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


Journal

HGG advances
ISSN: 2666-2477
Titre abrégé: HGG Adv
Pays: United States
ID NLM: 101772885

Informations de publication

Date de publication:
23 Apr 2024
Historique:
received: 11 07 2023
revised: 18 04 2024
accepted: 18 04 2024
medline: 25 4 2024
pubmed: 25 4 2024
entrez: 25 4 2024
Statut: aheadofprint

Résumé

Canonical splice site variants (CSSVs) are often presumed to cause loss-of-function (LoF) and are assigned very strong evidence of pathogenicity (according to ACMG criterion PVS1). The exact nature and predictability of splicing effects of unselected rare CSSVs in blood-expressed genes is poorly understood. 168 rare CSSVs in unselected blood-expressed genes were identified by genome sequencing in 112 individuals, and their impact on splicing was interrogated manually in RNA sequencing (RNA-seq) data. 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. 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 indels, 3.6% resulted in full exon skipping (in-frame), and 0.6% resulted in full intron inclusion (in-frame). The predicted impact on splicing using (i) SpliceAI, (ii) MaxEntScan, and (iii) 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. Approximately 1 in 4 rare CSSVs may not cause 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.

Sections du résumé

BACKGROUND/OBJECTIVES OBJECTIVE
Canonical splice site variants (CSSVs) are often presumed to cause loss-of-function (LoF) and are assigned very strong evidence of pathogenicity (according to ACMG criterion PVS1). The exact nature and predictability of splicing effects of unselected rare CSSVs in blood-expressed genes is poorly understood.
METHODS METHODS
168 rare CSSVs in unselected blood-expressed genes were identified by genome sequencing in 112 individuals, and their impact on splicing was interrogated manually in RNA sequencing (RNA-seq) data. 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.
RESULTS RESULTS
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 indels, 3.6% resulted in full exon skipping (in-frame), and 0.6% resulted in full intron inclusion (in-frame). The predicted impact on splicing using (i) SpliceAI, (ii) MaxEntScan, and (iii) 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.
CONCLUSION CONCLUSIONS
Approximately 1 in 4 rare CSSVs may not cause 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.

Identifiants

pubmed: 38659227
pii: S2666-2477(24)00038-1
doi: 10.1016/j.xhgg.2024.100299
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

100299

Informations de copyright

Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.

Auteurs

Rachel Y Oh (RY)

Division of Clinical and Metabolic Genetics, Hospital for Sick Children, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada.

Ali Almail (A)

Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Program in Genetics and Genome Biology, SickKids Research Institute, Toronto, Canada.

David Cheerie (D)

Program in Genetics and Genome Biology, SickKids Research Institute, Toronto, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Canada.

George Guirguis (G)

Program in Genetics and Genome Biology, SickKids Research Institute, Toronto, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Canada.

Huayun Hou (H)

Program in Genetics and Genome Biology, SickKids Research Institute, Toronto, Canada.

Kyoko E Yuki (KE)

Program in Genetics and Genome Biology, SickKids Research Institute, Toronto, Canada; Division of Genome Diagnostics, Hospital for Sick Children, Toronto, Canada.

Bushra Haque (B)

Program in Genetics and Genome Biology, SickKids Research Institute, Toronto, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Canada.

Bhooma Thiruvahindrapuram (B)

The Centre for Applied Genomics, SickKids Research Institute, Toronto, Canada.

Christian R Marshall (CR)

Division of Genome Diagnostics, Hospital for Sick Children, Toronto, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada.

Roberto Mendoza-Londono (R)

Division of Clinical and Metabolic Genetics, Hospital for Sick Children, Toronto, Canada; Program in Genetics and Genome Biology, SickKids Research Institute, Toronto, Canada; Department of Paediatrics, University of Toronto, Toronto, Canada.

Adam Shlien (A)

Program in Genetics and Genome Biology, SickKids Research Institute, Toronto, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Canada; Division of Genome Diagnostics, Hospital for Sick Children, Toronto, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada.

Lianna G Kyriakopoulou (LG)

Division of Genome Diagnostics, Hospital for Sick Children, Toronto, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada.

Susan Walker (S)

The Centre for Applied Genomics, SickKids Research Institute, Toronto, Canada.

James J Dowling (JJ)

Program in Genetics and Genome Biology, SickKids Research Institute, Toronto, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Canada; Department of Paediatrics, University of Toronto, Toronto, Canada; Division of Neurology, Hospital for Sick Children, Toronto, Canada.

Michael D Wilson (MD)

Program in Genetics and Genome Biology, SickKids Research Institute, Toronto, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Canada.

Gregory Costain (G)

Division of Clinical and Metabolic Genetics, Hospital for Sick Children, Toronto, Canada; Program in Genetics and Genome Biology, SickKids Research Institute, Toronto, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Canada; Department of Paediatrics, University of Toronto, Toronto, Canada. Electronic address: gregory.costain@sickkids.ca.

Classifications MeSH