A systematic assessment of the impact of rare canonical splice site variants on splicing using functional and in silico methods
| Autor: | Rachel Y. Oh, Ali AlMail, David Cheerie, George Guirguis, Huayun Hou, Kyoko E. Yuki, Bushra Haque, Bhooma Thiruvahindrapuram, Christian R. Marshall, Roberto Mendoza-Londono, Adam Shlien, Lianna G. Kyriakopoulou, Susan Walker, James J. Dowling, Michael D. Wilson, Gregory Costain |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | HGG Advances, Vol 5, Iss 3, Pp 100299- (2024) |
| Druh dokumentu: | article |
| ISSN: | 2666-2477 10684336 |
| DOI: | 10.1016/j.xhgg.2024.100299 |
| Popis: | Summary: Canonical splice site variants (CSSVs) are often presumed to cause loss-of-function (LoF) and are assigned very strong evidence of pathogenicity (according to American College of Medical Genetics/Association for Molecular Pathology criterion PVS1). The exact nature and predictability of splicing effects of unselected rare CSSVs in blood-expressed genes are poorly understood. We identified 168 rare CSSVs in blood-expressed genes in 112 individuals using genome sequencing, and studied their impact on splicing using RNA sequencing (RNA-seq). There was no evidence of a frameshift, nor of reduced expression consistent with nonsense-mediated decay, for 25.6% of CSSVs: 17.9% had wildtype splicing only and normal junction depths, 3.6% resulted in cryptic splice site usage and in-frame insertions or deletions, 3.6% resulted in full exon skipping (in frame), and 0.6% resulted in full intron inclusion (in frame). Blind to these RNA-seq data, we attempted to predict the precise impact of CSSVs by applying in silico tools and the ClinGen Sequence Variant Interpretation Working Group 2018 guidelines for applying PVS1 criterion. The predicted impact on splicing using (1) SpliceAI, (2) MaxEntScan, and (3) AutoPVS1, an automatic classification tool for PVS1 interpretation of null variants that utilizes Ensembl Variant Effect Predictor and MaxEntScan, was concordant with RNA-seq analyses for 65%, 63%, and 61% of CSSVs, respectively. In summary, approximately one in four rare CSSVs did not show evidence for LoF based on analysis of RNA-seq data. Predictions from in silico methods were often discordant with findings from RNA-seq. More caution may be warranted in applying PVS1-level evidence to CSSVs in the absence of functional data. |
| Databáze: | Directory of Open Access Journals |
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