level data for over 1.1 million single-nucleotide variants (SNVs), comprising all possible coding SNVs in 116 established CSGs. The data sources with which variants are annotated include in silico scores from 11 clinically relevant tools, population allele frequencies from gnomAD v4.1, case counts from multiple cohorts, including National Health Service (NHS) clinical laboratory testing, variant-level readouts from 47 selected functional and splicing datasets across 19 CSGs, genetic epidemiology studies, and live linkage to existing consensus classifications in the ClinVar database. The diagnostic discussion forum is only available to registered diagnostic scientist users. Through this, a variant-tagged email message can be dispatched in real time across the diagnostic forum community of >1,500 users, with all exchanges and classifications captured and stored in the platform. Already widely used by NHS diagnostic clinical scientists in the UK, CanVar-UK has a rapidly growing international diagnostic user base (>800 UK and >600 non-UK registered users). Survey of the NHS diagnostic user community illustrates the wide-ranging utility of CanVar-UK within their clinical workflows for interpretation of germline CSG variants.

Journal Article↗
PubMed2025

acmgscaler: an R package and Colab for standardized gene-level variant effect score calibration within the ACMG/AMP framework.

MOTIVATION: A genome-wide variant effect calibration method was recently developed under the guidelines of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP), following ClinGen recommendations for variant classification. While genome-wide approaches offer clinical utility, emerging evidence highlights the need for gene- and context-specific calibration to improve accuracy. Building on previous work, we have developed an algorithm tailored to converting functional scores from both multiplexed assays of variant effects (MAVEs) and computational variant effect predictors (VEPs) into ACMG/AMP evidence strengths. RESULTS: Our method is designed to deliver consistent performance across different genes and score distributions, with all variables adaptively determined from the input data, preventing selective adjustments or overfitting that could inflate evidence strengths beyond empirical support. To facilitate adoption, we introduce acmgscaler, a lightweight R package and a plug-and-play Google Colab notebook for the calibration of custom datasets. This algorithmic framework bridges the gap between MAVEs/VEPs and clinically actionable variant classification. AVAILABILITY AND IMPLEMENTATION: The R package and Colab notebook are available at https://github.com/badonyi/acmgscaler.

Software↗
PubMed2025

Insulin receptor variants: Extending the traditional Mendelian spectrum.

PURPOSE: INSR encodes the insulin receptor, the essential entrainer of growth and metabolism to nutritional cues. INSR variants cause a spectrum of monogenic insulin resistance (IR) syndromes, namely, type A insulin resistance, Rabson-Mendenhall, and Donohue syndromes. However, to our knowledge, no large cohort studies focused on variant classification and its diagnostic value have been described. METHODS: This multicentric cohort study included 73 patients carrying INSR variants, referred for IR by 52 centers from 6 countries. Variants were classified using new bioinformatic tools relying on different prediction mechanisms and the American College of Medical Genetics and Genomics guidelines. RESULTS: Besides expanding the INSR mutational spectrum, this study suggested a semidominant inheritance in several Donohue/Rabson-Mendenhall syndrome families. Questioning strictly Mendelian inheritance, heterozygous loss-of-function (LoF) variants were mostly found in overweight patients, with a higher LoF frequency in IR patients than in the general population (odds ratio 5.77). Diagnostic challenges arose when trying to refine classification criteria for variants of uncertain significance. Among the variant effect predictors assessed, MISTIC and AlphaMissense outperformed REVEL. CONCLUSION: The spectrum of INSR-related disorders extends beyond traditional entities. Heterozygous INSR LoF variants may increase IR susceptibility. International collaboration and functional assays are needed to drive precision medicine forward.

Humans↗
PubMed2026

RNU4ATAC-opathy: Clinical, molecular, and transcriptomic insights from a large cohort.

PURPOSE: We aim to better define the genotype and phenotype spectrum of RNU4ATAC-opathy, demonstrate the utility of RNA sequencing (RNA-seq) for variant classification, and highlight the challenges in detecting variants in this noncoding gene. METHODS: Sixty individuals with molecularly confirmed RNU4ATAC-opathy were recruited from multiple clinical and research centers internationally. RNA-seq was available for 7 affected individuals. RESULTS: We report the clinical and molecular findings of 60 individuals, including 42 not previously described, and 33 distinct RNU4ATAC variants, 13 of which are novel. Core features in this cohort-present in most individuals assessed and varying in severity-include microcephaly, short stature, skeletal anomalies, developmental delay, cerebral anomalies, skin conditions, and immune deficiency. Additional findings, such as diabetes, holoprosencephaly, and the absence of various core features in some individuals, highlight the broad phenotypic spectrum. All individuals who underwent RNA-seq showed a consistent pattern of minor intron retention. In 6 individuals, RNA-seq enabled the reclassification of variants of uncertain significance as likely pathogenic. Although RNU4ATAC variants are generally covered by clinical exomes, they are often overlooked in analysis because of their noncoding nature. CONCLUSION: This study highlights the variability of phenotypes and genotypes associated with RNU4ATAC-opathy. Laboratories should ensure RNU4ATAC and other noncoding genes are appropriately assessed by their analysis pipelines.

Lowry-Wood syndrome↗
PubMed2024

WilsonGenAI a deep learning approach to classify pathogenic variants in Wilson Disease.

BACKGROUND: Advances in Next Generation Sequencing have made rapid variant discovery and detection widely accessible. To facilitate a better understanding of the nature of these variants, American College of Medical Genetics and Genomics and the Association of Molecular Pathologists (ACMG-AMP) have issued a set of guidelines for variant classification. However, given the vast number of variants associated with any disorder, it is impossible to manually apply these guidelines to all known variants. Machine learning methodologies offer a rapid way to classify large numbers of variants, as well as variants of uncertain significance as either pathogenic or benign. Here we classify ATP7B genetic variants by employing ML and AI algorithms trained on our well-annotated WilsonGen dataset. METHODS: We have trained and validated two algorithms: TabNet and XGBoost on a high-confidence dataset of manually annotated, ACMG & AMP classified variants of the ATP7B gene associated with Wilson's Disease. RESULTS: Using an independent validation dataset of ACMG & AMP classified variants, as well as a patient set of functionally validated variants, we showed how both algorithms perform and can be used to classify large numbers of variants in clinical as well as research settings. CONCLUSION: We have created a ready to deploy tool, that can classify variants linked with Wilson's disease as pathogenic or benign, which can be utilized by both clinicians and researchers to better understand the disease through the nature of genetic variants associated with it.

Hepatolenticular Degeneration↗
PubMed2026

'Truthsets' for clinical validation of large-scale functional assays: Practice recommendations from Cancer Variant Interpretation Group UK (CanVIG-UK).

BACKGROUND: Large-scale functional assays, including multiplex assays of variant effect, have substantial potential to resolve variants of uncertain significance (VUS), particularly for rare missense variants where clinical and population evidence are limited. The ClinGen assay-level clinical validation framework described by Brnich et al provided baseline guidance for the use of functional data for variant classification. However, clear consensus regarding construction of variant 'truthsets' by which to clinically validate functional data remains lacking. METHODS: CanVIG-UK developed consensus recommendations for truthset construction through an iterative national consultation process involving the CanVIG Steering Advisory Group (CStAG), wider CanVIG-UK membership, and engagement with international functional genomics experts. Consultation was based on previous analyses of 2,120 truthset constructions examining the impact of truthset composition on evidence point allocation within the ClinGen assay-level clinical validation framework. RESULTS: Across several consultations, CanVIG-UK established nine guiding principles and seven best-practice recommendations for assay-level clinical validation, using the assumed context of an assay for a cancer susceptibility gene where loss-of-function is the mechanism of pathogenicity. The principal recommendation stipulates, where assays are intended for use in interpretation of largely missense variants, the truthset used to validate should comprise only missense variants. Rather than mixtures of different variant types which may serve to over-estimate assay performance. Additional recommendations support option for relaxation of truthset stringency to improve power, augmentation of benign missense truthsets with systematically derived 'proxy-clinical' benign variants, independent clinical validation separate from assayist-defined validation, and careful evaluation of missense score distributions against that of protein-truncating and synonymous variants. Guidance is also provided for scenarios with limited pathogenic truthset availability and for assays reporting multiple deleterious zones or readouts. CONCLUSIONS: The CanVIG-UK principles and recommendations for truthset construction upon the ClinGen assay-level clinical validation framework, while aiming to form a baseline for future discussion regarding other functional and disease contexts and helping to address the gap between publication of new data and routine clinical implementation.

Journal Article↗