Preannouncement: ERDERA 2026 - Resolving unsolved cases in rare genetic and non-genetic diseases through variant validation and new technological approaches

The second ERDERA call aims at facing the challenges of diagnosis in rare diseases. Accurately diagnosing RDs is a major challenge, with approximately 50% of individuals with a suspected rare genetic condition remaining undiagnosed or misdiagnosed despite standard clinical genetics care. In addition, RDs of non-genetic origin - estimated to account for about 10% of all RD cases - remain an under-investigated area. On average, it takes around 5 years to establish an accurate diagnosis for people living with a RD (PLWRD). Given the complexity of these disorders, multiple and complementary diagnostic approaches are required. This complexity underpins the objectives of this call.

The goal of this call is to solve Undiagnosed Rare Genetic diseases and to address complex, multifactorial Rare Non-Genetic diseases by identifying causative variants in patients with no molecular diagnosis after prior genetic or genomic testing and providing diagnostic clarity for conditions of unknown or mixed pathogenesis.

Suggested focus areas:

• Functional validation to classify variants of uncertain significance (VUS) and increase the diversity of functional genomics research, or validation of candidate VUS to improve outcomes for a broader range of patients using in silico, in vitro or animal model systems (e.g. CRISPR modified cells, iPSCs, organoids, etc.);
• Use of multi-omics or integrative methods (e.g. transcriptomics, epigenomics, etc.) to resolve ambiguous or complex variants;
• New tools/methodologies not yet validated in clinical settings, including biostatistics, advanced bioinformatics, and mathematics approaches (e.g. variant effect predictors, Artificial Intelligence (AI)-based annotation platforms, etc.);
• Systems biology and disease mechanism modelling;
• Integration of clinical, environmental, lifestyle, and sensor-derived data;
• Development of knowledge graphs or disease maps to link phenotypic and mechanistic insights;
• Use of advanced AI and modelling tools (graph ML, probabilistic causal models).