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
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
100299Informations de copyright
Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.