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Comparison of in silico strategies to prioritize rare genomic variants impacting RNA splicing for the diagnosis of genomic disorders
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  • Charlie Rowlands,
  • Huw Thomas,
  • Jenny Lord,
  • Htoo Wai,
  • Gavin Arno,
  • Glenda Beaman,
  • Panagiotis Sergouniotis,
  • Beatriz Gomes-Silva,
  • Christopher Campbell,
  • Nicole Gossan,
  • Claire Hardcastle,
  • Kevin Webb,
  • Christopher O'Callaghan,
  • Robert Hirst,
  • Simon Ramsden,
  • Elizabeth Jones,
  • Jill Clayton-Smith,
  • Andrew Webster,
  • Andrew Douglas,
  • Raymond T O'Keefe,
  • William Newman,
  • Diana Baralle,
  • Graeme Black,
  • Jamie Ellingford
Charlie Rowlands
University of Manchester

Corresponding Author:[email protected]

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Huw Thomas
The University of Manchester
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Jenny Lord
University of Southampton Faculty of Medicine
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Htoo Wai
University of Southampton Faculty of Medicine
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Gavin Arno
University College London
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Glenda Beaman
University of Manchester
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Panagiotis Sergouniotis
Central Manchester University Hospitals NHS Foundation Trust
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Beatriz Gomes-Silva
University of Manchester
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Christopher Campbell
Central Manchester University Hospitals NHS Foundation Trust
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Nicole Gossan
Central Manchester University Hospitals NHS Foundation Trust
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Claire Hardcastle
Central Manchester University Hospitals NHS Foundation Trust
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Kevin Webb
Central Manchester University Hospitals NHS Foundation Trust
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Christopher O'Callaghan
University College London
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Robert Hirst
University of Leicester
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Simon Ramsden
Manchester University NHS Foundation Trust
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Elizabeth Jones
Central Manchester University Hospitals NHS Foundation Trust
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Jill Clayton-Smith
Manchester University NHS Foundation Trust
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Andrew Webster
University College London
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Andrew Douglas
University Hospital Southampton NHS Foundation Trust
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Raymond T O'Keefe
The University of Manchester
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William Newman
University of Manchester
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Diana Baralle
University of Southampton Faculty of Medicine
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Graeme Black
The University of Manchester
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Jamie Ellingford
University of Manchester
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Abstract

The development of computational methods to assess pathogenicity of pre-messenger RNA splicing variants is critical for diagnosis of human disease. We assessed the capability of eight algorithms, and a consensus approach, to prioritize 250 variants of uncertain significance (VUS) that underwent splicing functional analyses. It is the capability of algorithms to differentiate VUSs away from the immediate splice site as ‘pathogenic’ or ‘benign’ that is likely to have the most substantial impact on diagnostic testing. We show that SpliceAI is the best single strategy in this regard, but that combined usage of tools using a weighted approach can increase accuracy further. We incorporated prioritization strategies alongside diagnostic testing for rare disorders. We show that 15% of 2783 referred individuals carry rare variants expected to impact splicing that were not initially identified as ‘pathogenic’ or ‘likely pathogenic’; 1 in 5 of these cases could lead to new or refined diagnoses.