loading page

Comparison of in silico strategies to prioritize rare genomic variants impacting RNA splicing for the diagnosis of genomic disorders
  • +21
  • 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
Author Profile
Huw Thomas
The University of Manchester
Author Profile
Jenny Lord
University of Southampton Faculty of Medicine
Author Profile
Htoo Wai
University of Southampton Faculty of Medicine
Author Profile
Gavin Arno
University College London
Author Profile
Glenda Beaman
University of Manchester
Author Profile
Panagiotis Sergouniotis
Central Manchester University Hospitals NHS Foundation Trust
Author Profile
Beatriz Gomes-Silva
University of Manchester
Author Profile
Christopher Campbell
Central Manchester University Hospitals NHS Foundation Trust
Author Profile
Nicole Gossan
Central Manchester University Hospitals NHS Foundation Trust
Author Profile
Claire Hardcastle
Central Manchester University Hospitals NHS Foundation Trust
Author Profile
Kevin Webb
Central Manchester University Hospitals NHS Foundation Trust
Author Profile
Christopher O'Callaghan
University College London
Author Profile
Robert Hirst
University of Leicester
Author Profile
Simon Ramsden
Manchester University NHS Foundation Trust
Author Profile
Elizabeth Jones
Central Manchester University Hospitals NHS Foundation Trust
Author Profile
Jill Clayton-Smith
Manchester University NHS Foundation Trust
Author Profile
Andrew Webster
University College London
Author Profile
Andrew Douglas
University Hospital Southampton NHS Foundation Trust
Author Profile
Raymond T O'Keefe
The University of Manchester
Author Profile
William Newman
University of Manchester
Author Profile
Diana Baralle
University of Southampton Faculty of Medicine
Author Profile
Graeme Black
The University of Manchester
Author Profile
Jamie Ellingford
University of Manchester
Author Profile

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.