New genetic diagnoses for inherited retinal dystrophies by integrating splicing tools into NGS pipelines.

Abstract

Variants affecting pre-mRNA splicing mechanisms are responsible for multiple monogenic disorders. However, their prioritization and interpretation remain challenging. Herein, we designed a strategy for the identification of likely spliceogenic variants in unsolved inherited retinal dystrophy (IRD) cases. We benchmarked thirteen splicing predictors on a curated training dataset, which revealed that the combination of SpliceAI and MaxEnt tools exhibited the best performance for the analysis of most splicing variants. However, for branch point variants, the BranchPoint tool (Alamut®-Batch) was the optimal choice. The proposed combination of tools was assessed using a validation cohort comprising 116 genetically diagnosed individuals with rare diseases, and subsequently applied for the analysis of 211 unsolved IRD families. The pipeline identified 30 likely pathogenic variants, 17 of which were predicted to alter splicing mechanisms. These results demonstrate an increase in diagnostic yield of up to 6.2%, reinforcing the importance of reanalysis strategies focused on identifying spliceogenic variants.