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A systematic assessment of the impact of rare canonical splice site variants on splicing using functional and in silico methods

Authors :
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
Source :
HGG Advances, Vol 5, Iss 3, Pp 100299- (2024)
Publication Year :
2024
Publisher :
Elsevier, 2024.

Abstract

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.

Details

Language :
English
ISSN :
26662477 and 10684336
Volume :
5
Issue :
3
Database :
Directory of Open Access Journals
Journal :
HGG Advances
Publication Type :
Academic Journal
Accession number :
edsdoj.106843364294daeb3b48f75146bc982
Document Type :
article
Full Text :
https://doi.org/10.1016/j.xhgg.2024.100299