The University of Southampton
University of Southampton Institutional Repository

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

A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project
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 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.
1756-994X
79
Blakes, Alexander J.M.
dfefed45-fab2-4980-8667-7e46ce0a14f5
Wai, Htoo
4428517b-33b3-42cb-9818-ca64763ab7bc
Davies, Ian
c9c5c54a-1dd8-4eda-a080-f0574f79740e
Moledina, Hassan E
6fa7b033-ba2b-4a56-9966-8d337b583556
Ruiz, April
12dc1e1a-5947-45d5-a286-4a6de264e6fb
Thomas, Tessy
92c593e1-2130-4f8b-81dc-4abdee8798b6
Bunyan, David
d57bd2a7-d531-4892-bcce-e096dc95eee7
Thomas, N Simon
2736b8b1-d10e-484a-bda8-8b761344a93e
Burren, Christine P
b8d1c5b4-3375-4d64-8df2-25be58b8efd2
Greenhalgh, Lynn
c763cce9-4b01-453d-8975-2a7390a8beaa
Lees, Melissa
f6e4350a-f8f5-4162-81c5-510c1edc925a
Pichini, Amanda
24a95cef-e9c1-4e0a-85b0-c1cf9d98be2d
Smithson, Sarah F.
92ad1e42-fbce-46c0-b85f-afdc74fd4feb
Tavares, Ana Lisa Taylor
730fb640-a84a-451f-815e-a3d7155e4938
O'Donovan, Peter
4f4d957c-5482-424b-a785-d28d7cf28c20
Douglas, Andrew
2c789ec4-a222-43bc-a040-522ca64fea42
Whiffin, Nicola
a12b021c-3330-4a73-9dc2-dea3d79ba9a8
Baralle, Diana
faac16e5-7928-4801-9811-8b3a9ea4bb91
Lord, Jenny
e1909780-36cd-4705-b21e-4580038d4ec6
Genomics England Research Consortium, Splicing and Disease Working Group
Blakes, Alexander J.M.
dfefed45-fab2-4980-8667-7e46ce0a14f5
Wai, Htoo
4428517b-33b3-42cb-9818-ca64763ab7bc
Davies, Ian
c9c5c54a-1dd8-4eda-a080-f0574f79740e
Moledina, Hassan E
6fa7b033-ba2b-4a56-9966-8d337b583556
Ruiz, April
12dc1e1a-5947-45d5-a286-4a6de264e6fb
Thomas, Tessy
92c593e1-2130-4f8b-81dc-4abdee8798b6
Bunyan, David
d57bd2a7-d531-4892-bcce-e096dc95eee7
Thomas, N Simon
2736b8b1-d10e-484a-bda8-8b761344a93e
Burren, Christine P
b8d1c5b4-3375-4d64-8df2-25be58b8efd2
Greenhalgh, Lynn
c763cce9-4b01-453d-8975-2a7390a8beaa
Lees, Melissa
f6e4350a-f8f5-4162-81c5-510c1edc925a
Pichini, Amanda
24a95cef-e9c1-4e0a-85b0-c1cf9d98be2d
Smithson, Sarah F.
92ad1e42-fbce-46c0-b85f-afdc74fd4feb
Tavares, Ana Lisa Taylor
730fb640-a84a-451f-815e-a3d7155e4938
O'Donovan, Peter
4f4d957c-5482-424b-a785-d28d7cf28c20
Douglas, Andrew
2c789ec4-a222-43bc-a040-522ca64fea42
Whiffin, Nicola
a12b021c-3330-4a73-9dc2-dea3d79ba9a8
Baralle, Diana
faac16e5-7928-4801-9811-8b3a9ea4bb91
Lord, Jenny
e1909780-36cd-4705-b21e-4580038d4ec6

Whiffin, Nicola, Baralle, Diana and Lord, Jenny , Genomics England Research Consortium, Splicing and Disease Working Group (2022) A systematic analysis of splicing variants identifies new diagnoses in the 100,000 Genomes Project. Genome Medicine, 14 (1), 79, [79]. (doi:10.1186/s13073-022-01087-x).

Record type: Article

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.

Text
Blakes_2022 - Accepted Manuscript
Available under License Creative Commons Attribution.
Download (1MB)
Text
s13073-022-01087-x - Version of Record
Available under License Creative Commons Attribution.
Download (1MB)

More information

Accepted/In Press date: 13 July 2022
Published date: 26 July 2022

Identifiers

Local EPrints ID: 468545
URI: http://eprints.soton.ac.uk/id/eprint/468545
ISSN: 1756-994X
PURE UUID: efacdf6f-45cf-404a-87e6-1498abe646b8
ORCID for Htoo Wai: ORCID iD orcid.org/0000-0002-3560-6980
ORCID for Andrew Douglas: ORCID iD orcid.org/0000-0001-5154-6714
ORCID for Diana Baralle: ORCID iD orcid.org/0000-0003-3217-4833
ORCID for Jenny Lord: ORCID iD orcid.org/0000-0002-0539-9343

Catalogue record

Date deposited: 17 Aug 2022 17:15
Last modified: 09 Nov 2022 02:58

Export record

Altmetrics

Contributors

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

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×