1. Comparison of in silico strategies to prioritize rare genomic variants impacting RNA splicing for the diagnosis of genomic disorders
- Author
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Augusto Rendon, A. Kousathanas, S. E. A. Leigh, D. Kasperaviciute, R. Jackson, J. Pullinger, Richard H. Scott, T. Rahim, Graeme C.M. Black, C. A. Odhams, Simon C Ramsden, A. Siddiq, L. Lahnstein, S. R. Thompson, Huw B. Thomas, Jenny Lord, M. Bleda, M. J. Welland, L. Moutsianas, A. Giess, Jill Clayton-Smith, A. Stuckey, Panagiotis I. Sergouniotis, H. Brittain, K. Sawant, Arianna Tucci, A. Sosinsky, I. U. S. Leong, Nicole Gossan, William G. Newman, Christopher Campbell, Mark J. Caulfield, T. Rogers, Diana Baralle, Andrew G. L. Douglas, A. L. Taylor Tavares, N. Murugaesu, Gavin Arno, S. M. Wood, Louise J. Jones, Robert A. Hirst, Htoo A Wai, A. Hamblin, Glenda M. Beaman, P. O’Donovan, A. Sieghart, F. Maleady-Crowe, S. Henderson, M. Tanguy, Claire Hardcastle, C. R. Boustred, G. C. Chan, M. McEntagart, Beatriz Gomes-Silva, E. Williams, Andrew R. Webster, Ellen R A Thomas, T. Fowler, Christine Patch, Raymond T. O'Keefe, Elizabeth A. Jones, D. Perez-Gil, M. B. Pereira, R. Bevers, F. J. Lopez, Jamie M Ellingford, Kevin Webb, M. Kayikci, S. C. Smith, F. Boardman-Pretty, Charlie Rowlands, K. Witkowsa, P. Arumugam, A. C. Need, Tim Hubbard, J. C. Ambrose, M. Mueller, Christopher O'Callaghan, F. Minneci, and K. Savage
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Prioritization ,Genetic testing ,Science ,RNA Splicing ,In silico ,Computational biology ,Biology ,Article ,Diagnosis, Differential ,Human disease ,Databases, Genetic ,Diagnosis ,RNA Precursors ,Genetics ,Humans ,Disease ,Clinical genetics ,Medical diagnosis ,Uncertain significance ,Diagnostic Techniques and Procedures ,Multidisciplinary ,Disease genetics ,Computational Biology ,Genetic Variation ,Diagnostic test ,Exons ,Genomics ,Pathogenicity ,Mutation ,RNA splicing ,Medicine ,RNA Splice Sites ,Algorithms - 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 (VUSs) that underwent splicing functional analyses. It is the capability of algorithms to differentiate VUSs away from the immediate splice site as being ‘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.
- Published
- 2020
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