50% of rare disease patients remain undiagnosed. enGenome searches for answers beyond the “one-gene, one-disease paradigm”.
Our AI approach can assess the pathogenicity of digenic variant combinations with respect to a patient's specific phenotypes.
By taking into consideration the additive effect of variant combinations and genetic modifiers we aim to explain the missing heritability and variability of phenotypic outcomes for hundreds of undiagnosed rare diseases.
Our new technology applies a sophisticated artificial intelligence algorithm that can identify combinations of genomic variants among millions of possibilities, rather than considering one variant at a time.
The tool, aptly named DIVAs, is able to evaluate the pathogenicity of combinations of variants present on two different genes, thus supporting pure digenic, modifiers-mediated or dual diagnosis hypothesis.
DIVAs is a machine-learning (ML) model exploiting different features to describe each mutated digenic combination capturing gene-gene interaction, single variant impact, a priori genes properties and genes association to HPO-based phenotypes. Family analysis can also be performed.
The project kicked off in 2021 and after the requests for collaborators opened at ESHG and ASHG, more than 1000 samples have been gathered from molecular testing laboratories worldwide to beta test the software.
enGenome has been selected for funding by the European Innovation Council (EIC) Accelerator. The project has overcome a highly competitive selection process (success rate below 3%) to win the approval of the European Commission.
This recognition is much more than just an important financial injection: "The feedback received from the many experts involved in the selection process validates the mission of enGenome and the continued development of our unique products and services”
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