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

Htoo A Wai; Jenny Lord; Matthew Lyon; Adam Gunning; Hugh Kelly; Penelope Cibin; Eleanor G Seaby; Kerry Spiers-Fitzgerald; Jed Lye; Sian Ellard; N Simon Thomas; David J Bunyan; Andrew G L Douglas; Diana Baralle; Splicing and disease working
                    group; Swati Naik; Nicola Ragge; Helen Cox; Jenny E. Morton; Mary O'Driscoll; Derek Lim; Deborah Osio; Frances Elmslie; Camilla Huber; Julie Hewitt; Heidy Brandon; Meriel McEntagart; Sahar Mansour; Nayana Lahiri; Esther Dempsey; Merrie Manalo; Tessa Homfray; Anand Saggar; Jin Li; Julian Barwell; Kate E. Chandler; Tracy Briggs; Sofia Douzgou; Julian Adlard; Alison Kraus; Sarju Mehta; Amy Watford; Alan Donaldson; Karen Low; Gabriela Jones; Abhijit Dixit; Elizabeth King; Nora Shannon; Marios Kaliakatsos; Merrie  Manalo; Shelagh Joss; Meena Balasubramanian; Diana Johnson; Sarah Everest; Claire Salter; Victoria Harrison; Gillian Wise; Audrey Torokwa; Victoria Sands; Esther Pyle; Tessy Thomas; Katherine Lachlan; Nicola Foulds; Andrew Lotery; Andrew G L Douglas; Simon R. 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; Ognjen Bojovic; L. Archer

doi:10.1038/s41436-020-0766-9

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

Htoo A Wai, Jenny Lord, Matthew Lyon, Adam Gunning, Hugh Kelly, Penelope Cibin, Eleanor G Seaby, Kerry Spiers-Fitzgerald, Jed Lye, Sian Ellard, N Simon Thomas, David J Bunyan, Andrew G L Douglas, Diana Baralle, Splicing and disease working group, Swati Naik, Nicola Ragge, Helen Cox, Jenny E. Morton, Mary O'DriscollDerek Lim, Deborah Osio, Frances Elmslie, Camilla Huber, Julie Hewitt, Heidy Brandon, Meriel McEntagart, Sahar Mansour, Nayana Lahiri, Esther Dempsey, Merrie Manalo, Tessa Homfray, Anand Saggar, Jin Li, Julian Barwell, Kate E. Chandler, Tracy Briggs, Sofia Douzgou, Julian Adlard, Alison Kraus, Sarju Mehta, Amy Watford, Alan Donaldson, Karen Low, Gabriela Jones, Abhijit Dixit, Elizabeth King, Nora Shannon, Marios Kaliakatsos, Merrie Manalo, Shelagh Joss, Meena Balasubramanian, Diana Johnson, Sarah Everest, Claire Salter, Victoria Harrison, Gillian Wise, Audrey Torokwa, Victoria Sands, Esther Pyle, Tessy Thomas, Katherine Lachlan, Nicola Foulds, Andrew Lotery, Andrew G L Douglas, Simon R. 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, Ognjen Bojovic, L. Archer

Division of Evolution, Infection and Genomics

Research output: Contribution to journal › Article › peer-review

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: Two hundred fifty-seven variants (coding and noncoding) were referred for analysis across three laboratories. Blood RNA samples underwent targeted reverse transcription polymerase chain reaction (RT-PCR) analysis with Sanger sequencing of PCR products and agarose gel electrophoresis. Seventeen samples also underwent transcriptome-wide RNA sequencing with targeted splicing analysis based on Sashimi plot visualization. Bioinformatic splicing predictions were 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), 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.

Original language	English
Pages (from-to)	1005-1014
Number of pages	10
Journal	Genetics in medicine : official journal of the American College of Medical Genetics
Volume	22
Issue number	6
Early online date	3 Mar 2020
DOIs	https://doi.org/10.1038/s41436-020-0766-9
Publication status	Published - Jun 2020

Keywords

computational biology
exons
humans
mutation
RNA/genetics
RNA Splicing

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10.1038/s41436-020-0766-9

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Wai, H. A., Lord, J., Lyon, M., Gunning, A., Kelly, H., Cibin, P., Seaby, E. G., Spiers-Fitzgerald, K., Lye, J., Ellard, S., Thomas, N. S., Bunyan, D. J., Douglas, A. G. L., Baralle, D., Splicing and disease working group, Naik, S., Ragge, N., Cox, H., Morton, J. E., ... Archer, L. (2020). Blood RNA analysis can increase clinical diagnostic rate and resolve variants of uncertain significance. Genetics in medicine : official journal of the American College of Medical Genetics, 22(6), 1005-1014. https://doi.org/10.1038/s41436-020-0766-9

@article{331a3686c2174963acb8752dbdc365c2,

title = "Blood RNA analysis can increase clinical diagnostic rate and resolve variants of uncertain significance",

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: Two hundred fifty-seven variants (coding and noncoding) were referred for analysis across three laboratories. Blood RNA samples underwent targeted reverse transcription polymerase chain reaction (RT-PCR) analysis with Sanger sequencing of PCR products and agarose gel electrophoresis. Seventeen samples also underwent transcriptome-wide RNA sequencing with targeted splicing analysis based on Sashimi plot visualization. Bioinformatic splicing predictions were 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), 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.",

keywords = "computational biology, exons, humans, mutation, RNA/genetics, RNA Splicing",

author = "Wai, {Htoo A} and Jenny Lord and Matthew Lyon and Adam Gunning and Hugh Kelly and Penelope Cibin and Seaby, {Eleanor G} and Kerry Spiers-Fitzgerald and Jed Lye and Sian Ellard and Thomas, {N Simon} and Bunyan, {David J} and Douglas, {Andrew G L} and Diana Baralle and {Splicing and disease working group} and Swati Naik and Nicola Ragge and Helen Cox and Morton, {Jenny E.} and Mary O'Driscoll and Derek Lim and Deborah Osio and Frances Elmslie and Camilla Huber and Julie Hewitt and Heidy Brandon and Meriel McEntagart and Sahar Mansour and Nayana Lahiri and Esther Dempsey and Merrie Manalo and Tessa Homfray and Anand Saggar and Jin Li and Julian Barwell and Chandler, {Kate E.} and Tracy Briggs and Sofia Douzgou and Julian Adlard and Alison Kraus and Sarju Mehta and Amy Watford and Alan Donaldson and Karen Low and Gabriela Jones and Abhijit Dixit and Elizabeth King and Nora Shannon and Marios Kaliakatsos and Merrie Manalo and Shelagh Joss and Meena Balasubramanian and Diana Johnson and Sarah Everest and Claire Salter and Victoria Harrison and Gillian Wise and Audrey Torokwa and Victoria Sands and Esther Pyle and Tessy Thomas and Katherine Lachlan and Nicola Foulds and Andrew Lotery and Douglas, {Andrew G L} and Hammans, {Simon R.} and Emily Pond and Rachel Horton and Mira Kharbanda and David Hunt and Charlene Thomas and Lucy Side and Catherine Willis and Stephanie Greville-Heygate and Rebecca Mawby and Catherine Mercer and Karen Temple and Esther Kinning and Ognjen Bojovic and L. Archer",

year = "2020",

month = jun,

doi = "10.1038/s41436-020-0766-9",

language = "English",

volume = "22",

pages = "1005--1014",

journal = "Genetics in medicine : official journal of the American College of Medical Genetics",

issn = "1098-3600",

publisher = "Elsevier BV",

number = "6",

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Wai, HA, Lord, J, Lyon, M, Gunning, A, Kelly, H, Cibin, P, Seaby, EG, Spiers-Fitzgerald, K, Lye, J, Ellard, S, Thomas, NS, Bunyan, DJ, Douglas, AGL, Baralle, D, Splicing and disease working group, Naik, S, Ragge, N, Cox, H, Morton, JE, O'Driscoll, M, Lim, D, Osio, D, Elmslie, F, Huber, C, Hewitt, J, Brandon, H, McEntagart, M, Mansour, S, Lahiri, N, Dempsey, E, Manalo, M, Homfray, T, Saggar, A, Li, J, Barwell, J, Chandler, KE, Briggs, T, Douzgou, S, Adlard, J, Kraus, A, Mehta, S, Watford, A, Donaldson, A, Low, K, Jones, G, Dixit, A, King, E, Shannon, N, Kaliakatsos, M, Manalo, M, Joss, S, Balasubramanian, M, Johnson, D, Everest, S, Salter, C, Harrison, V, Wise, G, Torokwa, A, Sands, V, Pyle, E, Thomas, T, Lachlan, K, Foulds, N, Lotery, A, Douglas, AGL, Hammans, SR, Pond, E, Horton, R, Kharbanda, M, Hunt, D, Thomas, C, Side, L, Willis, C, Greville-Heygate, S, Mawby, R, Mercer, C, Temple, K, Kinning, E, Bojovic, O & Archer, L 2020, 'Blood RNA analysis can increase clinical diagnostic rate and resolve variants of uncertain significance', Genetics in medicine : official journal of the American College of Medical Genetics, vol. 22, no. 6, pp. 1005-1014. https://doi.org/10.1038/s41436-020-0766-9

TY - JOUR

T1 - Blood RNA analysis can increase clinical diagnostic rate and resolve variants of uncertain significance

AU - Wai, Htoo A

AU - Lord, Jenny

AU - Lyon, Matthew

AU - Gunning, Adam

AU - Kelly, Hugh

AU - Cibin, Penelope

AU - Seaby, Eleanor G

AU - Spiers-Fitzgerald, Kerry

AU - Lye, Jed

AU - Ellard, Sian

AU - Thomas, N Simon

AU - Bunyan, David J

AU - Douglas, Andrew G L

AU - Baralle, Diana

AU - Splicing and disease working group

AU - Naik, Swati

AU - Ragge, Nicola

AU - Cox, Helen

AU - Morton, Jenny E.

AU - O'Driscoll, Mary

AU - Lim, Derek

AU - Osio, Deborah

AU - Elmslie, Frances

AU - Huber, Camilla

AU - Hewitt, Julie

AU - Brandon, Heidy

AU - McEntagart, Meriel

AU - Mansour, Sahar

AU - Lahiri, Nayana

AU - Dempsey, Esther

AU - Manalo, Merrie

AU - Homfray, Tessa

AU - Saggar, Anand

AU - Li, Jin

AU - Barwell, Julian

AU - Chandler, Kate E.

AU - Briggs, Tracy

AU - Douzgou, Sofia

AU - Adlard, Julian

AU - Kraus, Alison

AU - Mehta, Sarju

AU - Watford, Amy

AU - Donaldson, Alan

AU - Low, Karen

AU - Jones, Gabriela

AU - Dixit, Abhijit

AU - King, Elizabeth

AU - Shannon, Nora

AU - Kaliakatsos, Marios

AU - Manalo, Merrie

AU - Joss, Shelagh

AU - Balasubramanian, Meena

AU - Johnson, Diana

AU - Everest, Sarah

AU - Salter, Claire

AU - Harrison, Victoria

AU - Wise, Gillian

AU - Torokwa, Audrey

AU - Sands, Victoria

AU - Pyle, Esther

AU - Thomas, Tessy

AU - Lachlan, Katherine

AU - Foulds, Nicola

AU - Lotery, Andrew

AU - Douglas, Andrew G L

AU - Hammans, Simon R.

AU - Pond, Emily

AU - Horton, Rachel

AU - Kharbanda, Mira

AU - Hunt, David

AU - Thomas, Charlene

AU - Side, Lucy

AU - Willis, Catherine

AU - Greville-Heygate, Stephanie

AU - Mawby, Rebecca

AU - Mercer, Catherine

AU - Temple, Karen

AU - Kinning, Esther

AU - Bojovic, Ognjen

AU - Archer, L.

PY - 2020/6

Y1 - 2020/6

N2 - 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: Two hundred fifty-seven variants (coding and noncoding) were referred for analysis across three laboratories. Blood RNA samples underwent targeted reverse transcription polymerase chain reaction (RT-PCR) analysis with Sanger sequencing of PCR products and agarose gel electrophoresis. Seventeen samples also underwent transcriptome-wide RNA sequencing with targeted splicing analysis based on Sashimi plot visualization. Bioinformatic splicing predictions were 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), 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.

AB - 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: Two hundred fifty-seven variants (coding and noncoding) were referred for analysis across three laboratories. Blood RNA samples underwent targeted reverse transcription polymerase chain reaction (RT-PCR) analysis with Sanger sequencing of PCR products and agarose gel electrophoresis. Seventeen samples also underwent transcriptome-wide RNA sequencing with targeted splicing analysis based on Sashimi plot visualization. Bioinformatic splicing predictions were 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), 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.

KW - computational biology

KW - exons

KW - humans

KW - mutation

KW - RNA/genetics

KW - RNA Splicing

U2 - 10.1038/s41436-020-0766-9

DO - 10.1038/s41436-020-0766-9

M3 - Article

C2 - 32123317

SN - 1098-3600

VL - 22

SP - 1005

EP - 1014

JO - Genetics in medicine : official journal of the American College of Medical Genetics

JF - Genetics in medicine : official journal of the American College of Medical Genetics

IS - 6

ER -

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

Abstract

Keywords

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