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: 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.
| Errataetall: | |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:22 |
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| Enthalten in: |
Genetics in medicine : official journal of the American College of Medical Genetics - 22(2020), 6 vom: 03. Juni, Seite 1005-1014 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wai, Htoo A [VerfasserIn] |
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| Links: |
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| Themen: |
63231-63-0 |
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| Anmerkungen: |
Date Completed 27.04.2021 Date Revised 10.02.2022 published: Print-Electronic ErratumIn: Genet Med. 2020 Apr 1;:. - PMID 32235935 Citation Status MEDLINE |
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| doi: |
10.1038/s41436-020-0766-9 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM307147940 |
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| 100 | 1 | |a Wai, Htoo A |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Blood RNA analysis can increase clinical diagnostic rate and resolve variants of uncertain significance |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
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| 500 | |a Date Completed 27.04.2021 | ||
| 500 | |a Date Revised 10.02.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a ErratumIn: Genet Med. 2020 Apr 1;:. - PMID 32235935 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a RNA splicing | |
| 650 | 4 | |a RNA-seq | |
| 650 | 4 | |a genetic diagnosis | |
| 650 | 4 | |a genomic medicine | |
| 650 | 4 | |a variant interpretation | |
| 650 | 7 | |a RNA |2 NLM | |
| 650 | 7 | |a 63231-63-0 |2 NLM | |
| 700 | 1 | |a Lord, Jenny |e verfasserin |4 aut | |
| 700 | 1 | |a Lyon, Matthew |e verfasserin |4 aut | |
| 700 | 1 | |a Gunning, Adam |e verfasserin |4 aut | |
| 700 | 1 | |a Kelly, Hugh |e verfasserin |4 aut | |
| 700 | 1 | |a Cibin, Penelope |e verfasserin |4 aut | |
| 700 | 1 | |a Seaby, Eleanor G |e verfasserin |4 aut | |
| 700 | 1 | |a Spiers-Fitzgerald, Kerry |e verfasserin |4 aut | |
| 700 | 1 | |a Lye, Jed |e verfasserin |4 aut | |
| 700 | 1 | |a Ellard, Sian |e verfasserin |4 aut | |
| 700 | 1 | |a Thomas, N Simon |e verfasserin |4 aut | |
| 700 | 1 | |a Bunyan, David J |e verfasserin |4 aut | |
| 700 | 1 | |a Douglas, Andrew G L |e verfasserin |4 aut | |
| 700 | 1 | |a Baralle, Diana |e verfasserin |4 aut | |
| 700 | 0 | |a Splicing and disease working group |e verfasserin |4 aut | |
| 700 | 1 | |a Naik, Swati |e investigator |4 oth | |
| 700 | 1 | |a Ragge, Nicola |e investigator |4 oth | |
| 700 | 1 | |a Cox, Helen |e investigator |4 oth | |
| 700 | 1 | |a Morton, Jenny |e investigator |4 oth | |
| 700 | 1 | |a O'Driscoll, Mary |e investigator |4 oth | |
| 700 | 1 | |a Lim, Derek |e investigator |4 oth | |
| 700 | 1 | |a Osio, Deborah |e investigator |4 oth | |
| 700 | 1 | |a Elmslie, Frances |e investigator |4 oth | |
| 700 | 1 | |a Huber, Camilla |e investigator |4 oth | |
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| 700 | 1 | |a McEntagart, Meriel |e investigator |4 oth | |
| 700 | 1 | |a Mansour, Sahar |e investigator |4 oth | |
| 700 | 1 | |a Lahiri, Nayana |e investigator |4 oth | |
| 700 | 1 | |a Dempsey, Esther |e investigator |4 oth | |
| 700 | 1 | |a Manalo, Merrie |e investigator |4 oth | |
| 700 | 1 | |a Homfray, Tessa |e investigator |4 oth | |
| 700 | 1 | |a Saggar, Anand |e investigator |4 oth | |
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| 700 | 1 | |a Barwell, Julian |e investigator |4 oth | |
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| 700 | 1 | |a Douzgou, Sofia |e investigator |4 oth | |
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