利用功能和硅学方法系统评估罕见典型剪接位点变异对剪接的影响。

IF 3.3 Q2 GENETICS & HEREDITY
HGG Advances Pub Date : 2024-07-18 Epub Date: 2024-04-24 DOI:10.1016/j.xhgg.2024.100299
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
{"title":"利用功能和硅学方法系统评估罕见典型剪接位点变异对剪接的影响。","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","doi":"10.1016/j.xhgg.2024.100299","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":34530,"journal":{"name":"HGG Advances","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11144818/pdf/","citationCount":"0","resultStr":"{\"title\":\"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\",\"doi\":\"10.1016/j.xhgg.2024.100299\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":34530,\"journal\":{\"name\":\"HGG Advances\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11144818/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"HGG Advances\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.xhgg.2024.100299\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/4/24 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"HGG Advances","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.xhgg.2024.100299","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/4/24 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0

摘要

背景/目的:典型剪接位点变异(CSSVs)通常被认为会导致功能缺失(LoF),并被认为具有非常强的致病性证据(根据 ACMG 标准 PVS1)。方法:通过对 112 个个体进行基因组测序,确定了未选择的血液表达基因中的 168 个罕见 CSSV,并在 RNA 测序(RNA-seq)数据中人工检测了它们对剪接的影响。在对这些 RNA-seq 数据视而不见的情况下,我们尝试通过应用硅学工具和 ClinGen 序列变异解释工作组 2018 年指南中的 PVS1 标准来预测 CSSVs 的精确影响:结果:25.6%的CSSVs既没有证据表明存在框移位,也没有证据表明存在与无义介导的衰变一致的表达降低:17.9%的CSSVs仅有野生型剪接和正常的连接深度,3.6%的CSSVs导致了隐性剪接位点的使用和框内嵌合,3.6%的CSSVs导致了全外显子跳越(框内),0.6%的CSSVs导致了全内含子包含(框内)。使用(i) SpliceAI、(ii) MaxEntScan和(iii) AutoPVS1(一种利用Ensembl变异效应预测器和MaxEntScan对无效变异进行PVS1解读的自动分类工具)预测的对剪接的影响分别与65%、63%和61%的CSSVs的RNA-seq分析结果一致:结论:根据 RNA-seq 数据分析,约四分之一的罕见 CSSV 可能不会导致 LoF。硅学方法的预测结果往往与 RNA-seq 的结果不一致。在缺乏功能数据的情况下,将 PVS1 级别的证据应用于 CSSV 时可能需要更加谨慎。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A systematic assessment of the impact of rare canonical splice site variants on splicing using functional and in silico methods.

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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
HGG Advances
HGG Advances Biochemistry, Genetics and Molecular Biology-Molecular Medicine
CiteScore
4.30
自引率
4.50%
发文量
69
审稿时长
14 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信