Comparison of in silico strategies to prioritize rare genomic variants impacting RNA splicing for the diagnosis of genomic disorders

The development of computational methods to assess pathogenicity of pre-messenger RNA splicing variants is critical for diagnosis of human disease. We assessed the capability of eight algorithms, and a consensus approach, to prioritize 249 variants of uncertain significance (VUSs) that underwent splicing functional analyses. The capability of algorithms to differentiate VUSs away from the immediate splice site as being 'pathogenic' or 'benign' is likely to have substantial impact on diagnostic testing. We show that SpliceAI is the best single strategy in this regard, but that combined usage of tools using a weighted approach can increase accuracy further. We incorporated prioritization strategies alongside diagnostic testing for rare disorders. We show that 15% of 2783 referred individuals carry rare variants expected to impact splicing that were not initially identified as `pathogenic' or ` likely pathogenic'; one in five of these cases could lead to new or refined diagnoses.

Automated summary

The development of computational methods for evaluating pathogenic variants is crucial for diagnosis of human diseases. Evaluation of eight algorithms and a consensus approach showed that SpliceAI is the best single strategy for prioritizing VUSs, with a combination of tools using weighted approach increasing accuracy. Incorporating prioritization strategies in diagnostic testing for rare disorders can lead to new or refined diagnoses, with rare variants impacting splicing potentially being missed initially.