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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 of variants of uncertain significance (VUSs) identified through next-generation sequencing. Many such variants may disrupt normal RNA splicing. We examined effects on splicing of a large cohort of clinically identified variants and compared performance of bioinformatic splicing prediction tools commonly used in diagnostic laboratories.

Methods: 257 variants (coding and non-coding) were referred for analysis across three laboratories. Blood RNA samples underwent targeted RT-PCR analysis with Sanger sequencing of PCR products and agarose gel electrophoresis. 17 samples also underwent transcriptome-wide RNA sequencing with targeted splicing analysis based on Sashimi plot visualisation. Bioinformatic splicing predictions were obtained using Alamut, HSF 3.1 and SpliceAI software.

Results: 85 variants (33%) were associated with abnormal splicing. The most frequent abnormality was upstream exon skipping (39/85 variants), which was most often associated with splice donor region variants. SpliceAI had greatest accuracy in predicting splicing abnormalities (0.91) and outperformed other tools in sensitivity and specificity.

Conclusion: splicing analysis of blood RNA identifies diagnostically important splicing abnormalities and clarifies functional effects of a significant proportion of VUSs. Bioinformatic predictions are improving but still make significant errors. RNA analysis should therefore be routinely considered in genetic disease diagnostics.
RNA splicing, RNA-seq, genetic diagnosis, genomic medicine, variant interpretation
1098-3600
1-10
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, Grace
f9011f96-bbc5-4364-970a-0f510489c539
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
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, Grace
f9011f96-bbc5-4364-970a-0f510489c539
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, Grace, 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), 1-10. (doi:10.1038/s41436-020-0766-9).

Record type: Article

Abstract

Purpose: diagnosis of genetic disorders is hampered by large numbers of variants of uncertain significance (VUSs) identified through next-generation sequencing. Many such variants may disrupt normal RNA splicing. We examined effects on splicing of a large cohort of clinically identified variants and compared performance of bioinformatic splicing prediction tools commonly used in diagnostic laboratories.

Methods: 257 variants (coding and non-coding) were referred for analysis across three laboratories. Blood RNA samples underwent targeted RT-PCR analysis with Sanger sequencing of PCR products and agarose gel electrophoresis. 17 samples also underwent transcriptome-wide RNA sequencing with targeted splicing analysis based on Sashimi plot visualisation. Bioinformatic splicing predictions were obtained using Alamut, HSF 3.1 and SpliceAI software.

Results: 85 variants (33%) were associated with abnormal splicing. The most frequent abnormality was upstream exon skipping (39/85 variants), which was most often associated with splice donor region variants. SpliceAI had greatest accuracy in predicting splicing abnormalities (0.91) and outperformed other tools in sensitivity and specificity.

Conclusion: splicing analysis of blood RNA identifies diagnostically important splicing abnormalities and clarifies functional effects of a significant proportion of VUSs. Bioinformatic predictions are improving but still make significant errors. RNA analysis should therefore be routinely considered in genetic disease diagnostics.

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GIM-D-19-01085_R2 - Accepted Manuscript
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s41436-020-0766-9 - Version of Record
Available under License Creative Commons Attribution.
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More information

Accepted/In Press date: 30 January 2020
e-pub ahead of print date: 3 March 2020
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 Jenny Lord: ORCID iD orcid.org/0000-0002-0539-9343
ORCID for Andrew Douglas: ORCID iD orcid.org/0000-0001-5154-6714
ORCID for Diana Baralle: ORCID iD orcid.org/0000-0003-3217-4833

Catalogue record

Date deposited: 17 Feb 2020 17:32
Last modified: 22 Nov 2021 08:01

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Contributors

Author: Htoo Wai
Author: Jenny Lord ORCID iD
Author: Matthew S Lyon
Author: Adam Gunning
Author: Hugh Kelly
Author: Penelope Cibin
Author: Eleanor, Grace Seaby
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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