medRxivGenetic & Genomic MedicinePreprint
SwissGenVar: A platform for clinical grade interpretation of genetic variants to foster personalized health care in Switzerland
Dennis KraemeriD, Dillenn Terumalai, Maria Livia FamigliettiiD, Isabel FilgesiD, Pascal JosetiD, Samuel KolleriD, Fabienne Maurer, Stéphanie MeieriD, Thierry P. NouspikeliD, Javier SanziD, Christiane ZweieriD, Marc Joel AbramowicziD, Wolfgang BergeriD, Sven CichoniD, André SchalleriD, Andrea Superti‐FurgaiD, Valérie BarbiéiD, Anita M. RauchiD
Abstract
Large-scale next-generation sequencing (NGS) germline testing is technically feasible today, but variant interpretation represents a major bottleneck in analysis workflows including the extensive variant prioritization, annotation, and time-consuming evidence curation. The scale of the interpretation problem is massive, and variants of uncertain significance (VUS) are a challenge to personalized medicine. This challenge is further compounded by the complexity and heterogeneity of standards used to describe genetic variants and associated phenotypes when searching for relevant information to inform clinical decision-making. For this purpose, all five Swiss academic Medical Genetics Institutions joined forces with the Swiss Institute of Bioinformatics (SIB) to create SwissGenVar as a user-friendly nationwide repository and sharing platform for genetic variant data generated during routine diagnostic procedures and research sequencing projects. Its objective is to provide a protected environment for expert evidence sharing about individual variants to harmonize and up-scale their significance interpretation at clinical grade following international standards. To corroborate the clinical assessment, the variant-related data are combined with consented high-quality clinical information. Broader visibility will be gained by interfacing with international databases, thus supporting global initiatives in personalized health care.
- organism: human
- assay: whole-exome sequencing
- research focus: variant effect prediction
- research focus: database / resource
- approach: benchmark study
- Citations
- 0
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