Accelerate the discovery of genetic variants in mitochondrial diseases with Variant prIOritization using Latent spAce

Interpreting variants from whole-exome sequencing remains a major challenge, particularly for heterogeneous disorders such as mitochondrial diseases (MDs). To address this, we have developed Variant prIoritizatiOn using Latent spAce (VIOLA), a pipeline designed to help find a diagnosis for complex cases. VIOLA uses a variational autoencoder to embed functional annotations into a low-dimensional space, followed by DBSCAN-based outlier detection to identify potential pathogenic variants. Filtering steps and phenotype integration via HPO terms are then applied. The VIOLA score (Vscore) combines variant outlierness, transcriptomic co-expression data, and MD-specific annotations. Two rankings are derived: the VIOLA rank (all variants) and the ARrank (variants compatible with autosomal recessive inheritance). The VIOLA Aggregated score (VAscore) merges Vscore with Exomiser's pathogenicity score. Applied to 20 patients (four diagnosed), VIOLA reduced the variant list by >99% and ranked causal variants within the top 5 using ARrank, outperforming existing methods. Overall, VIOLA is a patient-specific strategy for variant prioritization, helping to resolve challenging MD cases and uncover novel disease mechanisms.