Ivo F. A. C. Fokkema

Background: The Human Genome Variation Society (HGVS) Nomenclature is the global standard for describing and communicating variants in DNA, RNA, and protein sequences in clinical and research genomics. This manuscript details recent updates to the HGVS Nomenclature, highlighting improvements in governance, community engagement, website functionality, and underlying implementation of the standard. Methods: The HGVS Variant Nomenclature Committee (HVNC) now operates under the Human Genome Organization (HUGO), facilitating broader community feedback and collaboration with related standards organizations. The website has been redesigned using modern documentation tools and practices. The specification was updated to include guidance for transcript selection and to align with recent cross-consortia recommendations for the representation of gene fusions. A formal computational grammar was introduced to improve the precision and consistency of variant descriptions. Results: Major improvements in HGVS Nomenclature v. 21.1 include a redesigned website with enhanced navigation, search functionality, and mobile responsiveness; a new versioning policy aligned with software management practices; formal mechanisms for community feedback and change proposals; and adoption of Extended Backus-Naur Form (EBNF) for defining syntax. The specification now recommends MANE Select transcripts where appropriate and includes updated guidance for representing adjoined transcripts and gene fusions. All content is freely available under permissive licenses at hgvs-nomenclature.org. Conclusions: These advancements establish a more sustainable foundation for maintaining and evolving the HGVS Nomenclature while improving its accessibility and utility. The introduction of formal computational grammar marks a crucial step toward unambiguous variant descriptions that can be reliably processed by both humans and machines. Combined with enhanced community engagement mechanisms and improved guidance, these changes position the HGVS Nomenclature to better serve the evolving needs of clinical and research genomics while maintaining the stability that users require.

Gene variant databases are the backbone of DNA-based diagnostics. These databases, also called Locus-Specific DataBases (LSDBs), store information on variants in the human genome and the observed phenotypic consequences. The largest collection of public databases uses the free, open-source LOVD software platform. To cope with the current demand for online databases, we have entirely redesigned the LOVD software. LOVD3 is genome-centered and can be used to store summary variant data, as well as full case-level data with information on individuals, phenotypes, screenings, and variants. While built on a standard core, the software is highly flexible and allows personalization to cope with the largely different demands of gene/disease database curators. LOVD3 follows current standards and includes tools to check variant descriptions, generate HTML files of reference sequences, predict the consequences of exon deletions/duplications on the reading frame, and link to genomic views in the different genomes browsers. It includes APIs to collect and submit data. The software is used by about 100 databases, of which 56 public LOVD instances are registered on our website and together contain 1,000,000,000 variant observations in 1,500,000 individuals. 42 LOVD instances share data with the federated LOVD data network containing 3,000,000 unique variants in 23,000 genes. This network can be queried directly, quickly identifying LOVD instances containing relevant information on a searched variant.

Each year diagnostic laboratories in the Netherlands profile thousands of individuals for heritable disease using next‐generation sequencing (NGS). This requires pathogenicity classification of millions of DNA variants on the standard 5‐tier scale. To reduce time spent on data interpretation and increase data quality and reliability, the nine Dutch labs decided to publicly share their classifications. Variant classifications of nearly 100,000 unique variants were catalogued and compared in a centralized MOLGENIS database. Variants classified by more than one center were labeled as “consensus” when classifications agreed, and shared internationally with LOVD and ClinVar. When classifications opposed (LB/B vs. LP/P), they were labeled “conflicting”, while other nonconsensus observations were labeled “no consensus”. We assessed our classifications using the InterVar software to compare to ACMG 2015 guidelines, showing 99.7% overall consistency with only 0.3% discrepancies. Differences in classifications between Dutch labs or between Dutch labs and ACMG were mainly present in genes with low penetrance or for late onset disorders and highlight limitations of the current 5‐tier classification system. The data sharing boosted the quality of DNA diagnostics in Dutch labs, an initiative we hope will be followed internationally. Recently, a positive match with a case from outside our consortium resulted in a more definite disease diagnosis.

Background: Sharing of data about variation and the associated phenotypes is a critical need, yet variant information can be arbitrarily complex, making a single standard vocabulary elusive and re-formatting difficult. Complex standards have proven too time-consuming to implement. Results: The GEN2PHEN project addressed these difficulties by developing a comprehensive data model for capturing biomedical observations, Observ-OM, and building the VarioML format around it. VarioML pairs a simplified open specification for describing variants, with a toolkit for adapting the specification into one's own research workflow. Straightforward variant data can be captured, federated, and exchanged with no overhead; more complex data can be described, without loss of compatibility. The open specification enables push-button submission to gene variant databases (LSDBs) e.g., the Leiden Open Variation Database, using the Cafe Variome data publishing service, while VarioML bidirectionally transforms data between XML and web-application code formats, opening up new possibilities for open source web applications building on shared data. A Java implementation toolkit makes VarioML easily integrated into biomedical applications. VarioML is designed primarily for LSDB data submission and transfer scenarios, but can also be used as a standard variation data format for JSON and XML document databases and user interface components. Conclusions: VarioML is a set of tools and practices improving the availability, quality, and comprehensibility of human variation information. It enables researchers, diagnostic laboratories, and clinics to share that information with ease, clarity, and without ambiguity.

Genitopatellar syndrome (GPS) and Say–Barber–Biesecker–Young–Simpson syndrome (SBBYSS or Ohdo syndrome) have both recently been shown to be caused by distinct mutations in the histone acetyltransferase KAT6B (a.k.a. MYST4/MORF). All variants are de novo dominant mutations that lead to protein truncation. Mutations leading to GPS occur in the proximal portion of the last exon and lead to the expression of a protein without a C‐terminal domain. Mutations leading to SBBYSS occur either throughout the gene, leading to nonsense‐mediated decay, or more distally in the last exon. Features present only in GPS are contractures, anomalies of the spine, ribs and pelvis, renal cysts, hydronephrosis, and agenesis of the corpus callosum. Features present only in SBBYSS include long thumbs and long great toes and lacrimal duct abnormalities. Several features occur in both, such as intellectual disability, congenital heart defects, and genital and patellar anomalies. We propose that haploinsufficiency or loss of a function mediated by the C‐terminal domain causes the common features, whereas gain‐of‐function activities would explain the features unique to GPS. Further molecular studies and the compilation of mutations in a database for genotype–phenotype correlations (www.LOVD.nl/KAT6B) might help tease out answers to these questions and understand the developmental programs dysregulated by the different truncations. Hum Mutat 33:1520–1525, 2012.

Locus‐Specific DataBases (LSDBs) store information on gene sequence variation associated with human phenotypes and are frequently used as a reference by researchers and clinicians. We developed the Leiden Open‐source Variation Database (LOVD) as a platform‐independent Web‐based LSDB‐in‐a‐Box package. LOVD was designed to be easy to set up and maintain and follows the Human Genome Variation Society (HGVS) recommendations. Here we describe LOVD v.2.0, which adds enhanced flexibility and functionality and has the capacity to store sequence variants in multiple genes per patient. To reduce redundancy, patient and sequence variant data are stored in separate tables. Tables are linked to generate connections between sequence variant data for each gene and every patient. The dynamic structure allows database managers to add custom columns. The database structure supports fast queries and allows storage of sequence variants from high‐throughput sequence analysis, as demonstrated by the X‐chromosomal Mental Retardation LOVD installation. LOVD contains measures to ensure database security from unauthorized access. Currently, the LOVD Website (http://www.LOVD.nl/) lists 71 public LOVD installations hosting 3,294 gene variant databases with 199,000 variants in 84,000 patients. To promote LSDB standardization and thereby database interoperability, we offer free server space and help to establish an LSDB on our Leiden server. Hum Mutat 32:1–7, 2011.