Comparison of in silico strategies to prioritize rare genomic variants impacting RNA splicing for the diagnosis of genomic disorders

Autor: Charlie Rowlands, Huw B. Thomas, Jenny Lord, Htoo A. Wai, Gavin Arno, Glenda Beaman, Panagiotis Sergouniotis, Beatriz Gomes-Silva, Christopher Campbell, Nicole Gossan, Claire Hardcastle, Kevin Webb, Christopher O’Callaghan, Robert A. Hirst, Simon Ramsden, Elizabeth Jones, Jill Clayton-Smith, Andrew R. Webster, Genomics England Research Consortium, Andrew G. L. Douglas, Raymond T. O’Keefe, William G. Newman, Diana Baralle, Graeme C. M. Black, Jamie M. Ellingford
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Scientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-021-99747-2
Popis: 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 249 variants of uncertain significance (VUSs) that underwent splicing functional analyses. The capability of algorithms to differentiate VUSs away from the immediate splice site as being ‘pathogenic’ or ‘benign’ is likely to have 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’; one in five of these cases could lead to new or refined diagnoses.
Databáze: Directory of Open Access Journals
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