Routine RNA-based analysis of potential splicing variants facilitates genomic diagnostics and reveals limitations of in silico prediction tools

Abstract

DNA variants affecting pre-mRNA splicing are an important cause of genetic disorders and remain challenging to interpret without experimental data. Although variant classification guidelines recommend experimental characterization of variant splicing effects, the added value of routine diagnostic investigation of patient mRNA splicing has not been systematically described. Here, we assessed the utility of pre-mRNA splicing analysis in a diagnostic setting for 202 suspected splice-altering variants from individuals referred for genetic testing. Pre-mRNA splicing was assessed in patient cells by RT-PCR, followed by agarose gel electrophoresis and Sanger sequencing and/or exon trapping assays. An effect on pre-mRNA splicing was demonstrated in 63% (n = 128/202) of the tested variants. Among the 177 variants initially classified as variants of uncertain significance (VUS), 54% (n = 96/177) were reclassified based on pre-mRNA splicing analysis, including 48% (n = 85/177) that were upgraded to likely pathogenic or pathogenic. We benchmarked the splice prediction algorithms SpliceAI, SQUIRLS, SPiP, and Pangolin, the tools integrated in Alamut on this clinically relevant and experimentally validated dataset, and the CAGI6 splicing VUS dataset and found variable performance dependent on variant type and location. No single tool classified all variants equally well. We describe several examples of hard-to-predict effects and unexpected results highlighting the limitations of prediction tools, including a not previously described variant type affecting U12-splice site subtype. In summary, we provide a framework for RNA-based analysis in a molecular diagnostic setting, demonstrate the added value of routine testing of RNA from individuals with suspected splice-altering variants, and highlight the limitations of in silico prediction tools.

Keywords: RNA splicing; diagnostics; genetic disorders; splice prediction algorithms.