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Blood RNA analysis can increase clinical diagnostic rate and resolve variants of uncertain significance

Blood RNA analysis can increase clinical diagnostic rate and resolve variants of uncertain significance
Blood RNA analysis can increase clinical diagnostic rate and resolve variants of uncertain significance

Purpose: Diagnosis of genetic disorders is hampered by large numbers ofvariants of uncertain significance (VUSs) identified through next-generationsequencing. Many such variants may disrupt normal RNA splicing. We examinedeffects on splicing of a large cohort of clinically identified variants andcompared performance of bioinformatic splicing prediction tools commonly used indiagnostic laboratories. Methods: Two hundred fifty-seven variants (coding and noncoding) werereferred for analysis across three laboratories. Blood RNA samples underwenttargeted reverse transcription polymerase chain reaction (RT-PCR) analysis withSanger sequencing of PCR products and agarose gel electrophoresis. Seventeensamples also underwent transcriptome-wide RNA sequencing with targeted splicinganalysis based on Sashimi plot visualization. Bioinformatic splicing predictionswere obtained using Alamut, HSF 3.1, and SpliceAI software. Results: Eighty-five variants (33%) were associated with abnormal splicing.The most frequent abnormality was upstream exon skipping (39/85 variants), whichwas most often associated with splice donor region variants. SpliceAI hadgreatest accuracy in predicting splicing abnormalities (0.91) and outperformedother tools in sensitivity and specificity. Conclusion: Splicing analysis of blood RNA identifies diagnostically importantsplicing abnormalities and clarifies functional effects of a significantproportion of VUSs. Bioinformatic predictions are improving but still makesignificant errors. RNA analysis should therefore be routinely considered ingenetic disease diagnostics.

RNA splicing, RNA-seq, genetic diagnosis, genomic medicine, variant interpretation
1098-3600
1005-1014
Wai, Htoo
4428517b-33b3-42cb-9818-ca64763ab7bc
Lord, Jenny
e1909780-36cd-4705-b21e-4580038d4ec6
Lyon, Matthew S
a12193fd-b7bd-405d-9d14-cec2d0f9031d
Gunning, Adam
74b9a711-4be5-4556-8dbb-57d601a578a7
Kelly, Hugh
1490638e-6fde-4c65-a480-f72034a570c5
Cibin, Penelope
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Seaby, Eleanor
ec948f42-007c-4bd8-9dff-bb86278bf03f
Spiers-Fitzgerald, Kerry
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Lye, Jed
95f6689e-ec36-4c93-a63e-78ac67584ed2
Ellard, Sian
6c9b0ede-8980-4602-b063-444b165baa09
Simon Thomas, N.
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Bunyan, David
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Douglas, Andrew
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Baralle, Diana
faac16e5-7928-4801-9811-8b3a9ea4bb91
Wai, Htoo
4428517b-33b3-42cb-9818-ca64763ab7bc
Lord, Jenny
e1909780-36cd-4705-b21e-4580038d4ec6
Lyon, Matthew S
a12193fd-b7bd-405d-9d14-cec2d0f9031d
Gunning, Adam
74b9a711-4be5-4556-8dbb-57d601a578a7
Kelly, Hugh
1490638e-6fde-4c65-a480-f72034a570c5
Cibin, Penelope
a33fd551-c911-4751-afbb-9a3e441bf866
Seaby, Eleanor
ec948f42-007c-4bd8-9dff-bb86278bf03f
Spiers-Fitzgerald, Kerry
80c71c1c-d336-43f9-a7d5-870cc8a09845
Lye, Jed
95f6689e-ec36-4c93-a63e-78ac67584ed2
Ellard, Sian
6c9b0ede-8980-4602-b063-444b165baa09
Simon Thomas, N.
2736b8b1-d10e-484a-bda8-8b761344a93e
Bunyan, David
d57bd2a7-d531-4892-bcce-e096dc95eee7
Douglas, Andrew
2c789ec4-a222-43bc-a040-522ca64fea42
Baralle, Diana
faac16e5-7928-4801-9811-8b3a9ea4bb91

Wai, Htoo, Lord, Jenny, Lyon, Matthew S, Gunning, Adam, Kelly, Hugh, Cibin, Penelope, Seaby, Eleanor, Spiers-Fitzgerald, Kerry, Lye, Jed, Ellard, Sian, Simon Thomas, N., Bunyan, David, Douglas, Andrew and Baralle, Diana (2020) Blood RNA analysis can increase clinical diagnostic rate and resolve variants of uncertain significance. Genetics in Medicine, 22 (6), 1005-1014. (doi:10.1038/s41436-020-0766-9).

Record type: Article

Abstract

Purpose: Diagnosis of genetic disorders is hampered by large numbers ofvariants of uncertain significance (VUSs) identified through next-generationsequencing. Many such variants may disrupt normal RNA splicing. We examinedeffects on splicing of a large cohort of clinically identified variants andcompared performance of bioinformatic splicing prediction tools commonly used indiagnostic laboratories. Methods: Two hundred fifty-seven variants (coding and noncoding) werereferred for analysis across three laboratories. Blood RNA samples underwenttargeted reverse transcription polymerase chain reaction (RT-PCR) analysis withSanger sequencing of PCR products and agarose gel electrophoresis. Seventeensamples also underwent transcriptome-wide RNA sequencing with targeted splicinganalysis based on Sashimi plot visualization. Bioinformatic splicing predictionswere obtained using Alamut, HSF 3.1, and SpliceAI software. Results: Eighty-five variants (33%) were associated with abnormal splicing.The most frequent abnormality was upstream exon skipping (39/85 variants), whichwas most often associated with splice donor region variants. SpliceAI hadgreatest accuracy in predicting splicing abnormalities (0.91) and outperformedother tools in sensitivity and specificity. Conclusion: Splicing analysis of blood RNA identifies diagnostically importantsplicing abnormalities and clarifies functional effects of a significantproportion of VUSs. Bioinformatic predictions are improving but still makesignificant errors. RNA analysis should therefore be routinely considered ingenetic disease diagnostics.

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Accepted/In Press date: 30 January 2020
e-pub ahead of print date: 3 March 2020
Published date: June 2020
Additional Information: Funding Information: This research was funded by National Institute for Health Research (NIHR) and the NewLife Foundation. The Baralle lab is supported by NIHR Research Professorship to D.B. (RP-2016-07-011). The authors thank all patients and families taking part in this research, and acknowledge the NIHR Clinical Research Network (CRN) in recruiting patients and the Musketeers Memorandum, as well as support from the NIHR UK Rare Genetic Disease Consortium. We thank staff from regional genetics services who recruited patients: Birmingham Women’s and Children’s NHS Foundation Trust (Swati Naik, Nicola Ragge, Helen Cox, Jenny Morton, Mary O’Driscoll, Derek Lim, Deborah Osio, Camilla Huber, Julie Hewitt); St George’s University Hospitals NHS Foundation Trust (Heidy Brandon, Meriel McEnta-gart, Sahar Mansour, Nayana Lahiri, Esther Dempsey, Merrie Manalo, Tessa Homfray, Anand Saggar); University Hospitals of Leicester NHS Trust (Jin Li, Julian Barwell); Manchester University NHS Foundation Trust (Kate Chandler, Tracy Briggs, Sofia Douzgou), Leeds Teaching Hospital NHS Trust (Julian Adlard, Alison Kraus); Cambridge University Hospitals NHS Foundation Trust (Sarju Mehta); University Hospitals Bristol NHS Foundation Trust (Amy Watford, Alan Donaldson, Karen Low); Nottingham University Hospitals NHS Trust (Gabriela Jones, Abhijit Dixit, Elizabeth King, Nora Shannon); Great Ormond Street Hospital for Children NHS Foundation Trust (Marios Kaliakatsos); Guys and St Thomas’ NHS Foundation Trust (Merrie Manalo); NHS Greater Glasgow and Clyde (Shelagh Joss); Sheffield Children’s NHS Foundation Trust (Meena Balasubramanian, Diana Johnson); Royal Devon and Exeter NHS Foundation Trust (Sarah Everest); University Hospital Southampton NHS Foundation Trust (Claire Salter, Victoria Harrison, Gillian Wise, Audrey Torokwa, Victoria Sands, Esther Pyle, Tessy Thomas, Katherine Lachlan, Nicola Foulds, Diana Baralle, Andrew Lotery, Andrew Douglas, Simon Hammans, Emily Pond, Rachel Horton, Mira Kharbanda, David Hunt, Charlene Thomas, Lucy Side, Catherine Willis, Stephanie Greville-Heygate, Rebecca Mawby, Catherine Mercer, Karen Temple, Esther Kinning); University of Bergen, Norway (Ognjen Bojovic); L. Archer. The authors acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work. Publisher Copyright: © 2020, The Author(s).
Keywords: RNA splicing, RNA-seq, genetic diagnosis, genomic medicine, variant interpretation

Identifiers

Local EPrints ID: 437797
URI: http://eprints.soton.ac.uk/id/eprint/437797
ISSN: 1098-3600
PURE UUID: 3f7c9e31-ba38-4349-8dbf-3909536e2c73
ORCID for Htoo Wai: ORCID iD orcid.org/0000-0002-3560-6980
ORCID for Jenny Lord: ORCID iD orcid.org/0000-0002-0539-9343
ORCID for Eleanor Seaby: ORCID iD orcid.org/0000-0002-6814-8648
ORCID for Andrew Douglas: ORCID iD orcid.org/0000-0001-5154-6714
ORCID for Diana Baralle: ORCID iD orcid.org/0000-0003-3217-4833

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Date deposited: 17 Feb 2020 17:32
Last modified: 08 Jul 2022 04:04

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Contributors

Author: Htoo Wai ORCID iD
Author: Jenny Lord ORCID iD
Author: Matthew S Lyon
Author: Adam Gunning
Author: Hugh Kelly
Author: Penelope Cibin
Author: Eleanor Seaby ORCID iD
Author: Kerry Spiers-Fitzgerald
Author: Jed Lye
Author: Sian Ellard
Author: N. Simon Thomas
Author: David Bunyan
Author: Andrew Douglas ORCID iD
Author: Diana Baralle ORCID iD

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