Assessing predictions of the impact of variants on splicing in CAGI5

Mount, Stephen M.; Avsec, Žiga; Carmel, Liran; Casadio, Rita; Çelik, Muhammed Hasan; Chen, Ken; Cheng, Jianghua; Cohen, Noa; Fairbrother, William G.; Fenesh, Tzila; Gotea, Valer; Holzer, Tamar; Lin, Chiao‐Feng; Martelli, Pier Luigi; Naito, Tatsuhiko; Nguyen, Thi Yen Duong; Savojardo, Castrense; Unger, Ron; Wang, Robert; Yang, Yuedong; Zhao, Huiying

doi:10.1002/humu.23869

Cited by 19 publications

(16 citation statements)

References 35 publications

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“…Most of these (~99%) employ the canonical 5′ splice site GT dinucleotide whereas a minority (~1%) use the noncanonical 5′ splice site GC dinucleotide (Abril, Castelo, & Guigo, 2005; Burset, Seledtsov, & Solovyev, 2000, 2001; Parada, Munita, Cerda, & Gysling, 2014; Sheth et al, 2006). 5′ splice site GT>GC (or +2T>C) variants have been frequently described as causing human genetic disease (Stenson et al, 2017) and are routinely scored as splicing mutations (Mount et al, 2019). However, we have recently provided evidence that such variants in human disease genes may not invariably be pathogenic.…”

Section: Gene Symbol Chr Hg38 Position Reference Allele Variant Allementioning

confidence: 99%

5′ splice site GC>GT and GT>GC variants differ markedly in terms of their functionality and pathogenicity

et al. 2020

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In the human genome, most 5′ splice sites (~99%) employ the canonical GT dinucleotide whereas a small minority (~1%) use the noncanonical GC dinucleotide. The functionality and pathogenicity of 5′ splice site GT>GC (+2T>C) variants have been extensively studied but we know very little about 5′ splice site GC>GT (+2C>T) variants. Herein, we have addressed this deficiency by performing a meta‐analysis of reported +2C>T “pathogenic” variants together with a functional analysis of engineered +2C>T substitutions using a cell culture‐based full‐length gene splicing assay. Our results establish proof of concept that +2C>T variants are qualitatively different from +2T>C variants in terms of their functionality and suggest that, in sharp contrast to +2T>C variants, most if not all +2C>T variants have no pathological relevance. Our findings have important implications for interpreting the clinical relevance of +2C>T variants and understanding the evolutionary switching between GT and GC 5′ splice sites in mammalian genomes.

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Section: Gene Symbol Chr Hg38 Position Reference Allele Variant Allementioning

confidence: 99%

5′ splice site GC>GT and GT>GC variants differ markedly in terms of their functionality and pathogenicity

et al. 2020

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“…Even for variants that occur within the supposedly obligate splice--site dinucleotides, we may still encounter problems of interpretation. For example, variants affecting the 5' splice site GT dinucleotide, which have been frequently reported to cause human genetic disease [13], are routinely scored as pathogenic splicing mutations and are usually considered to be fully penetrant [14,15]. However, we have recently provided evidence to suggest that 5' splice site GT>GC variants (henceforth simply termed GT>GC variants or alternatively +2T>C variants) in human disease genes may not invariably be pathogenic [16].…”

Section: Introductionmentioning

confidence: 99%

The Experimentally Obtained Functional Impact Assessments of 5' Splice Site GT>GC Variants Differ Markedly from Those Predicted

Chen

Lin

Masson

et al. 2020

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Introduction: 5' splice site GT>GC or +2T>C variants have been frequently reported to cause human genetic disease and are routinely scored as pathogenic splicing mutations. However, we have recently demonstrated that such variants in human disease genes may not invariably be pathogenic. Moreover, we found that no splicing prediction tools appear to be capable of reliably distinguishing those +2T>C variants that generate wild-type transcripts from those that do not. Methodology: Herein, we evaluated the performance of a novel deep learning-based tool, SpliceAI, in the context of three datasets of +2T>C variants, all of which had been characterized functionally in terms of their impact on pre-mRNA splicing. The first two datasets refer to our recently described “in vivo” dataset of 45 known disease-causing +2T>C variants and the “in vitro” dataset of 103 +2T>C substitutions subjected to full-length gene splicing assay. The third dataset comprised 12 BRCA1 +2T>C variants that were recently analyzed by saturation genome editing. Results: Comparison of the SpliceAI-predicted and experimentally obtained functional impact assessments of these variants (and smaller datasets of +2T>A and +2T>G variants) revealed that although SpliceAI performed rather better than other prediction tools, it was still far from perfect. A key issue was that the impact of those +2T>C (and +2T>A) variants that generated wild-type transcripts represents a quantitative change that can vary from barely detectable to an almost full expression of wild-type transcripts, with wild-type transcripts often co-existing with aberrantly spliced transcripts. Conclusion: Our findings highlight the challenges that we still face in attempting to accurately identify splice-altering variants.

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“…Previously, CAGI has had just one small splicing challenge [https://genomeinterpretation.org/content/Splicing-2012]. CAGI5 included two full‐scale splicing challenges (Mount et al, ) and these have resulted in five papers from participants (Chen, Lu, Zhao, & Yang, ; Cheng, Çelik, Nguyen, Avsec, & Gagneur, ; Gotea, Margolin, & Elnitski, ; Naito, ; Wang, Wang, & Hu, ). The issue also contains an overview paper from one of the splicing data providers (Rhine et al, ).…”

Section: Introductionmentioning

confidence: 99%

Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation

et al. 2019

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Interpretation of genomic variation plays an essential role in the analysis of cancer and monogenic disease, and increasingly also in complex trait disease, with applications ranging from basic research to clinical decisions. Many computational impact prediction methods have been developed, yet the field lacks a clear consensus on their appropriate use and interpretation. The Critical Assessment of Genome Interpretation (CAGI, /'kā‐jē/) is a community experiment to objectively assess computational methods for predicting the phenotypic impacts of genomic variation. CAGI participants are provided genetic variants and make blind predictions of resulting phenotype. Independent assessors evaluate the predictions by comparing with experimental and clinical data. CAGI has completed five editions with the goals of establishing the state of art in genome interpretation and of encouraging new methodological developments. This special issue (https://onlinelibrary.wiley.com/toc/10981004/2019/40/9) comprises reports from CAGI, focusing on the fifth edition that culminated in a conference that took place 5 to 7 July 2018. CAGI5 was comprised of 14 challenges and engaged hundreds of participants from a dozen countries. This edition had a notable increase in splicing and expression regulatory variant challenges, while also continuing challenges on clinical genomics, as well as complex disease datasets and missense variants in diseases ranging from cancer to Pompe disease to schizophrenia. Full information about CAGI is at https://genomeinterpretation.org.

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Assessing predictions of the impact of variants on splicing in CAGI5

Cited by 19 publications

References 35 publications

5′ splice site GC>GT and GT>GC variants differ markedly in terms of their functionality and pathogenicity

5′ splice site GC>GT and GT>GC variants differ markedly in terms of their functionality and pathogenicity

The Experimentally Obtained Functional Impact Assessments of 5' Splice Site GT>GC Variants Differ Markedly from Those Predicted

Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation

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