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

Genomics England Research Consortium, Splicing and Disease Working Group

doi:10.1186/s13073-022-01087-x

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

Genomics England Research Consortium, Splicing and Disease Working Group

Research output: Contribution to journal › Article (Academic Journal) › peer-review

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Abstract

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.

Original language	English
Article number	79
Pages (from-to)	79
Journal	Genome Medicine
Volume	14
Issue number	1
DOIs	https://doi.org/10.1186/s13073-022-01087-x
Publication status	Published - 26 Jul 2022

Bibliographical note

Funding Information:
The authors thank all the participants and families involved in this research. We thank all clinicians and contributors who helped to assess potential splicing diagnoses, including Ellen Thomas, Jessica Radley, Rebecca Igbokwe, Suresh Vijay, Deirdre Cilliers, Evan Reid, Mick Parker, David Hunt, Rachel Keen, Ed Blair, Helen Firth, Peggy O’Driscoll, Chiara Marini Bettol, Monish Suri, John Barton, Angela Barnicoat, Sahar Mansour, Melody Redman, Kate Barr, Debbie Fuller, Meena Balasubramanian, Julia Rankin, Sian Ellard, Olga Tsoulaki, and Emma Kivuva. We acknowledge the NIHR Clinical Research Network (CRN) in recruiting the participants and the Musketeers Memorandum, as well as support from the NIHR UK Rare Genetic Disease Consortium. We thank Patrick J. Short for providing a curated set of de novo variants used in earlier iterations of these analyses. This research was made possible through access to the data and findings generated by the 100,000 Genomes Project. The 100,000 Genomes Project uses data provided by participants and collected by the National Health Service as part of their care and support. These analyses were performed using Python 3.7.6 and Pandas 1.2.1. The figures were generated in R 3.5.2 using RStudio 1.1.463.

Funding Information:
The 100,000 Genomes Project is managed by Genomics England Limited (a wholly owned company of the Department of Health and Social Care). The 100,000 Genomes Project is funded by the National Institute for Health Research and NHS England. The Wellcome Trust, Cancer Research UK, and the Medical Research Council have also funded the research infrastructure.

Funding Information:
The Baralle Lab is supported by the NIHR Research Professorship awarded to D.B. (RP-2016–07-011). Functional work was additionally supported by a Wessex Medical Research Innovation Grant awarded to J.L. NW is currently supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 220134/Z/20/Z) and funding from the Rosetrees Trust. AB was supported by funding from Health Education England.

Funding Information:
We acknowledge the NIHR Clinical Research Network (CRN) in recruiting the participants and the Musketeers Memorandum, as well as support from the NIHR UK Rare Genetic Disease Consortium. We thank Patrick J. Short for providing a curated set of de novo variants used in earlier iterations of these analyses.

Publisher Copyright:
© 2022, The Author(s).

Keywords

Exons
Humans
Introns
RNA
RNA Splicing
Rare Diseases/genetics

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10.1186/s13073-022-01087-x

s13073-022-01087-xFinal published version, 1.4 MBLicence: CC BY

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Additional file 3 of A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project
Blakes, A. J. M. (Creator), Wai, H. A. (Creator), Davies, I. (Creator), Moledina, H. E. (Creator), Ruiz, A. (Creator), Thomas, T. (Creator), Bunyan, D. (Creator), Thomas, N. S. (Creator), Burren, C. P. (Creator), Greenhalgh, L. (Creator), Lees, M. (Creator), Pichini, A. (Creator), Smithson, S. F. (Creator), Taylor Tavares, A. L. (Creator), O’Donovan, P. (Creator), Douglas, A. G. L. (Creator), Whiffin, N. (Creator), Baralle, D. (Creator) & Lord, J. (Creator), figshare, 27 Jul 2022
DOI: 10.6084/m9.figshare.20381700.v1, https://springernature.figshare.com/articles/dataset/Additional_file_3_of_A_systematic_analysis_of_splicing_variants_identifies_new_diagnoses_in_the_100_000_Genomes_Project/20381700/1
Dataset
Additional file 2 of A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project
Blakes, A. J. M. (Creator), Wai, H. A. (Creator), Davies, I. M. (Creator), Moledina, H. E. (Creator), Ruiz, A. (Creator), Thomas, T. (Creator), Bunyan, D. (Creator), Thomas, N. S. (Creator), Burren, C. P. (Creator), Greenhalgh, L. (Creator), Lees, M. (Creator), Pichini, A. (Creator), Smithson, S. F. (Creator), Taylor Tavares, A. L. (Creator), O’Donovan, P. (Creator), Douglas, A. G. L. (Creator), Whiffin, N. (Creator), Baralle, D. (Creator) & Lord, J. (Creator), figshare, 27 Jul 2022
DOI: 10.6084/m9.figshare.20381697.v1, https://springernature.figshare.com/articles/dataset/Additional_file_2_of_A_systematic_analysis_of_splicing_variants_identifies_new_diagnoses_in_the_100_000_Genomes_Project/20381697/1
Dataset
Additional file 4 of A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project
Blakes, A. J. M. (Creator), Wai, H. A. (Creator), Davies, I. (Creator), Moledina, H. E. (Creator), Ruiz, A. (Creator), Thomas, T. (Creator), Bunyan, D. (Creator), Thomas, N. S. (Creator), Burren, C. P. (Creator), Greenhalgh, L. (Creator), Lees, M. (Creator), Pichini, A. (Creator), Smithson, S. F. (Creator), Taylor Tavares, A. L. (Creator), O’Donovan, P. (Creator), Douglas, A. G. L. (Creator), Whiffin, N. (Creator), Baralle, D. (Creator) & Lord, J. (Creator), figshare, 27 Jul 2022
DOI: 10.6084/m9.figshare.20381703.v1, https://springernature.figshare.com/articles/dataset/Additional_file_4_of_A_systematic_analysis_of_splicing_variants_identifies_new_diagnoses_in_the_100_000_Genomes_Project/20381703/1
Dataset

Cite this

@article{0568f0f4b7bf4fb38bc16baa9a7ed462,

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

abstract = "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.",

keywords = "Exons, Humans, Introns, RNA, RNA Splicing, Rare Diseases/genetics",

author = "{Genomics England Research Consortium, Splicing and Disease Working Group} and Blakes, {Alexander J M} and Wai, {Htoo A} and Ian Davies and Moledina, {Hassan E} and April Ruiz and Tessy Thomas and David Bunyan and Thomas, {N Simon} and Burren, {Christine P} and Lynn Greenhalgh and Melissa Lees and Amanda Pichini and Smithson, {Sarah F} and {Taylor Tavares}, {Ana Lisa} and Peter O'Donovan and Douglas, {Andrew G L} and Nicola Whiffin and Diana Baralle and Jenny Lord",

note = "Funding Information: The authors thank all the participants and families involved in this research. We thank all clinicians and contributors who helped to assess potential splicing diagnoses, including Ellen Thomas, Jessica Radley, Rebecca Igbokwe, Suresh Vijay, Deirdre Cilliers, Evan Reid, Mick Parker, David Hunt, Rachel Keen, Ed Blair, Helen Firth, Peggy O{\textquoteright}Driscoll, Chiara Marini Bettol, Monish Suri, John Barton, Angela Barnicoat, Sahar Mansour, Melody Redman, Kate Barr, Debbie Fuller, Meena Balasubramanian, Julia Rankin, Sian Ellard, Olga Tsoulaki, and Emma Kivuva. We acknowledge the NIHR Clinical Research Network (CRN) in recruiting the participants and the Musketeers Memorandum, as well as support from the NIHR UK Rare Genetic Disease Consortium. We thank Patrick J. Short for providing a curated set of de novo variants used in earlier iterations of these analyses. This research was made possible through access to the data and findings generated by the 100,000 Genomes Project. The 100,000 Genomes Project uses data provided by participants and collected by the National Health Service as part of their care and support. These analyses were performed using Python 3.7.6 and Pandas 1.2.1. The figures were generated in R 3.5.2 using RStudio 1.1.463. Funding Information: The 100,000 Genomes Project is managed by Genomics England Limited (a wholly owned company of the Department of Health and Social Care). The 100,000 Genomes Project is funded by the National Institute for Health Research and NHS England. The Wellcome Trust, Cancer Research UK, and the Medical Research Council have also funded the research infrastructure. Funding Information: The Baralle Lab is supported by the NIHR Research Professorship awarded to D.B. (RP-2016–07-011). Functional work was additionally supported by a Wessex Medical Research Innovation Grant awarded to J.L. NW is currently supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 220134/Z/20/Z) and funding from the Rosetrees Trust. AB was supported by funding from Health Education England. Funding Information: We acknowledge the NIHR Clinical Research Network (CRN) in recruiting the participants and the Musketeers Memorandum, as well as support from the NIHR UK Rare Genetic Disease Consortium. We thank Patrick J. Short for providing a curated set of de novo variants used in earlier iterations of these analyses. Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

month = jul,

day = "26",

doi = "10.1186/s13073-022-01087-x",

language = "English",

volume = "14",

pages = "79",

journal = "Genome Medicine",

issn = "1756-994X",

publisher = "BioMed Central",

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TY - JOUR

T1 - A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project

AU - Genomics England Research Consortium, Splicing and Disease Working Group

AU - Blakes, Alexander J M

AU - Wai, Htoo A

AU - Davies, Ian

AU - Moledina, Hassan E

AU - Ruiz, April

AU - Thomas, Tessy

AU - Bunyan, David

AU - Thomas, N Simon

AU - Burren, Christine P

AU - Greenhalgh, Lynn

AU - Lees, Melissa

AU - Pichini, Amanda

AU - Smithson, Sarah F

AU - Taylor Tavares, Ana Lisa

AU - O'Donovan, Peter

AU - Douglas, Andrew G L

AU - Whiffin, Nicola

AU - Baralle, Diana

AU - Lord, Jenny

N1 - Funding Information: The authors thank all the participants and families involved in this research. We thank all clinicians and contributors who helped to assess potential splicing diagnoses, including Ellen Thomas, Jessica Radley, Rebecca Igbokwe, Suresh Vijay, Deirdre Cilliers, Evan Reid, Mick Parker, David Hunt, Rachel Keen, Ed Blair, Helen Firth, Peggy O’Driscoll, Chiara Marini Bettol, Monish Suri, John Barton, Angela Barnicoat, Sahar Mansour, Melody Redman, Kate Barr, Debbie Fuller, Meena Balasubramanian, Julia Rankin, Sian Ellard, Olga Tsoulaki, and Emma Kivuva. We acknowledge the NIHR Clinical Research Network (CRN) in recruiting the participants and the Musketeers Memorandum, as well as support from the NIHR UK Rare Genetic Disease Consortium. We thank Patrick J. Short for providing a curated set of de novo variants used in earlier iterations of these analyses. This research was made possible through access to the data and findings generated by the 100,000 Genomes Project. The 100,000 Genomes Project uses data provided by participants and collected by the National Health Service as part of their care and support. These analyses were performed using Python 3.7.6 and Pandas 1.2.1. The figures were generated in R 3.5.2 using RStudio 1.1.463. Funding Information: The 100,000 Genomes Project is managed by Genomics England Limited (a wholly owned company of the Department of Health and Social Care). The 100,000 Genomes Project is funded by the National Institute for Health Research and NHS England. The Wellcome Trust, Cancer Research UK, and the Medical Research Council have also funded the research infrastructure. Funding Information: The Baralle Lab is supported by the NIHR Research Professorship awarded to D.B. (RP-2016–07-011). Functional work was additionally supported by a Wessex Medical Research Innovation Grant awarded to J.L. NW is currently supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 220134/Z/20/Z) and funding from the Rosetrees Trust. AB was supported by funding from Health Education England. Funding Information: We acknowledge the NIHR Clinical Research Network (CRN) in recruiting the participants and the Musketeers Memorandum, as well as support from the NIHR UK Rare Genetic Disease Consortium. We thank Patrick J. Short for providing a curated set of de novo variants used in earlier iterations of these analyses. Publisher Copyright: © 2022, The Author(s).

PY - 2022/7/26

Y1 - 2022/7/26

N2 - 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.

AB - 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.

KW - Exons

KW - Humans

KW - Introns

KW - RNA

KW - RNA Splicing

KW - Rare Diseases/genetics

U2 - 10.1186/s13073-022-01087-x

DO - 10.1186/s13073-022-01087-x

M3 - Article (Academic Journal)

C2 - 35883178

SN - 1756-994X

VL - 14

SP - 79

JO - Genome Medicine

JF - Genome Medicine

IS - 1

M1 - 79

ER -

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

Abstract

Bibliographical note

Keywords

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Additional file 3 of A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project

Additional file 2 of A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project

Additional file 4 of A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project

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