The University of Southampton
University of Southampton Institutional Repository

Evaluating the performance of a clinical genome sequencing program for diagnosis of rare genetic disease, seen through the lens of craniosynostosis

Evaluating the performance of a clinical genome sequencing program for diagnosis of rare genetic disease, seen through the lens of craniosynostosis
Evaluating the performance of a clinical genome sequencing program for diagnosis of rare genetic disease, seen through the lens of craniosynostosis
Purpose

Genome sequencing (GS) for diagnosis of rare genetic disease is being introduced into the clinic, but the complexity of the data poses challenges for developing pipelines with high diagnostic sensitivity. We evaluated the performance of the Genomics England 100,000 Genomes Project (100kGP) panel-based pipelines, using craniosynostosis as a test disease.

Methods

GS data from 114 probands with craniosynostosis and their relatives (314 samples), negative on routine genetic testing, were scrutinized by a specialized research team, and diagnoses compared with those made by 100kGP.

Results

Sixteen likely pathogenic/pathogenic variants were identified by 100kGP. Eighteen additional likely pathogenic/pathogenic variants were identified by the research team, indicating that for craniosynostosis, 100kGP panels had a diagnostic sensitivity of only 47%. Measures that could have augmented diagnoses were improved calling of existing panel genes (+18% sensitivity), review of updated panels (+12%), comprehensive analysis of de novo small variants (+29%), and copy-number/structural variants (+9%). Recent NHS England recommendations that partially incorporate these measures should achieve 85% overall sensitivity (+38%).

Conclusion

GS identified likely pathogenic/pathogenic variants in 29.8% of previously undiagnosed patients with craniosynostosis. This demonstrates the value of research analysis and the importance of continually improving algorithms to maximize the potential of clinical GS.

1098-3600
2360-2368
Hyder, Zerin
b9e87dfa-5829-4d65-aa75-306e8fe731ea
Calpena, Eduardo
bad80457-ecc0-481a-8f9b-3cebc677d722
Pei, Yang
933fc229-1c3f-4225-8646-d47ce0c684f3
Tooze, Rebecca S
17084b0c-11e9-43ae-822b-c263e9348da4
Brittain, Helen
19411fbb-588d-49c3-93e8-f2926c8541e3
Twigg, Stephen R F
894d2681-a4c2-4571-825b-72577969617b
Cilliers, Deirdre
58d7fbc7-9f32-4ac5-bd04-d56b36e6afcd
Morton, Jenny E V
4404222b-195a-4d27-baec-3248e153e054
McCann, Emma
e86886b8-4f81-453f-a381-41003d14d699
Weber, Astrid
68f254cc-0a8b-483a-83ce-0f7cbd4a85d5
Wilson, Louise C
9ddd87d2-0e18-4ca8-b8a9-28ae1dfcc0ab
Douglas, Andrew G L
2c789ec4-a222-43bc-a040-522ca64fea42
McGowan, Ruth
a7f8dc75-9777-4366-b9c8-823e362170be
Need, Anna
149e1309-2447-45f9-b5a3-177584009b08
Bond, Andrew
94b1c5ea-ac6f-4062-9673-13064c82aeee
Tavares, Ana Lisa Taylor
730fb640-a84a-451f-815e-a3d7155e4938
Thomas, Ellen R A
659e19e1-db65-4097-ad3f-518138316823
Hill, Susan L
205c58b1-452d-466f-8f7f-c0459490a0ea
Deans, Zandra C
c0246abe-844c-4a2f-b668-2f31f73c2c52
Boardman-Pretty, Freya
b928cc82-f967-4ba2-a6f3-105090e3099c
Caulfield, Mark
2267d97b-b1ca-4902-ab87-d989374eb7f5
Scott, Richard H
19488755-f941-45d3-985b-dc0ab1e647d2
Wilkie, Andrew O M
7064a09e-66d9-4acf-92eb-cffad1ce3762
Genomics England Research Consortium
Hyder, Zerin
b9e87dfa-5829-4d65-aa75-306e8fe731ea
Calpena, Eduardo
bad80457-ecc0-481a-8f9b-3cebc677d722
Pei, Yang
933fc229-1c3f-4225-8646-d47ce0c684f3
Tooze, Rebecca S
17084b0c-11e9-43ae-822b-c263e9348da4
Brittain, Helen
19411fbb-588d-49c3-93e8-f2926c8541e3
Twigg, Stephen R F
894d2681-a4c2-4571-825b-72577969617b
Cilliers, Deirdre
58d7fbc7-9f32-4ac5-bd04-d56b36e6afcd
Morton, Jenny E V
4404222b-195a-4d27-baec-3248e153e054
McCann, Emma
e86886b8-4f81-453f-a381-41003d14d699
Weber, Astrid
68f254cc-0a8b-483a-83ce-0f7cbd4a85d5
Wilson, Louise C
9ddd87d2-0e18-4ca8-b8a9-28ae1dfcc0ab
Douglas, Andrew G L
2c789ec4-a222-43bc-a040-522ca64fea42
McGowan, Ruth
a7f8dc75-9777-4366-b9c8-823e362170be
Need, Anna
149e1309-2447-45f9-b5a3-177584009b08
Bond, Andrew
94b1c5ea-ac6f-4062-9673-13064c82aeee
Tavares, Ana Lisa Taylor
730fb640-a84a-451f-815e-a3d7155e4938
Thomas, Ellen R A
659e19e1-db65-4097-ad3f-518138316823
Hill, Susan L
205c58b1-452d-466f-8f7f-c0459490a0ea
Deans, Zandra C
c0246abe-844c-4a2f-b668-2f31f73c2c52
Boardman-Pretty, Freya
b928cc82-f967-4ba2-a6f3-105090e3099c
Caulfield, Mark
2267d97b-b1ca-4902-ab87-d989374eb7f5
Scott, Richard H
19488755-f941-45d3-985b-dc0ab1e647d2
Wilkie, Andrew O M
7064a09e-66d9-4acf-92eb-cffad1ce3762

Genomics England Research Consortium (2021) Evaluating the performance of a clinical genome sequencing program for diagnosis of rare genetic disease, seen through the lens of craniosynostosis. Genetics in Medicine, 23 (12), 2360-2368. (doi:10.1038/s41436-021-01297-5).

Record type: Article

Abstract

Purpose

Genome sequencing (GS) for diagnosis of rare genetic disease is being introduced into the clinic, but the complexity of the data poses challenges for developing pipelines with high diagnostic sensitivity. We evaluated the performance of the Genomics England 100,000 Genomes Project (100kGP) panel-based pipelines, using craniosynostosis as a test disease.

Methods

GS data from 114 probands with craniosynostosis and their relatives (314 samples), negative on routine genetic testing, were scrutinized by a specialized research team, and diagnoses compared with those made by 100kGP.

Results

Sixteen likely pathogenic/pathogenic variants were identified by 100kGP. Eighteen additional likely pathogenic/pathogenic variants were identified by the research team, indicating that for craniosynostosis, 100kGP panels had a diagnostic sensitivity of only 47%. Measures that could have augmented diagnoses were improved calling of existing panel genes (+18% sensitivity), review of updated panels (+12%), comprehensive analysis of de novo small variants (+29%), and copy-number/structural variants (+9%). Recent NHS England recommendations that partially incorporate these measures should achieve 85% overall sensitivity (+38%).

Conclusion

GS identified likely pathogenic/pathogenic variants in 29.8% of previously undiagnosed patients with craniosynostosis. This demonstrates the value of research analysis and the importance of continually improving algorithms to maximize the potential of clinical GS.

Archive
FW__GIM_Decision_GIM-D-21-00383 - Accepted Manuscript
Download (2MB)

More information

Accepted/In Press date: 22 July 2021
e-pub ahead of print date: 25 August 2021
Published date: 1 December 2021
Additional Information: Funding Information: We thank all the family members for their participation; Kate Chandler, Jill Clayton-Smith, John Dean, Verity Hartill, Diana Johnson, Gabriela Jones, Usha Kini, Melissa Lees, Martin McKibbin, Gillian Rea, Ruth Richardson, and Brian Wilson for patient recruitment and liaison; and Giada Melistaccio for help with bioinformatics analysis. This work was supported by the NIHR Oxford Biomedical Research Centre Program (A.O.M.W.), the MRC through a Project Grant MR/T031670/1 (A.O.M.W.), a Doctoral Training Program studentship (R.S.T) and the WIMM Strategic Alliance (G0902418 and MC UU 12025), the VTCT Foundation (S.R.F.T., A.O.M.W.) and a Wellcome Investigator Award 102731 (A.O.M.W.). We acknowledge support from the NIHR UK Rare Genetic Disease Research Consortium. This research was made possible through access to the data and findings generated by the 100,000 Genomes Project. 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 NIHR and National Health Service (NHS) England. Wellcome, Cancer Research UK and the MRC have also funded research infrastructure. The Scottish Genomes Partnership is funded by the Chief Scientist Office of the Scottish Government Health Directorates [SGP/1] and The MRC Whole Genome Sequencing for Health and Wealth Initiative (MC/PC/15080). The 100,000 Genomes Project uses data provided by patients and collected by the NHS as part of their care and support. The views expressed in this publication are those of the authors and not necessarily those of Wellcome, NIHR, NIDCR, or the Department of Health and Social Care. Publisher Copyright: © 2021, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

Identifiers

Local EPrints ID: 453631
URI: http://eprints.soton.ac.uk/id/eprint/453631
ISSN: 1098-3600
PURE UUID: 875cd019-6146-483c-9350-decbf0f63ec5
ORCID for Andrew G L Douglas: ORCID iD orcid.org/0000-0001-5154-6714

Catalogue record

Date deposited: 20 Jan 2022 17:40
Last modified: 17 Mar 2024 03:21

Export record

Altmetrics

Contributors

Author: Zerin Hyder
Author: Eduardo Calpena
Author: Yang Pei
Author: Rebecca S Tooze
Author: Helen Brittain
Author: Stephen R F Twigg
Author: Deirdre Cilliers
Author: Jenny E V Morton
Author: Emma McCann
Author: Astrid Weber
Author: Louise C Wilson
Author: Ruth McGowan
Author: Anna Need
Author: Andrew Bond
Author: Ana Lisa Taylor Tavares
Author: Ellen R A Thomas
Author: Susan L Hill
Author: Zandra C Deans
Author: Freya Boardman-Pretty
Author: Mark Caulfield
Author: Richard H Scott
Author: Andrew O M Wilkie
Corporate Author: Genomics England Research Consortium

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.

×