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A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project

BACKGROUND: Genomic variants which disrupt splicing are a major cause of rare genetic diseases. However, variants which lie outside of the canonical splice sites are difficult to interpret clinically. Improving the clinical interpretation of non-canonical splicing variants offers a major opportunity...

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Autores principales: Blakes, Alexander J. M., Wai, Htoo A., Davies, Ian, Moledina, Hassan E., Ruiz, April, Thomas, Tessy, Bunyan, David, Thomas, N. Simon, Burren, Christine P., Greenhalgh, Lynn, Lees, Melissa, Pichini, Amanda, Smithson, Sarah F., Taylor Tavares, Ana Lisa, O’Donovan, Peter, Douglas, Andrew G. L., Whiffin, Nicola, Baralle, Diana, Lord, Jenny
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9327385/
https://www.ncbi.nlm.nih.gov/pubmed/35883178
http://dx.doi.org/10.1186/s13073-022-01087-x
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author Blakes, Alexander J. M.
Wai, Htoo A.
Davies, Ian
Moledina, Hassan E.
Ruiz, April
Thomas, Tessy
Bunyan, David
Thomas, N. Simon
Burren, Christine P.
Greenhalgh, Lynn
Lees, Melissa
Pichini, Amanda
Smithson, Sarah F.
Taylor Tavares, Ana Lisa
O’Donovan, Peter
Douglas, Andrew G. L.
Whiffin, Nicola
Baralle, Diana
Lord, Jenny
author_facet Blakes, Alexander J. M.
Wai, Htoo A.
Davies, Ian
Moledina, Hassan E.
Ruiz, April
Thomas, Tessy
Bunyan, David
Thomas, N. Simon
Burren, Christine P.
Greenhalgh, Lynn
Lees, Melissa
Pichini, Amanda
Smithson, Sarah F.
Taylor Tavares, Ana Lisa
O’Donovan, Peter
Douglas, Andrew G. L.
Whiffin, Nicola
Baralle, Diana
Lord, Jenny
author_sort Blakes, Alexander J. M.
collection PubMed
description BACKGROUND: Genomic variants which disrupt splicing are a major cause of rare genetic diseases. However, variants which lie outside of the canonical splice sites are difficult to interpret clinically. Improving the clinical interpretation of non-canonical splicing variants offers a major opportunity to uplift diagnostic yields from whole genome sequencing data. METHODS: Here, we examine the landscape of splicing variants in whole-genome sequencing data from 38,688 individuals in the 100,000 Genomes Project and assess the contribution of non-canonical splicing variants to rare genetic diseases. We use a variant-level constraint metric (the mutability-adjusted proportion of singletons) to identify constrained functional variant classes near exon–intron junctions and at putative splicing branchpoints. To identify new diagnoses for individuals with unsolved rare diseases in the 100,000 Genomes Project, we identified individuals with de novo single-nucleotide variants near exon–intron boundaries and at putative splicing branchpoints in known disease genes. We identified candidate diagnostic variants through manual phenotype matching and confirmed new molecular diagnoses through clinical variant interpretation and functional RNA studies. RESULTS: We show that near-splice positions and splicing branchpoints are highly constrained by purifying selection and harbour potentially damaging non-coding variants which are amenable to systematic analysis in sequencing data. From 258 de novo splicing variants in known rare disease genes, we identify 35 new likely diagnoses in probands with an unsolved rare disease. To date, we have confirmed a new diagnosis for six individuals, including four in whom RNA studies were performed. CONCLUSIONS: Overall, we demonstrate the clinical value of examining non-canonical splicing variants in individuals with unsolved rare diseases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-022-01087-x.
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spelling pubmed-93273852022-07-28 A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project Blakes, Alexander J. M. Wai, Htoo A. Davies, Ian Moledina, Hassan E. Ruiz, April Thomas, Tessy Bunyan, David Thomas, N. Simon Burren, Christine P. Greenhalgh, Lynn Lees, Melissa Pichini, Amanda Smithson, Sarah F. Taylor Tavares, Ana Lisa O’Donovan, Peter Douglas, Andrew G. L. Whiffin, Nicola Baralle, Diana Lord, Jenny Genome Med Research BACKGROUND: Genomic variants which disrupt splicing are a major cause of rare genetic diseases. However, variants which lie outside of the canonical splice sites are difficult to interpret clinically. Improving the clinical interpretation of non-canonical splicing variants offers a major opportunity to uplift diagnostic yields from whole genome sequencing data. METHODS: Here, we examine the landscape of splicing variants in whole-genome sequencing data from 38,688 individuals in the 100,000 Genomes Project and assess the contribution of non-canonical splicing variants to rare genetic diseases. We use a variant-level constraint metric (the mutability-adjusted proportion of singletons) to identify constrained functional variant classes near exon–intron junctions and at putative splicing branchpoints. To identify new diagnoses for individuals with unsolved rare diseases in the 100,000 Genomes Project, we identified individuals with de novo single-nucleotide variants near exon–intron boundaries and at putative splicing branchpoints in known disease genes. We identified candidate diagnostic variants through manual phenotype matching and confirmed new molecular diagnoses through clinical variant interpretation and functional RNA studies. RESULTS: We show that near-splice positions and splicing branchpoints are highly constrained by purifying selection and harbour potentially damaging non-coding variants which are amenable to systematic analysis in sequencing data. From 258 de novo splicing variants in known rare disease genes, we identify 35 new likely diagnoses in probands with an unsolved rare disease. To date, we have confirmed a new diagnosis for six individuals, including four in whom RNA studies were performed. CONCLUSIONS: Overall, we demonstrate the clinical value of examining non-canonical splicing variants in individuals with unsolved rare diseases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-022-01087-x. BioMed Central 2022-07-26 /pmc/articles/PMC9327385/ /pubmed/35883178 http://dx.doi.org/10.1186/s13073-022-01087-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Blakes, Alexander J. M.
Wai, Htoo A.
Davies, Ian
Moledina, Hassan E.
Ruiz, April
Thomas, Tessy
Bunyan, David
Thomas, N. Simon
Burren, Christine P.
Greenhalgh, Lynn
Lees, Melissa
Pichini, Amanda
Smithson, Sarah F.
Taylor Tavares, Ana Lisa
O’Donovan, Peter
Douglas, Andrew G. L.
Whiffin, Nicola
Baralle, Diana
Lord, Jenny
A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project
title A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project
title_full A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project
title_fullStr A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project
title_full_unstemmed A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project
title_short A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project
title_sort systematic analysis of splicing variants identifies new diagnoses in the 100,000 genomes project
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9327385/
https://www.ncbi.nlm.nih.gov/pubmed/35883178
http://dx.doi.org/10.1186/s13073-022-01087-x
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