Abstract
PurposeDiagnosis of genetic disorders is hampered by large numbers of variants of uncertain significance (VUSs) identified through next-generation sequencing. Many such variants may disrupt normal RNA splicing. We examined effects on splicing of a large cohort of clinically identified variants and compared performance of bioinformatic splicing prediction tools commonly used in diagnostic laboratories. MethodsTwo hundred fifty-seven variants (coding and noncoding) were referred for analysis across three laboratories. Blood RNA samples underwent targeted reverse transcription polymerase chain reaction (RT-PCR) analysis with Sanger sequencing of PCR products and agarose gel electrophoresis. Seventeen samples also underwent transcriptome-wide RNA sequencing with targeted splicing analysis based on Sashimi plot visualization. Bioinformatic splicing predictions were obtained using Alamut, HSF 3.1, and SpliceAI software. ResultsEighty-five variants (33%) were associated with abnormal splicing. The most frequent abnormality was upstream exon skipping (39/85 variants), which was most often associated with splice donor region variants. SpliceAI had greatest accuracy in predicting splicing abnormalities (0.91) and outperformed other tools in sensitivity and specificity. ConclusionSplicing analysis of blood RNA identifies diagnostically important splicing abnormalities and clarifies functional effects of a significant proportion of VUSs. Bioinformatic predictions are improving but still make significant errors. RNA analysis should therefore be routinely considered in genetic disease diagnostics.
Highlights
Use of next-generation sequencing (NGS) technologies in clinical practice has led to an unprecedented increase in the number of variants being identified in patients undergoing investigation for genetic disorders
RNA analysis should be routinely considered in genetic disease diagnostics
Variant of uncertain significance (VUS) reporting rates vary over time and depending on local reporting policies but of all variants listed on ClinVar, 48% are asserted to be of uncertain significance (Figure S1).[1]
Summary
Use of next-generation sequencing (NGS) technologies in clinical practice has led to an unprecedented increase in the number of variants being identified in patients undergoing investigation for genetic disorders. Variant of uncertain significance (VUS) reporting rates vary over time and depending on local reporting policies but of all variants listed on ClinVar (as of 13 November 2019), 48% are asserted to be of uncertain significance (Figure S1).[1] In a clinical setting, this uncertainty has major implications for patients and their families, where having a clear genetic diagnosis can allow evidence-based management decisions to be taken and informed reproductive choices to be made.[2,3]. Deep intronic variant data are increasingly available via NGS approaches like genome sequencing, such noncoding variants are rarely considered owing to a lack of evidence on which to base interpretations.
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