Each meeting introduces a focused topic in a simple, accessible way so everyone can contribute, whatever their background. We explore what the topic means, why it may matter in practice, and what it would take to make it work at national scale. The discussion is open and exploratory: members share their views, uncertainties, and experience, and this collective input is then shaped into scientific guidelines and priority areas for Switzerland. Your perspective is genuinely needed, wherever you sit in our shared network.
Meetings are 30-60 minutes. Simple background information will be shared ahead of time so that you can participate comfortably or simply listen in. Notes are taken but no recording is made.
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2026-01-05 17:00 Zürich CET (UTC+1) (Upcoming)Zoom meetingReview and complete the official guideline for the consensus evidence model (About). Health systems, research, and industry cannot exchange genomic results because each pipeline outputs something different; the SGA qualifying variant evidence standard (QV-ES) solves this with a shared national-scale rule spec and a minimal evidence layer that every pipeline can generate and every institution can verifiably trust.
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2025-09-01 17:00 Zürich CET (UTC+1) (Ended)Zoom meetingDraft SGA guidelines for variant interpretation and genomic decision infrastructure, including the full QV specification, clinical bioinformatics recommendations, GA4GH aligned evidence structures, a three pillar architecture, and practical examples of how these standards can be applied.
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2025-06-02 17:00 Zürich CET (UTC+1) (Ended)Zoom meetingCore scientific questions in variant interpretation, including population data, inheritance, and phenotype matching, with an introduction to the QV approach and to national genomic provenance and data flow, and how shared formats can benefit both clinical and commercial sectors.
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2025-03-03 17:00 Zürich CET (UTC+1) (Ended)Zoom meetingHow Switzerland can adopt shared national standards for genomics, with an introduction to treating variant interpretation as a data science and reproducibility problem rather than a purely manual clinical genetics task.
