Blood RNA analysis can increase clinical diagnostic rate and resolve variants of uncertain significance

Purpose: Diagnosis of genetic disorders is hampered by large numbers ofvariants of uncertain significance (VUSs) identified through next-generationsequencing. Many such variants may disrupt normal RNA splicing. We examinedeffects on splicing of a large cohort of clinically identified variants andcompared performance of bioinformatic splicing prediction tools commonly used indiagnostic laboratories. Methods: Two hundred fifty-seven variants (coding and noncoding) werereferred for analysis across three laboratories. Blood RNA samples underwenttargeted reverse transcription polymerase chain reaction (RT-PCR) analysis withSanger sequencing of PCR products and agarose gel electrophoresis. Seventeensamples also underwent transcriptome-wide RNA sequencing with targeted splicinganalysis based on Sashimi plot visualization. Bioinformatic splicing predictionswere obtained using Alamut, HSF 3.1, and SpliceAI software. Results: Eighty-five variants (33%) were associated with abnormal splicing.The most frequent abnormality was upstream exon skipping (39/85 variants), whichwas most often associated with splice donor region variants. SpliceAI hadgreatest accuracy in predicting splicing abnormalities (0.91) and outperformedother tools in sensitivity and specificity. Conclusion: Splicing analysis of blood RNA identifies diagnostically importantsplicing abnormalities and clarifies functional effects of a significantproportion of VUSs. Bioinformatic predictions are improving but still makesignificant errors. RNA analysis should therefore be routinely considered ingenetic disease diagnostics.